diff --git a/.gitignore b/.gitignore
index ebc68b4fe..103e6bfd8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,6 +42,10 @@ implementations/**/data/
 !implementations/energy_oil_forecasting/data/predictions/
 !implementations/energy_oil_forecasting/data/predictions/**
 
+# Manufacturing workbench reports are reproducible project artifacts.
+!implementations/manufacturing_stress_forecasting/reports/
+!implementations/manufacturing_stress_forecasting/reports/**
+
 # workspace-specific agents
 .github/agents/
 .github/prompts/
diff --git a/aieng-forecasting/aieng/forecasting/data/adapters/fred.py b/aieng-forecasting/aieng/forecasting/data/adapters/fred.py
index 3bb6a68c6..b857c0b45 100644
--- a/aieng-forecasting/aieng/forecasting/data/adapters/fred.py
+++ b/aieng-forecasting/aieng/forecasting/data/adapters/fred.py
@@ -86,7 +86,8 @@ def __init__(
         refresh: bool = False,
     ) -> None:
         self._series_id = series_id
-        self._api_key = api_key or os.environ.get("FRED_API_KEY")
+        raw_key = (api_key if api_key is not None else os.environ.get("FRED_API_KEY", "")).strip()
+        self._api_key = raw_key or None
         self._cache_dir = Path(cache_dir) if cache_dir is not None else None
         self._refresh = refresh
 
diff --git a/aieng-forecasting/aieng/forecasting/methods/README.md b/aieng-forecasting/aieng/forecasting/methods/README.md
index 2c851763e..da2441f1f 100644
--- a/aieng-forecasting/aieng/forecasting/methods/README.md
+++ b/aieng-forecasting/aieng/forecasting/methods/README.md
@@ -102,7 +102,7 @@ from aieng.forecasting.methods.agentic import (
 
 | Module | Class / Function | Description |
 |---|---|---|
-| `agentic/adk_runner.py` | `AdkTextRunner` | Async text-in / text-out wrapper around ADK `InMemoryRunner`. Manages ADK sessions (fresh-per-message or sticky) and optionally traces each turn to Langfuse via `propagate_attributes`. |
+| `agentic/adk_runner.py` | `AdkTextRunner` | Async text-in / text-out wrapper around ADK `InMemoryRunner`. Drains each event stream, joins non-thought text parts from the first usable final response, prefers structured JSON submitted through `set_model_response`, manages ADK sessions (fresh-per-message or sticky), and optionally traces each turn to Langfuse via `propagate_attributes`. |
 | `agentic/adk_runner.py` | `AdkTextRunnerConfig` | Pydantic configuration for `AdkTextRunner` (session mode, Langfuse fields). |
 | `agentic/agent_factory.py` | `build_adk_agent` | Generic ADK `LlmAgent` factory with optional code execution, context retrieval, skills, generation controls, and structured output schema. `search_web` runs a cutoff-aware googleSearch sub-call plus an independent leakage verifier on historical origins (skipped when `as_of` is today or later); both inner LLM calls emit nested Langfuse generations (`search_web.google_search`, `search_web.leakage_verifier`) under the agent trace. |
 | `agentic/agent_factory.py` | `AgentConfig` | Pydantic configuration for reusable ADK agents. `output_schema=None` supports interactive/free-form agents; a structured `AgentForecastOutput` schema supports Track 1 predictors. The `function_tools` field attaches conventional ADK tools (e.g. `ForecastTool`). Use-case-specific prompts and presets should live in `implementations/<use-case>/`. |
@@ -111,5 +111,5 @@ from aieng.forecasting.methods.agentic import (
 | `agentic/outputs.py` | `ContinuousAgentForecastOutput` | Canonical continuous forecasting output schema. Declares `modality = "continuous"`, requires one forecast per task horizon and the standard quantile grid, then converts to `ContinuousForecast` payloads. |
 | `agentic/outputs.py` | `DiscreteAgentForecastOutput` | Binary event output schema (`modality = "discrete"`): one probability plus `reasoning` / `key_signals` metadata, converted to a `BinaryForecast` payload. |
 | `agentic/outputs.py` | `CategoricalAgentForecastOutput` | Ordered-categorical output schema (`modality = "categorical"`): one `{label, probability}` row per task category, validated against `task.categories` and converted to a `CategoricalForecast` payload. |
-| `agentic/predictor.py` | `AgentPredictor` | Track 1 `Predictor` that builds prompts, runs an ADK agent through `AdkTextRunner`, validates structured JSON, and converts it to `Prediction` objects. Accepts an optional injected runner for tests or custom observability. |
+| `agentic/predictor.py` | `AgentPredictor` | Track 1 `Predictor` that builds prompts, runs an ADK agent through `AdkTextRunner`, validates structured JSON, and converts it to `Prediction` objects. Accepts an optional injected runner for tests or custom observability; schema-validation failure logs include the Langfuse trace ID when available. |
 | `agentic/predictor.py` | `ForecastPromptBuilder` | Protocol for task-specific prompt builders that turn `(task, context)` into the text passed to the agent. |
diff --git a/aieng-forecasting/aieng/forecasting/methods/agentic/adk_runner.py b/aieng-forecasting/aieng/forecasting/methods/agentic/adk_runner.py
index 17fdfc918..b4dcbe513 100644
--- a/aieng-forecasting/aieng/forecasting/methods/agentic/adk_runner.py
+++ b/aieng-forecasting/aieng/forecasting/methods/agentic/adk_runner.py
@@ -295,15 +295,24 @@ async def run_text_async(
         content = genai_types.Content(role="user", parts=[genai_types.Part(text=prompt)])
 
         async def drain_run() -> str:
+            final_text: str | None = None
             async for event in self._runner.run_async(
                 user_id=user_id,
                 session_id=session_id,
                 new_message=content,
                 run_config=run_config,
             ):
-                if event.is_final_response() and event.content and event.content.parts:
-                    return event.content.parts[0].text or ""
-            return ""
+                if final_text is None and event.is_final_response() and event.content and event.content.parts:
+                    text_parts = [
+                        part.text
+                        for part in event.content.parts
+                        if isinstance(part.text, str)
+                        and part.text.strip()
+                        and getattr(part, "thought", False) is not True
+                    ]
+                    if text_parts:
+                        final_text = "".join(text_parts)
+            return final_text or ""
 
         async def run_and_resolve() -> str:
             """Run the agent and return the best available output string.
diff --git a/aieng-forecasting/aieng/forecasting/methods/agentic/predictor.py b/aieng-forecasting/aieng/forecasting/methods/agentic/predictor.py
index 25010e602..dea7e6878 100644
--- a/aieng-forecasting/aieng/forecasting/methods/agentic/predictor.py
+++ b/aieng-forecasting/aieng/forecasting/methods/agentic/predictor.py
@@ -296,7 +296,11 @@ def predict(self, task: ForecastingTask, context: ForecastContext) -> list[Predi
             try:
                 output = self.output_schema.model_validate(json.loads(output_str))
             except Exception:
-                logger.warning("Raw agent response (schema validation failed):\n%s", output_str)
+                logger.warning(
+                    "Raw agent response (schema validation failed; langfuse_trace_id=%s):\n%s",
+                    self._runner.last_trace_id,
+                    output_str,
+                )
                 raise
 
         # Convert output to list of predictions
diff --git a/aieng-forecasting/tests/aieng/forecasting/data/test_fred_adapter.py b/aieng-forecasting/tests/aieng/forecasting/data/test_fred_adapter.py
index 7c74e7b53..25cf48996 100644
--- a/aieng-forecasting/tests/aieng/forecasting/data/test_fred_adapter.py
+++ b/aieng-forecasting/tests/aieng/forecasting/data/test_fred_adapter.py
@@ -67,3 +67,12 @@ def test_missing_api_key_without_cache_raises(tmp_path: Path) -> None:
         adapter = FREDAdapter("EXCAUS", api_key=None, cache_dir=cache_dir)
         with pytest.raises(ValueError, match="FRED API key not provided"):
             adapter.fetch()
+
+
+def test_api_key_whitespace_is_trimmed() -> None:
+    """Whitespace around a pasted key must not be included in the request URL."""
+    fake = _fred_cls_returning(_raw_fred_series())
+    with patch("fredapi.Fred", fake):
+        FREDAdapter("EXCAUS", api_key="  fake-key\n", cache_dir=None, refresh=True).fetch()
+
+    assert fake.call_args.kwargs["api_key"] == "fake-key"
diff --git a/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_adk_runner.py b/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_adk_runner.py
index 179c6ea1d..83cb5d2e0 100644
--- a/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_adk_runner.py
+++ b/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_adk_runner.py
@@ -28,6 +28,7 @@ def _final_event(text: str) -> MagicMock:
     event.is_final_response.return_value = True
     part = MagicMock()
     part.text = text
+    part.thought = False
     event.content.parts = [part]
     return event
 
@@ -373,6 +374,41 @@ async def test_returns_text_from_first_final_event(self, patch_runner_cls, mock_
         runner = AdkTextRunner(mock_agent, config=AdkTextRunnerConfig(app_name="app"))
         assert await runner.run_text_async("hi") == "hello world"
 
+    async def test_joins_final_text_parts_and_ignores_thought_parts(self, patch_runner_cls, mock_agent) -> None:
+        """Use final answer text, not internal thought or function-call parts."""
+        event = _final_event("")
+        thought = MagicMock()
+        thought.text = "internal thought"
+        thought.thought = True
+        json_start = MagicMock()
+        json_start.text = '{"probability":'
+        json_start.thought = False
+        json_end = MagicMock()
+        json_end.text = "0.4}"
+        json_end.thought = False
+        event.content.parts = [thought, json_start, json_end]
+        patch_runner_cls.run_async.return_value = _stream(event)
+
+        runner = AdkTextRunner(mock_agent, config=AdkTextRunnerConfig(app_name="app"))
+
+        assert await runner.run_text_async("forecast") == '{"probability":0.4}'
+
+    async def test_drains_stream_after_first_final_event(self, patch_runner_cls, mock_agent) -> None:
+        """Consume cleanup events after capturing the final response."""
+        consumed = False
+
+        async def stream_with_trailing_event():
+            nonlocal consumed
+            yield _final_event("hello world")
+            yield _intermediate_event()
+            consumed = True
+
+        patch_runner_cls.run_async.return_value = stream_with_trailing_event()
+        runner = AdkTextRunner(mock_agent, config=AdkTextRunnerConfig(app_name="app"))
+
+        assert await runner.run_text_async("hi") == "hello world"
+        assert consumed
+
     async def test_returns_empty_string_when_stream_has_no_final_event(self, patch_runner_cls, mock_agent) -> None:
         """Stream with only non-final events yields an empty string."""
         patch_runner_cls.run_async.return_value = _stream(_intermediate_event())
diff --git a/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_predictor.py b/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_predictor.py
index aab25e2f1..02453055a 100644
--- a/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_predictor.py
+++ b/aieng-forecasting/tests/aieng/forecasting/methods/agentic/test_predictor.py
@@ -306,6 +306,16 @@ def test_schema_validation_errors_are_not_swallowed(self) -> None:
         with pytest.raises(ValidationError):
             predictor.predict(_task([1]), _context())
 
+    def test_schema_error_log_includes_langfuse_trace_id(self, caplog: pytest.LogCaptureFixture) -> None:
+        """Include trace IDs in parse failures for easier generation inspection."""
+        predictor, _ = _make_predictor(response="")
+        predictor._runner.last_trace_id = "trace-123"
+
+        with caplog.at_level(logging.WARNING), pytest.raises(json.JSONDecodeError):
+            predictor.predict(_task([1]), _context())
+
+        assert "langfuse_trace_id=trace-123" in caplog.text
+
 
 # ---------------------------------------------------------------------------
 # Async/sync bridge
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diff --git a/data/fred/UNRATE.parquet b/data/fred/UNRATE.parquet
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diff --git a/data/fred/VIXCLS.parquet b/data/fred/VIXCLS.parquet
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diff --git a/data/yfinance/spy_adj_close_1d.parquet b/data/yfinance/spy_adj_close_1d.parquet
new file mode 100644
index 000000000..112ac93ed
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diff --git a/data/yfinance/xli_adj_close_1d.parquet b/data/yfinance/xli_adj_close_1d.parquet
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diff --git a/implementations/__pycache__/__init__.cpython-312.pyc b/implementations/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 000000000..c5ab751af
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diff --git a/implementations/agentic_forecasting_implementations.egg-info/PKG-INFO b/implementations/agentic_forecasting_implementations.egg-info/PKG-INFO
new file mode 100644
index 000000000..c0cb18f9e
--- /dev/null
+++ b/implementations/agentic_forecasting_implementations.egg-info/PKG-INFO
@@ -0,0 +1,69 @@
+Metadata-Version: 2.4
+Name: agentic-forecasting-implementations
+Version: 0.1.0
+Summary: Reference method implementations for the Agentic Forecasting Bootcamp
+Author-email: Vector AI Engineering <ai_engineering@vectorinstitute.ai>
+Requires-Python: >=3.12
+Description-Content-Type: text/markdown
+Requires-Dist: aieng-forecasting[agentic,documents,llm,numerical]
+Requires-Dist: beautifulsoup4<5,>=4.12
+Requires-Dist: xgboost<4,>=2.1
+
+# implementations
+
+Self-contained reference implementations and their helper code.
+
+This is a local uv workspace package. It is installed automatically when you run `uv sync` from the repository root, but it is not a separately published public API.
+
+Some use cases are notebook-only. Others expose a small importable helper package so shared analysis, plotting, or data-registration code can live in Python modules instead of large notebook cells.
+
+---
+
+## Directory layout
+
+Numbered in the recommended order (mirrors the bootcamp progression: conventional numerical methods → LLM Processes → agents → agentic evaluation). The directories are not renamed — the numbers are an ordering convention used across the docs, and each directory stays an importable package (`from sp500_forecasting.data import ...`).
+
+```text
+implementations/
+|-- getting_started/          # 0 · CPI gasoline hello-world (start here)
+|   `-- specs/                #     backtest and eval YAML
+|-- sp500_forecasting/        # 1 · S&P 500 multivariate numerical comparison (financial markets)
+|   `-- specs/                #     backtest YAML (smoke + full)
+|-- food_price_forecasting/   # 2 · CFPR-style food CPI experiment
+|   `-- specs/                #     backtest YAML
+|-- energy_oil_forecasting/   # 3 · Daily WTI oil price forecasting experiment
+|   `-- specs/                #     backtest and eval YAML
+|-- boc_rate_decisions/       # 4 · Discrete-event reference: BoC cut/hold/hike direction
+|   `-- specs/                #     direction + binary backtest / eval / smoke YAML
+|-- tests/                    # tests for implementation-specific helper modules
+`-- pyproject.toml            # local workspace packaging
+```
+
+YAML backtest and eval specs live under each use case in `specs/`. Each directory is independent; see its `README.md` for the walkthrough. For the build-phase moves — onboarding data, standing up an experiment, customizing an agent, auditing a result — see [`guides/`](../guides/). To chat with the concierge or a domain starter in the ADK browser UI, see [`guides/05-access-adk-web-via-ssh-tunnel.md`](../guides/05-access-adk-web-via-ssh-tunnel.md) (includes the Coder SSH tunnel).
+
+Every domain use case (all except `getting_started`) also ships a `starter_agent/` module and a `99_starter_agent.ipynb` — a fresh, hackable **starter agent** that is the consistent "build your own" entry point for that use case (toggleable news search + code execution, two lightweight tool-usage skills, an interactive cell, and one scored forecast).
+
+`getting_started/` additionally ships a **`concierge_agent/`** module and **`99_repo_concierge.ipynb`** — a repo onboarding helper (not a forecaster) that answers questions about how the codebase works using a committed public-`main` knowledge digest. From the repository root: `uv run adk run implementations/getting_started/concierge_agent` (or `uv run adk web implementations/getting_started/concierge_agent` for the browser UI — [guide 5](../guides/05-access-adk-web-via-ssh-tunnel.md)). See [`getting_started/README.md`](getting_started/README.md) and the notebook for full usage.
+
+---
+
+## Relationship to `aieng-forecasting`
+
+- `aieng-forecasting` (`aieng.forecasting`) owns reusable infrastructure and reusable reference predictors under `aieng.forecasting.methods`.
+- `implementations/` owns use-case material: walkthrough notebooks, experiment-specific helper modules, plotting/analysis code, and task-specific framing.
+
+If code becomes broadly reusable across use cases, promote it into `aieng-forecasting`.
+
+---
+
+## Adding a new use case
+
+1. Create `implementations/<use-case>/`.
+2. Add a `README.md` describing the task, the data, and what the notebooks cover.
+3. Add YAML specs under `implementations/<use-case>/specs/`.
+4. Start with notebooks as the primary user surface.
+5. If notebook code becomes bulky or repeated, extract small helper modules into that use-case directory.
+6. Add tests under `implementations/tests/<use-case>/` for non-trivial helper logic.
+7. Promote code into `aieng-forecasting` once it is clearly reusable across more than one use case.
+
+For architecture principles and cross-cutting extension ideas, see `planning-docs/roadmap.md`.
diff --git a/implementations/agentic_forecasting_implementations.egg-info/SOURCES.txt b/implementations/agentic_forecasting_implementations.egg-info/SOURCES.txt
new file mode 100644
index 000000000..3e509917e
--- /dev/null
+++ b/implementations/agentic_forecasting_implementations.egg-info/SOURCES.txt
@@ -0,0 +1,76 @@
+README.md
+pyproject.toml
+agentic_forecasting_implementations.egg-info/PKG-INFO
+agentic_forecasting_implementations.egg-info/SOURCES.txt
+agentic_forecasting_implementations.egg-info/dependency_links.txt
+agentic_forecasting_implementations.egg-info/requires.txt
+agentic_forecasting_implementations.egg-info/top_level.txt
+boc_rate_decisions/__init__.py
+boc_rate_decisions/analysis.py
+boc_rate_decisions/data.py
+boc_rate_decisions/plots.py
+boc_rate_decisions/press_releases.py
+boc_rate_decisions/rationale_eval.py
+boc_rate_decisions/analyst_agent/__init__.py
+boc_rate_decisions/analyst_agent/agent.py
+boc_rate_decisions/predictors/__init__.py
+boc_rate_decisions/predictors/llmp_binary.py
+boc_rate_decisions/predictors/llmp_direction.py
+boc_rate_decisions/predictors/logistic_baseline.py
+boc_rate_decisions/starter_agent/__init__.py
+boc_rate_decisions/starter_agent/agent.py
+energy_oil_forecasting/__init__.py
+energy_oil_forecasting/analysis.py
+energy_oil_forecasting/data.py
+energy_oil_forecasting/paths.py
+energy_oil_forecasting/prophet_baseline.py
+energy_oil_forecasting/tasks.py
+energy_oil_forecasting/viz.py
+energy_oil_forecasting/adaptive_agent/__init__.py
+energy_oil_forecasting/adaptive_agent/agent.py
+energy_oil_forecasting/adaptive_agent/skill_state.py
+energy_oil_forecasting/adaptive_agent/skill_tools.py
+energy_oil_forecasting/adaptive_agent/curriculum/snapshot_utils.py
+energy_oil_forecasting/analyst_agent/__init__.py
+energy_oil_forecasting/analyst_agent/agent.py
+energy_oil_forecasting/starter_agent/__init__.py
+energy_oil_forecasting/starter_agent/agent.py
+energy_oil_forecasting/starter_agent/tools.py
+food_price_forecasting/__init__.py
+food_price_forecasting/analysis.py
+food_price_forecasting/data.py
+food_price_forecasting/plots.py
+food_price_forecasting/reports.py
+food_price_forecasting/smoke_report.py
+food_price_forecasting/predictors/__init__.py
+food_price_forecasting/predictors/llmp_quantile_grid.py
+food_price_forecasting/predictors/llmp_sampled_trajectory.py
+food_price_forecasting/starter_agent/__init__.py
+food_price_forecasting/starter_agent/agent.py
+getting_started/__init__.py
+getting_started/concierge_agent/__init__.py
+getting_started/concierge_agent/agent.py
+getting_started/concierge_agent/catalog.py
+getting_started/concierge_agent/catalog_build.py
+getting_started/concierge_agent/knowledge.py
+manufacturing_stress_forecasting/__init__.py
+manufacturing_stress_forecasting/data.py
+manufacturing_stress_forecasting/features.py
+manufacturing_stress_forecasting/run_agent_backtest.py
+manufacturing_stress_forecasting/run_agent_prediction.py
+manufacturing_stress_forecasting/run_smoke.py
+manufacturing_stress_forecasting/targets.py
+manufacturing_stress_forecasting/analyst_agent/__init__.py
+manufacturing_stress_forecasting/analyst_agent/agent.py
+manufacturing_stress_forecasting/predictors/__init__.py
+manufacturing_stress_forecasting/predictors/logistic.py
+manufacturing_stress_forecasting/predictors/xgboost.py
+sp500_forecasting/__init__.py
+sp500_forecasting/analysis.py
+sp500_forecasting/data.py
+sp500_forecasting/leaderboard.py
+sp500_forecasting/plots.py
+sp500_forecasting/predictors/__init__.py
+sp500_forecasting/predictors/llmp_sampled_trajectory.py
+sp500_forecasting/starter_agent/__init__.py
+sp500_forecasting/starter_agent/agent.py
\ No newline at end of file
diff --git a/implementations/agentic_forecasting_implementations.egg-info/dependency_links.txt b/implementations/agentic_forecasting_implementations.egg-info/dependency_links.txt
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/implementations/agentic_forecasting_implementations.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/implementations/agentic_forecasting_implementations.egg-info/requires.txt b/implementations/agentic_forecasting_implementations.egg-info/requires.txt
new file mode 100644
index 000000000..0f935ba3c
--- /dev/null
+++ b/implementations/agentic_forecasting_implementations.egg-info/requires.txt
@@ -0,0 +1,3 @@
+aieng-forecasting[agentic,documents,llm,numerical]
+beautifulsoup4<5,>=4.12
+xgboost<4,>=2.1
diff --git a/implementations/agentic_forecasting_implementations.egg-info/top_level.txt b/implementations/agentic_forecasting_implementations.egg-info/top_level.txt
new file mode 100644
index 000000000..328569e9e
--- /dev/null
+++ b/implementations/agentic_forecasting_implementations.egg-info/top_level.txt
@@ -0,0 +1,6 @@
+boc_rate_decisions
+energy_oil_forecasting
+food_price_forecasting
+getting_started
+manufacturing_stress_forecasting
+sp500_forecasting
diff --git a/implementations/getting_started/00_environment_check.ipynb b/implementations/getting_started/00_environment_check.ipynb
index 1564db1c4..fb36f228a 100644
--- a/implementations/getting_started/00_environment_check.ipynb
+++ b/implementations/getting_started/00_environment_check.ipynb
@@ -49,10 +49,20 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "id": "d3f3d463",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Repository root: /home/coder/agentic-forecasting\n",
+            ".env present:    True\n",
+            "Helpers ready.\n"
+          ]
+        }
+      ],
       "source": [
         "from __future__ import annotations\n",
         "\n",
@@ -173,10 +183,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "id": "268ed976",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tier         Variable               Status           Value\n",
+            "----------------------------------------------------------------------\n",
+            "Required     OPENAI_BASE_URL        ✅ set            https://proxy.vectorinstitute.ai/v1\n",
+            "Required     OPENAI_API_KEY         ✅ set            …e7b6\n",
+            "Required     E2B_API_KEY            ✅ set            …a984\n",
+            "Recommended  LANGFUSE_PUBLIC_KEY    ✅ set            …0772\n",
+            "Recommended  LANGFUSE_SECRET_KEY    ✅ set            …94fd\n",
+            "Recommended  LANGFUSE_HOST          ✅ set            https://us.cloud.langfuse.com\n",
+            "Optional     FRED_API_KEY           ✅ set            …e it\n",
+            "\n",
+            "Legend: ✅ set   ⚠️ still a placeholder from .env.example   ❌ not set\n"
+          ]
+        }
+      ],
       "source": [
         "_INVENTORY = [\n",
         "    (\"Required\", \"OPENAI_BASE_URL\"),\n",
@@ -223,10 +251,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "id": "2a74283d",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅  PASS — Package imports & LightGBM/OpenMP\n",
+            "      aieng.forecasting, LightGBM 4.6.0, default model 'gemini-3.1-flash-lite-preview'.\n"
+          ]
+        }
+      ],
       "source": [
         "try:\n",
         "    # Import the project and LightGBM; LightGBM's import triggers the native\n",
@@ -280,10 +317,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "id": "6b959bbc",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅  PASS — LLM inference via proxy\n",
+            "      Model 'gemini-3.1-flash-lite-preview' responded: 'OK'\n"
+          ]
+        }
+      ],
       "source": [
         "_NAME = \"LLM inference via proxy\"\n",
         "\n",
@@ -349,10 +395,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "id": "2010eb9a",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅  PASS — Langfuse tracing\n",
+            "      Authenticated to https://us.cloud.langfuse.com (public key …0772).\n"
+          ]
+        }
+      ],
       "source": [
         "_NAME = \"Langfuse tracing\"\n",
         "\n",
@@ -412,10 +467,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "id": "0a5ec930",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅  PASS — E2B code execution\n",
+            "      Sandbox (template 'agentic-forecasting-bootcamp') executed code and returned: '2'\n"
+          ]
+        }
+      ],
       "source": [
         "_NAME = \"E2B code execution\"\n",
         "\n",
@@ -492,10 +556,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "id": "f4fd163a",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅  PASS — StatCan data pull\n",
+            "      Fetched cpi_gasoline_canada: 931 rows, 1949-01 → 2026-07.\n"
+          ]
+        }
+      ],
       "source": [
         "_NAME = \"StatCan data pull\"\n",
         "\n",
@@ -545,10 +618,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "id": "d74f5ea7",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅  PASS — FRED data pull\n",
+            "      Validated FRED key — fetched EXCAUS (CAD/USD): 667 rows, latest 2026-07 = 1.4105.\n"
+          ]
+        }
+      ],
       "source": [
         "_NAME = \"FRED data pull\"\n",
         "\n",
@@ -603,10 +685,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "id": "59a46292",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅  PASS — End-to-end mini forecast\n",
+            "      Ran last_value_naive over the gasoline backtest — mean CRPS = 10.1083.\n"
+          ]
+        }
+      ],
       "source": [
         "_NAME = \"End-to-end mini forecast\"\n",
         "\n",
@@ -667,10 +758,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "id": "3d141741",
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "================================================================\n",
+            "  Checks run: 7    ✅ 7    ⚠️ 0    ❌ 0\n",
+            "================================================================\n",
+            "\n",
+            "🎉 You're ready! Open 01_cpi_data_exploration.ipynb to begin.\n"
+          ]
+        }
+      ],
       "source": [
         "_passed = [r for r in RESULTS if r[\"status\"] == \"PASS\"]\n",
         "_warned = [r for r in RESULTS if r[\"status\"] == \"WARN\"]\n",
@@ -702,7 +805,7 @@
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": ".venv",
+      "display_name": ".venv (3.12.3)",
       "language": "python",
       "name": "python3"
     },
@@ -716,7 +819,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.12.12"
+      "version": "3.12.3"
     }
   },
   "nbformat": 4,
diff --git a/implementations/getting_started/01_cpi_data_exploration.ipynb b/implementations/getting_started/01_cpi_data_exploration.ipynb
index 6c7abbafe..feb97d337 100644
--- a/implementations/getting_started/01_cpi_data_exploration.ipynb
+++ b/implementations/getting_started/01_cpi_data_exploration.ipynb
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "63b3c541",
    "metadata": {
     "execution": {
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "24ba4b45",
    "metadata": {
     "execution": {
@@ -77,8 +77,54 @@
      "shell.execute_reply": "2026-04-17T19:06:02.630586Z"
     }
    },
-   "outputs": [],
-   "source": "CPI_TABLE_ID = \"18-10-0004-11\"\n# Resolve the repo-root data cache the same way 00/02 do, so this notebook\n# reuses the cache populated by `scripts/fetch_cpi.py` regardless of CWD.\nROOT = Path.cwd().resolve().parents[1]\nCACHE_DIR = ROOT / \"data\" / \"statcan\"\n\n# (series_id, StatCan product-group label, short label)\nCPI_SERIES = [\n    (\"cpi_all_items_canada\", \"All-items\", \"All-items\"),\n    (\"cpi_gasoline_canada\", \"Gasoline\", \"Gasoline\"),\n    (\"cpi_shelter_canada\", \"Shelter\", \"Shelter\"),\n]\n\nsvc = DataService()\n\nfor series_id, product_group, short_label in CPI_SERIES:\n    adapter = StatCanAdapter(\n        table_id=CPI_TABLE_ID,\n        member_filter={\"GEO\": \"Canada\", \"Products and product groups\": product_group},\n        cache_dir=CACHE_DIR,\n    )\n    metadata = SeriesMetadata(\n        series_id=series_id,\n        description=f\"CPI {short_label}, Canada (2002=100)\",\n        source=\"StatCan\",\n        units=\"Index 2002=100\",\n        frequency=\"MS\",\n        table_id=CPI_TABLE_ID,\n    )\n    svc.register(series_id, adapter, metadata)\n    print(f\"  Registered: {series_id}\")\n\nprint(\"\\nDone.\")"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Registered: cpi_all_items_canada\n",
+      "  Registered: cpi_gasoline_canada\n",
+      "  Registered: cpi_shelter_canada\n",
+      "\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "CPI_TABLE_ID = \"18-10-0004-11\"\n",
+    "# Resolve the repo-root data cache the same way 00/02 do, so this notebook\n",
+    "# reuses the cache populated by `scripts/fetch_cpi.py` regardless of CWD.\n",
+    "ROOT = Path.cwd().resolve().parents[1]\n",
+    "CACHE_DIR = ROOT / \"data\" / \"statcan\"\n",
+    "\n",
+    "# (series_id, StatCan product-group label, short label)\n",
+    "CPI_SERIES = [\n",
+    "    (\"cpi_all_items_canada\", \"All-items\", \"All-items\"),\n",
+    "    (\"cpi_gasoline_canada\", \"Gasoline\", \"Gasoline\"),\n",
+    "    (\"cpi_shelter_canada\", \"Shelter\", \"Shelter\"),\n",
+    "]\n",
+    "\n",
+    "svc = DataService()\n",
+    "\n",
+    "for series_id, product_group, short_label in CPI_SERIES:\n",
+    "    adapter = StatCanAdapter(\n",
+    "        table_id=CPI_TABLE_ID,\n",
+    "        member_filter={\"GEO\": \"Canada\", \"Products and product groups\": product_group},\n",
+    "        cache_dir=CACHE_DIR,\n",
+    "    )\n",
+    "    metadata = SeriesMetadata(\n",
+    "        series_id=series_id,\n",
+    "        description=f\"CPI {short_label}, Canada (2002=100)\",\n",
+    "        source=\"StatCan\",\n",
+    "        units=\"Index 2002=100\",\n",
+    "        frequency=\"MS\",\n",
+    "        table_id=CPI_TABLE_ID,\n",
+    "    )\n",
+    "    svc.register(series_id, adapter, metadata)\n",
+    "    print(f\"  Registered: {series_id}\")\n",
+    "\n",
+    "print(\"\\nDone.\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -90,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "87fa42b8",
    "metadata": {
     "execution": {
@@ -140,9 +186,9 @@
        "      <td>StatCan</td>\n",
        "      <td>Index 2002=100</td>\n",
        "      <td>MS</td>\n",
-       "      <td>1347</td>\n",
+       "      <td>1351</td>\n",
        "      <td>1914-01</td>\n",
-       "      <td>2026-03</td>\n",
+       "      <td>2026-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -151,9 +197,9 @@
        "      <td>StatCan</td>\n",
        "      <td>Index 2002=100</td>\n",
        "      <td>MS</td>\n",
-       "      <td>927</td>\n",
+       "      <td>931</td>\n",
        "      <td>1949-01</td>\n",
-       "      <td>2026-03</td>\n",
+       "      <td>2026-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -162,9 +208,9 @@
        "      <td>StatCan</td>\n",
        "      <td>Index 2002=100</td>\n",
        "      <td>MS</td>\n",
-       "      <td>571</td>\n",
+       "      <td>575</td>\n",
        "      <td>1978-09</td>\n",
-       "      <td>2026-03</td>\n",
+       "      <td>2026-07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -177,12 +223,12 @@
        "2    cpi_shelter_canada    CPI Shelter, Canada (2002=100)  StatCan   \n",
        "\n",
        "            units frequency  n_obs    start      end  \n",
-       "0  Index 2002=100        MS   1347  1914-01  2026-03  \n",
-       "1  Index 2002=100        MS    927  1949-01  2026-03  \n",
-       "2  Index 2002=100        MS    571  1978-09  2026-03  "
+       "0  Index 2002=100        MS   1351  1914-01  2026-07  \n",
+       "1  Index 2002=100        MS    931  1949-01  2026-07  \n",
+       "2  Index 2002=100        MS    575  1978-09  2026-07  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -210,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "d5249c2b",
    "metadata": {
     "execution": {
@@ -300,7 +346,7 @@
        "887 2022-12-01  208.3  2022-12-22"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -329,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "7f3483f9",
    "metadata": {
     "execution": {
@@ -342,7 +388,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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TdOnq+2OrU6dOmfehiIjUDBq+JyIiHhUWFkZqamq5nrNlyxYee+wxpk+fTlJSUrF9x/bHAfOB/tghb/YQu2OnRX/77bd59913WbNmDU6ns9i+I0eOFKxv3bqV6OjoEkkif39/mjRpUuzYisR8LPs65X2vAJ544gl69+6Nl5cXoaGhtGzZstyJQFtGRgbJyckVei6YPjmRkZEVfn5pyvP9BWjevHmJbevWrSM1NbXEcKaiDhw4QPPmzVm3bh2WZdGyZcsyj60Ie4r7okORbPa2rVu30qVLl4LETceOHSt0rdL06dOHESNGMHnyZD799FO6du1Kv379GD58OK1bty7zuXFxcTz22GO8+OKLREdH06FDB84//3yuvPJKunbtWnBced5rW2nfs9IcPHiQQ4cOMWPGjOPeZ0WTda+99hrXX3897dq1o0mTJpx77rkMGTKEIUOGnDCpZ//cWpZ1wrgmTpzIgw8+yODBg3nzzTeL7bMTpFlZWSWel5mZWewYe720Y+3jj024uhoHQJMmTWjSpMkJX09lBQUFHTdhdOxrLu/7Y7Msy6V/W0VExLOUlBIREY9q27Yt8+bNY+vWrS59GDp69CjnnHMOaWlp3HPPPbRr147Q0FC8vLx48cUXS+0Fc2xPoaKKfqB89dVXuf/+++nfvz933303MTEx+Pn5sWfPHm644YYSSSpXVSTmY7Vt2xaAZcuWcdlll5Xr+u3atSuo9qqsL7/8ssT09OURFxdXoil1Zbn6/bUd7wNsZGRkQRPp0tjfA/vD7i+//HLca5eWVPKUsj6Y5+bmltj20Ucf8eCDD/LLL78wf/58XnnlFZ5//nlee+017rzzzjKv9dxzz3HjjTfy888/M3/+fCZOnMh//vMfHnroIcaPHw+U7722lZVoKcr+fvfr14+HH374hMdfcsklbN++nWnTpjF37lxmzZrFBx98QO/evZk1a1apEwrY7KTX4cOHy7zGpEmTuOWWW+jfvz/ffvttQTWmLSYmBjA97Y5lb2vQoEGx40s71j6+6LHliQPMv1VHjx4t8/XY/Pz8XK4SO1ZMTAxr164lKyurRAXUnj17qFu3bsF7X/T9ObZarrT3x3b48GG3J8BFRMT9lJQSERGPGjp0KPPmzWPixIm88MILJzz+t99+Y+/evUyaNKlEcmTcuHGVimXKlCnEx8fzyy+/FKuS+PXXX0sc26RJE2bMmEFKSkqxaqmsrCy2bt1K7dq13Rrz4MGDCQgIYMqUKTz22GPHHfpS1QYMGMDMmTMr/PzAwEA3RuM+Z5xxBhs3bqR79+7FZvg63rG//vorjRo1cmlIWXnYidk1a9bQtGnTYvvWrl1b7Bi7emj58uVlVhLZiYPSkifHzkZna9u2LW3btuXBBx8kKSmJbt268cgjjzBmzJgTVp80adKEu+66i7vuuovMzEwGDBjASy+9xP33309UVFS53uvyioyMJDw8nJSUFJcTsREREVx33XVcd911WJbFI488wksvvcQPP/zAlVdeedzn2YnH0oaI2iZNmsRNN91Ev379+P7770v9uY2OjqZBgwb89ddfJfbZ27p06VKwrWvXrnz66afs2rWrWLPzXbt2sXfv3hJN4l2NA+Dll1/m6aefPu7rKapPnz7MmTPHpWOP1bVrV2bMmMGiRYuKTfKQmZnJ8uXLOeecc4odC7Bw4cIS39O//vqLsLCwEvd/VlYWu3btOu6MriIiUnOop5SIiHjUTTfdRIsWLXj55Zf54YcfSj1m6dKlvP3220BhVcyxFTAzZsxwqTdTWby9vXE4HMXOnZuby7///e8Sx15yySXk5eWV6KvyzjvvlOjF446Yo6KiePDBB9m+fTs33XQT2dnZJY5JSUnh3nvvdel8FRUdHU2/fv0q/NWzZ88qja+iRowYgdPpZOzYsaXuLzoc7/rrrwfg0UcfJS8vr8xjy+viiy/G4XDwn//8p9g09/v27ePDDz8kLi6uYLjeFVdcgZ+fH08//XSp/Z/s+61x48b4+Pgwa9asYvsXLFhQIhFy+PDhEhWB4eHhNG7cmPT09ILhUqVJTk4uFjOYnj924s4e0lqe97q8vLy8uPbaa1m0aFHBzJ7HsoeN5eXllTqU1n5/T1QBFRkZSZs2bUpNJoGZcfPmm2/mvPPO44cffihz1syrr76aLVu28NNPPxVsy8vL47///S/h4eHFZly8+uqrATP0sCj78bGz+JUnjurqKTV8+HAcDkeJ1/D++++Tnp5e7DX06dOH6OhoJk6cWKyKa8WKFcyZM4crr7yyRNXXsmXLyM7Opk+fPhWOUUREqocqpURExKOCgoKYOnUqgwcP5tJLL6V///5ccMEF1KlTh4MHDzJ79mymT5/OQw89BECvXr2oX78+999/P9u3b6dhw4YsX76cKVOm0K5dO1atWlXhWK644grGjh3LwIEDufzyy0lJSeGzzz4rdZjLqFGjeO+993jmmWfYtm0bPXr0YNmyZXz99dc0bdq02LAod8X81FNPsW/fPiZOnMgff/zBVVddRbNmzcjOzmb58uV8/fXX+Pn5MWHChAq/B6erK664glGjRvHmm2/yzz//cNFFF1G3bl12797NwoUL2bx5c0FVUdeuXXnqqad46qmn6NChA1deeSUxMTHs27ePpUuXMm3atFKThq5o0aIFDz74IC+99BLnnHMOw4cPJzU1lffee4+jR4/y6aefFiQ5GzZsyGuvvcaYMWNo164dI0aMIC4ujj179vDDDz8wadIkOnToQEhICDfccAMTJ07k6quvpm/fvmzatIkPP/yQ9u3bs2LFioLrf/zxx0yYMIHLLruMZs2a4evry9y5c5k+fTrDhg0rs9Jt9uzZ3HLLLQwdOpQWLVoQEhLC0qVLmThxIt26daNFixblfq8r4vnnn+fPP/9k2LBhDBs2jO7du+Pn58eOHTuYNm0anTt3ZvLkyaSmphIdHc3FF19Mx44diYqKYtu2bbzzzjvUrl2bIUOGnPBaV155Jc8++yz79u0jOjq6YPuPP/7I6NGjCQsLY/jw4Xz77bfFnhcSEsKll15a8PiRRx7h66+/5pprruG+++6jQYMGfP755yxevJiJEycW6wE3ePBgLrroIl599VWSk5Pp0aMHCxcu5IMPPuC6666jV69eFY6jMj2lkpOT+e9//wvA3r17AZg3bx7PPfccYBKu7du3B8yQ4jFjxvDmm29y+eWXM2jQINatW8cbb7xBnz59uOaaawrO6+vry+uvv87w4cPp3bs3N998MykpKUyYMIHIyMhSK7umTZuGr69vsdcmIiI1VPVP+CciIlJSWlqa9eqrr1o9e/a0wsPDLR8fHysqKsoaNGiQ9fHHH1u5ubkFx65YscIaMGCAFR4eboWEhFh9+vSx5s2bV2J6e8uyrD59+lhxcXElrmdPNV90yvLc3FzrhRdesJo2bWr5+flZjRo1sh588EFr7dq1pU5vfujQIevGG2+0IiIirKCgIKtPnz7W4sWLS71meWI+kZkzZ1pXXHGF1aBBA8vX19cKCQmxOnXqZI0bN87au3dvwXEffvihBVhff/31Cc9pTwW/ePHicsVS1eLi4kr9/tnK8/2134/Zs2cf93wff/yx1atXLys0NNTy9/e34uLirMsuu8z64osvShw7depUq3///lbt2rUtPz8/q2HDhtaFF15ovfPOOy69NsAaOXJkqfvee+89q0OHDpa/v78VGhpq9evXz5o3b16px06fPt3q16+fFRYWZvn7+1uNGze2brrpJisxMbHgmNTUVGv06NFWRESEFRgYaPXq1cv6888/S9x/y5Yts0aMGGE1bdrUCgoKskJDQ6327dtbL7/8spWZmVnm69m6dat16623Wi1btrRCQ0OtoKAgq2XLltbjjz9uJSUllTjelfe6tO9jUfZ9u23btmLb09LSrGeeecZq27atFRAQYIWEhFgtW7a0brrpJuuvv/6yLMuysrKyrEceecTq2rWrFRERYfn5+VlxcXHWqFGjrI0bN5b5Wm179uyxfHx8rJdffrnUuI73Vdo9u3v3buu6666z6tSpY/n7+1sdO3Ys9b6zLMvKyMiwHnvsMSsuLs7y8/OzGjdubD3zzDNWdnZ2peOoKPt7dbyvDz/8sNjxubm51ssvv2w1b97c8vPzs2JiYqx7773XSk1NLfX8P/30k9WtWzcrMDDQCg8Pt4YOHWpt3ry51GMbN25sXXHFFW57bSIiUnUcluXClCEiIiIiIlLCbbfdxowZM9iwYUOpVZVSvX744Qcuv/xyli5dSocOHTwdjoiInICSUiIiIiIiFZSQkMAZZ5zBf/7zH2655RZPh3NasyyLTp060aFDBz788ENPhyMiIi5QUkpERERERERERKqdZt8TEREREREREZFqp6SUiIiIiIiIiIhUOyWlRERERERERESk2ikpJSIiIiIiIiIi1c7H0wHUBE6nk7179xIaGorD4fB0OCIiIiIiIiIiJy3LskhNTSUmJgYvr+PXQykpBezdu5fY2FhPhyEiIiIiIiIicsrYtWsXDRs2PO5+JaWA0NBQwLxZYWFhHo6m4pxOJwcPHiQyMrLMTKSIu+iek+qk+02qk+43qW6656Q66X6T6qT77fSUkpJCbGxsQb7leJSUgoIhe2FhYSd9UiozM5OwsDD9sEu10D0n1Un3m1Qn3W9S3XTPSXXS/SbVSffb6e1ELZJ0R4iIiIiIiIiISLVTUkpERERERERERKqdklIiIiIiIiIiIlLt1FNKRERERERERDwuLy+PnJwcT4chLvD19cXb27vS51FSSkREREREREQ8xrIs9u/fT1JSkqdDkXIIDw+nfv36J2xmXhYlpURERERERETEY+yEVFRUFEFBQZVKckjVsyyL9PR0EhISAIiOjq7wuZSUEhERERERERGPyMvLK0hI1alTx9PhiIsCAwMBSEhIICoqqsJD+dToXEREREREREQ8wu4hFRQU5OFIpLzs71ll+oB5NCn1zjvv0L59e8LCwggLC6NHjx788ssvBfszMzMZM2YMderUISQkhKFDh3LgwIFi59i5cyeDBw8mKCiIqKgoHnzwQXJzc6v7pYiIiIiIiIhIBWnI3snHHd8zjyalGjZsyL///W+WLl3KkiVLOO+887jkkktYs2YNAPfeey8//fQTX3/9NXPnzmXv3r1cfvnlBc/Py8tj8ODBZGdns2DBAj766CMmT57ME0884amXJCIiIiIiIiIiLvBoUmrIkCEMGjSIM844g+bNm/P8888TEhLCX3/9RXJyMh988AGvvvoq5513Hp07d+bDDz9kwYIF/PXXXwDMmDGDtWvX8sknn9ChQwcGDhzIs88+y1tvvUV2drYnX5qIiIiIiIiInMbmzJmDw+EomFVw8uTJhIeHl/mcG264gUsvvbTKY6spakxPqby8PL744gvS0tLo0aMHS5cuJScnh379+hUc07JlSxo1asTChQsBWLhwIe3ataNevXoFxwwYMICUlJSCaisRERERERERkaqycOFCvL29GTx4cKXP9frrrzN58uSCx3379uWee+6p9HlrKo/Pvrdq1Sp69OhBZmYmISEh/O9//6N169YsX74cPz+/ElnEevXqsX//fsBMG1k0IWXvt/cdT1ZWFllZWQWPU1JSAHA6nTidTne8LI9wOp1YlnVSvwY5ueiek+qk+02qk+43qW6656Q66X7zkIMbcCz5AKv7GKgd5+loqs2J7jd7v/11Mpo4cSJ33nknkyZNYs+ePcTExAAUvJ5jX19ZrzMsLKzEMTX1vbHjKi2X4uq/Lx5PSrVo0YLly5eTnJzMN998w8iRI5k7d26VXvPFF1/k6aefLrH94MGDZGZmVum1q5LT6SQ5ORnLsvDyqjFFcHIK0z0n1Un3m1Qn3W9S3XTPSXXS/eYZtX57nsBNP5GekUlqr3GeDqfanOh+y8nJwel0kpube1JOWnb06FG++uorFi5cyL59+5g0aRKPPPIIYEaEAQWvzU7UlPU6R48eTVJSEt9++y2jR49m7ty5zJ07lzfeeAOAjRs3Eh8fz+rVqxk7dix//PEHwcHB9OvXj5dffpm6desC0K9fP9q2bYu3tzdTpkzBz8+Pp59+mquuuop//etffPfdd9SrV48JEyZw4YUXAnDkyBH+9a9/MWvWLI4ePUrDhg15+OGHGTlyZKmx2q/p0KFD+Pr6FtuXmprq0vvn8aSUn58fzZo1A6Bz584sXryY119/neHDh5OdnU1SUlKxaqkDBw5Qv359AOrXr8+iRYuKnc+enc8+pjRjx47lvvvuK3ickpJCbGwskZGRBVnJk5HT6cThcBAZGan/XKRa6J6T6qT7TaqT7jepbrrnpDrpfvMMR9peAIKcqQRGRXk4mupzovstMzOT1NRUfHx88PExKYrcPCcHj2aVOLY6RIb44+Pt+s/Fd999R8uWLWnTpg3XX3899957L4899hgOhwNvb2+Agtdmv377dZbGy8sLLy8vfHx8eOONN9i8eTNt2rThmWeeMfFFRpKamsqAAQMYPXo0EyZMICMjg0ceeYRrr72W3377DTAz402ZMoUHH3yQv//+my+//JI777yTH3/8kUsvvZTHHnuMCRMmMGrUKHbs2EFQUBBPP/0069evZ9q0adStW5fNmzeTkZFx3Hjt11SnTh0CAgKK7Tv28fF4PCl1LKfTSVZWFp07d8bX15fffvuNoUOHArBhwwZ27txJjx49AOjRowfPP/88CQkJROX/UM+cOZOwsDBat2593Gv4+/vj7+9fYrv9zT+ZORyOU+J1yMlD95xUJ91vUp10v0l10z0n1Un3mwck7QDAkX4Ix2n2vpd1v3l5eeFwOAq+ABLTsjn737OrO0wAFo49j+hagS4fP2nSJK677jocDgcDBw7kxhtvZN68efTt27fg9Rz7+uxlWRwOB+Hh4fj5+REcHEx0dHTBvrfeeouOHTvy4osvFosjNjaWTZs20bx5cwDOPPNMHn/8cQAeffRRxo8fT926dbnlllsAePLJJ3n33XdZtWoV3bt3Z9euXXTs2JGuXbsC0Lhx4xPGeLzvrav/tnj0J2Hs2LHMmzeP7du3s2rVKsaOHcucOXO49tprqVWrFqNHj+a+++5j9uzZLF26lFGjRtGjRw+6d+8OQP/+/WndujXXX389K1asYPr06YwbN44xY8aUmnQSERERERERqXbZaZCeaNbTD3s2FnGbDRs2sGjRIq6++mrAVA4NHz6cDz744ITP3blzJyEhIQVfL7zwgsvXXbFiBbNnzy72/JYtWwKwZcuWguPat29fsO7t7U2dOnVo165dwTa7J3dCQgIAt99+O1988QUdOnTgoYceYsGCBS7HVFEerZRKSEhgxIgR7Nu3j1q1atG+fXumT5/OBRdcAMCECRPw8vJi6NChZGVlMWDAAN5+++2C53t7ezN16lRuv/12evToQXBwMCNHjiwoaxMRERERERHxuKSdhevphzwXx0kiMsSfhWPP89i1XfXBBx+Qm5tb0NgcTPNvf39/3nzzzTKfGxMTw/LlywseR0REuHzdo0ePMmTIEMaPH19iX9GKqmP7PDkcjmLb7Iotu9fVwIED2bFjB9OmTWPmzJmcf/75jBkzhpdfftnl2MrLo0mpE2UPAwICeOutt3jrrbeOe0xcXBzTpk1zd2giIiIiIiIi7nFsUsqywIUhXKcrH2+vcg2h84Tc3Fw+/vhjXnnlFfr3719s36WXXsrnn39eUL1UGh8fn4L+2mXx8/MraJhu69SpE99++y3x8fFl9qeqiMjISEaOHMnIkSPp3bs3Dz74YJUmpU6vgawiIiIiIiIi1a1oUsqZA1muzUwmNdfUqVM5cuQIo0ePpm3btsW+hg4d6tIQPlfEx8fz999/s337dhITE3E6nYwZM4bDhw9z9dVXs3jxYrZs2cL06dMZNWpUiQRWeTzxxBP88MMPbN68mTVr1jB16lRatWrlltdxPEpKiYiIiIiIiFSlI9uLP9YQvpPeBx98QL9+/ahVq1aJfUOHDmXJkiWsXLmy0td54IEH8Pb2pnXr1kRGRrJz505iYmL4888/ycvLo3///rRr14577rmH8PDwSk1e4Ofnx9ixY2nfvj3nnHMO3t7efPHFF5V+DWVxWJZlVekVTgIpKSnUqlWL5ORkwsLCPB1OhTmdzoKZCDWLhlQH3XNSnXS/SXXS/SbVTfecVCfdbx7w5fWw7sfCxzf/Dg06ey6eanSi+y0zM5Nt27bRuHFjAgICPBChVFRZ3ztX8yz6F0hERERERESkKhUdvgeagU8kn5JSIiIiIiIiIlUpaUfxxxq+JwIoKSUiIiIiIiJSdTJTIOOIWffJH+KkpJQIoKSUiIiIiIiISNVJ3lW4Xr+9WSopJQIoKSUiIiIiIiJSdY7kD93zrwURTcy6klIigJJSIiIiIiIiIlXHbnIe3giC6ph1JaVEACWlRERERERERKqO3eQ8vBEERZj19COei0ekBlFSSkRERERERKSq2JVSteNUKSVyDCWlRERERERERKpKsUopJaVEilJSSkRERERERKSqHLF7Sh1TKWVZnotJTjl9+/blnnvuKXgcHx/Pa6+95rF4XKWklIiIiIiIiEhVyEiCrGSzXrSnlJUHmckeC0vca//+/fzrX/+iWbNmBAQEUK9ePXr27Mk777xDenq6R2JavHgxt9xyi0euXR4+ng5ARERERERE5JRk95MCk5TKzSx8nH4IAsOrPSRxr61bt9KzZ0/Cw8N54YUXaNeuHf7+/qxatYr33nuPBg0acPHFF1d7XJGRkdV+zYpQpZSIiIiIiIhIVbD7SQXWhoAws7SlH/ZMTOJWd9xxBz4+PixZsoRhw4bRqlUrmjRpwiWXXMLPP//MkCFDAHj11Vdp164dwcHBxMbGcscdd3D06NGC8+zYsYMhQ4ZQu3ZtgoODadOmDdOmTSvYP3fuXM466yz8/f2Jjo7mkUceITc397hxHTt8z+FwMHHiRC677DKCgoI444wz+PHHH4s9Z/Xq1QwcOJCQkBDq1avH9ddfT2JiopveqdIpKSUiIiIiIiJSFexKqfBGZuntCwG1zLqanR9fXi4k7/HMV97xEz3HOnToEDNmzGDMmDEEBweXeozD4QDAy8uLN954gzVr1vDRRx/x+++/89BDDxUcN2bMGLKyspg3bx6rVq1i/PjxhISEALBnzx4GDRpE165dWbFiBe+88w4ffPABzz33XLne1qeffpphw4axcuVKBg0axLXXXsvhwyY5mpSUxHnnnUfHjh1ZsmQJv/76KwcOHGDYsGHlukZ5afieiIiIiIiISFVIKtLk3BZUx/STylCl1HEdPQATWnvm2veuhVoNXDp08+bNWJZFixYtim2vW7cumZlmqOaYMWMYP358iSbkzz33HLfddhtvv/02ADt37mTo0KG0a9cOgCZNmhQc//bbbxMbG8ubb76Jw+GgZcuW7N27l4cffpgnnngCLy/X6o1uuOEGrr76agBeeOEF3njjDRYtWsSFF17Im2++SceOHXnhhRcKjp80aRKxsbFs3LiR5s2bu3SN8lKllIiIiIiIiEhVOJI/fM+ulILiM/DJKWnRokUsX76cNm3akJWVBcCsWbM4//zzadCgAaGhoVx//fUcOnSooBH63XffzXPPPUfPnj158sknWblyZcH51q1bR48ePQqqrgB69uzJ0aNH2b17t8txtW/fvmA9ODiYsLAwEhISAFixYgWzZ88mJCSk4Ktly5YAbNmypeJvxgmoUkpERERERESkKtiVUrXjC7cpKXViIfVMxZKnru2iZs2a4XA42LBhQ7HtdpVTYGAgANu3b+eiiy7i9ttv5/nnnyciIoI//viD0aNHk52dTVBQEDfddBMDBgzg559/ZsaMGbz44ou88sor3HXXXW57ab6+vsUeOxwOnE4nAEePHmXIkCGMHz++xPOio6PdFsOxlJQSERERERERqQrJu8yyVmzhtsAIs1RS6vi8fVweQudJderU4YILLuDNN9/krrvuOm5fqaVLl+J0OnnllVcKhtp99dVXJY6LjY3ltttu47bbbmPs2LG8//773HXXXbRq1Ypvv/0Wy7IKqqX+/PNPQkNDadiwoVteS6dOnfj222+Jj4/Hx6f6UkUaviciIiIiIiLibrnZkJVi1kMiC7cH2Ukp9ZQ6Fbz99tvk5ubSpUsXvvzyS9atW8eGDRv45JNPWL9+Pd7e3jRr1oycnBz++9//snXrVqZMmcK7775b7Dz33HMP06dPZ9u2bfzzzz/Mnj2bVq1aAWaGv127dnHXXXexfv16fvjhB5588knuu+8+l/tJnciYMWM4fPgwV199NYsXL2bLli1Mnz6dUaNGkZeX55ZrlEZJKRERERERERF3yzhSuG5XR4GG751imjZtyrJly+jXrx9jx47lzDPPpEuXLvz3v//lgQce4Nlnn+XMM8/k1VdfZfz48bRt25ZPP/2UF198sdh58vLyGDNmDK1ateLCCy+kefPmBU3QGzRowLRp01i0aBFnnnkmt912G6NHj2bcuHFuex0xMTH8+eef5OXl0b9/f9q1a8c999xDeHi42xJfpXFYlmVV2dlPEikpKdSqVYvk5GTCwsI8HU6FOZ1OEhISiIqKqtKbRsSme06qk+43qU6636S66Z6T6qT7rZokrIe3u5n1h3dAYLhZX/oR/HQ31G0Ody72WHjV5UT3W2ZmJtu2baNx48YEBAR4IEKpqLK+d67mWfQvkIiIiIiIiIi72ZVSDi/wL/KhvKBSSsP3RJSUEhEREREREXE3OykVEA5FK4TspFTGYcif+UzkdKWklIiIiIiIiIi72UmpwNrFt9tJKcsJmUnVGpJITaOklIiIiIiIiIi7HTcpVaTpuYbwyWlOSSkRERERERERd8vITzgdm5QKCAccZl0z8MlpTkkpEREREREREXezK6WKVkYBePsUzsSnpJSc5pSUEhEREREREXG34w3fgyIz8CkpJac3JaVERERERERE3E1JKZETUlJKRERERERExN1cSUplqNG5nN6UlBIRERERERFxtzKTUvl9plQpdcpzOBx8//33lTpH3759ueeee9wST02jpJSIiIiIiIiIu2UkmWVpSalAOymlSqmT3cGDB7n99ttp1KgR/v7+1K9fnwEDBvDnn39W2TXj4+N57bXXquz81cnH0wGIiIiIiIiInFLyciArxayXNXwvLbH6YpIqMXToULKzs/noo49o0qQJBw4c4LfffuPQoZpfBZednY2fn59HY1CllIiIiIiIiIg72VVSUHpSKriuWWr43kktKSmJ+fPnM378eM4991zi4uI466yzGDt2LBdffHHBcYmJiVx22WUEBQVxxhln8OOPPxY7z+rVqxk4cCAhISHUq1eP66+/nsTE0hOWffv2ZceOHdx77704HA4cDkfBvj/++IPevXsTGBhIbGwsd999N2lpaQX74+PjefbZZxkxYgRhYWHccsstbn5Hyk9JKRERERERERF3svtJwXGSUpFmqUqpUuU6c9mftt8jX7nOXJfjDAkJISQkhO+//56srKzjHvf0008zbNgwVq5cyaBBg7j22ms5fNgM3UxKSuK8886jY8eOLFmyhF9//ZUDBw4wbNiwUs/13Xff0bBhQ5555hn27dvHvn37ANiyZQsXXnghQ4cOZeXKlXz55Zf88ccf3HnnncWe//LLL3PmmWeybNkyHn/8cZdfa1XR8D0RERERERERdypISjkgoFbJ/XalVFYy5GaBj3+1hXYySMxI5IJvLvDItWdeMZP6wfVdOtbHx4fJkydz88038+6779KpUyf69OnDVVddRfv27QuOu+GGG7j66qsBeOGFF3jjjTdYtGgRF154IW+++SYdO3bkhRdeKDh+0qRJxMbGsnHjRpo3b17smhEREXh7exMaGkr9+oVxvvjii1x77bUFDdHPOOMM3njjDfr06cM777xDQEAAAOeddx73339/hd6bqqBKKRERERERERF3spNSAbXAy7vkfrtSClQtdZIbOnQoe/fu5ccff+TCCy9kzpw5dOrUicmTJxccUzRBFRwcTFhYGAkJCQCsWLGC2bNnF1RdhYSE0LJlS8BUP7lqxYoVTJ48udh5BgwYgNPpZNu2bQXHdenSpZKv2L1UKSUiIiIiIiLiTnZSqrShewBBdQvX0w5CrQZVH9NJpG5gXWZeMdNj1y6vgIAALrjgAi644AIef/xxbrrpJp588kluuOEGAHx9fYsd73A4cDqdABw9epQhQ4Ywfvz4EueNjo52OYajR49y6623cvfdd5fY16hRo4L14OBgl89ZHZSUEhEREREREXGnDNMv6LhJKb8g8A2GnDRIV6XUsXy8fFweQlcTtW7dmu+//96lYzt16sS3335LfHw8Pj6upWj8/PzIy8srcZ61a9fSrFmz8obrURq+JyIiIiIiIuJOJ6qUgsK+Uhq+d9I6dOgQ5513Hp988gkrV65k27ZtfP3117z00ktccsklLp1jzJgxHD58mKuvvprFixezZcsWpk+fzqhRo0oknmzx8fHMmzePPXv2FMzS9/DDD7NgwQLuvPNOli9fzqZNm/jhhx9KNDqvaVQpJSIiIiIiIuJOdlIqKOL4xwRHQtIOM3xPTkohISF069aNCRMmsGXLFnJycoiNjeXmm2/m0UcfdekcMTEx/Pnnnzz88MP079+frKws4uLiuPDCC/HyKr2O6JlnnuHWW2+ladOmZGVlYVkW7du3Z+7cuTz22GP07t0by7Jo2rQpw4cPd+dLdjslpURERERERETcyaVKqfxm50pKnbT8/f158cUXefHFF497jGVZJbYlJSUVe3zGGWfw3XffHfccc+bMKfa4e/furFixosRxXbt2ZcaMGcc9z/bt24+7z1M0fE9ERERERETEnVxKStUxSw3fk9OYklIiIiIiIiIi7lSuSiklpeT0paSUiIiIiIiIiDtp+J6IS5SUEhEREREREXGndFVKibhCSSkRERERERERd8nLhaxks15mUqquWaYdhFKaYYucDpSUEhEREREREXGXzOTC9cCI4x8XlJ+Uys2A7LSqjekk4HQ6PR2ClJM7vmc+bohDRERERERERKCwnxS4NnwPID0R/EOqLqYazM/PDy8vL/bu3UtkZCR+fn44HA5PhyVlsCyL7OxsDh48iJeXF35+fhU+l5JSIiIiIiIiIu5SNCkVUOv4xwXVKVxPS4Ta8VUWUk3m5eVF48aN2bdvH3v37vV0OFIOQUFBNGrUCC+vig/CU1JKRERERERExF3spJR/LfAu4yO3jx8EhENm0mk/A5+fnx+NGjUiNzeXvLw8T4cjLvD29sbHx6fSVW1KSomIiIiIiIi4S8ZhswwMP/GxwZFKSuVzOBz4+vri6+vr6VCkGqnRuYiIiIiIiIi72JVSZfWTshXMwJdYdfGI1GBKSomIiIiIiIi4i5JSIi5TUkpERERERETEXeykVFDEiY+1Z+DT8D05TSkpJSIiIiIiIuIu5aqUUlJKTm9KSomIiIiIiIi4S4WSUhq+J6cnJaVERERERERE3KU8SamgOmaZrqSUnJ6UlBIRERERERFxl/TDZlne4XuWVXUxidRQSkqJiIiIiIiIVFTKPvjhTlg/zTyuyPA9Zy5kJlVJeCI1mY+nAxARERERERE5aS3/BJZNMV/d74DMZLO9PEkpMH2lXHmOyCnEo5VSL774Il27diU0NJSoqCguvfRSNmzYUOyYvn374nA4in3ddtttxY7ZuXMngwcPJigoiKioKB588EFyc3Or86WIiIiIiIjI6ehoQuH6X28D+cPwAiNO/NzA2uDI/1iuGfjkNOTRpNTcuXMZM2YMf/31FzNnziQnJ4f+/fuTlpZW7Libb76Zffv2FXy99NJLBfvy8vIYPHgw2dnZLFiwgI8++ojJkyfzxBNPVPfLERERERERkdNN+iGzDI8rTDCBa1VPXl6Fzc41A5+chjw6fO/XX38t9njy5MlERUWxdOlSzjnnnILtQUFB1K9fv9RzzJgxg7Vr1zJr1izq1atHhw4dePbZZ3n44Yd56qmn8PPzq9LXICIiIiIiIqcxu7F5++EQ3wu+vwPCYgqTTScSHGmqpFQpJaehGtXoPDnZjL2NiChe5vjpp59St25d2rZty9ixY0lPTy/Yt3DhQtq1a0e9evUKtg0YMICUlBTWrFlTPYGLiIiIiIjI6SkjPykVFAFN+sA9q+DG6aYKyhXBdc1SlVJyGqoxjc6dTif33HMPPXv2pG3btgXbr7nmGuLi4oiJiWHlypU8/PDDbNiwge+++w6A/fv3F0tIAQWP9+/fX+q1srKyyMrKKnickpJSEIPT6XTr66pOTqcTy7JO6tcgJxfdc1KddL9JddL9JtVN95xUJ91v7uVIP4wDcAbWhqLvqYvvryOoLg7ASkvAOgW/J7rfTk+ufr9rTFJqzJgxrF69mj/++KPY9ltuuaVgvV27dkRHR3P++eezZcsWmjZtWqFrvfjiizz99NMlth88eJDMzMwKnbMmcDqdJCcnY1kWXq5m5UUqQfecVCfdb1KddL9JddM9J9VJ95t7RaUfwgEkZXmRnZBwwuOPFeoIJhjIPLSb5GOe78g8QuDmn8lsciHOoLruCbia6X47PaWmprp0XI1ISt15551MnTqVefPm0bBhwzKP7datGwCbN2+madOm1K9fn0WLFhU75sCBAwDH7UM1duxY7rvvvoLHKSkpxMbGEhkZSVhYWGVeikc5nU4cDgeRkZH6YZdqoXtOqpPuN6lOut+kuumek+qk+82N8rLxyjHtZcKjm0BUVPnPUbcRAAF5R/E/5vmOWW/hWPAGoalbsC55q9LhVkjKHsjNhojGFXq67rfTU0BAgEvHeTQpZVkWd911F//73/+YM2cOjRuf+CZfvnw5ANHR0QD06NGD559/noSEBKLyf4BnzpxJWFgYrVu3LvUc/v7++Pv7l9ju5eV10v+QOByOU+J1yMlD95xUJ91vUp10v0l10z0n1Un3m5ukJRWsegXXcb2PVFEhkQA40hNxHPv8w1vNvq1zcDgc4HBUNNKKycmE98+DjCNww1Ro1L1Cp9H9dvpx9Xvt0TtizJgxfPLJJ3z22WeEhoayf/9+9u/fT0ZGBgBbtmzh2WefZenSpWzfvp0ff/yRESNGcM4559C+fXsA+vfvT+vWrbn++utZsWIF06dPZ9y4cYwZM6bUxJOIiIiIiIiIW9gz74FpdF4RwSYpVerse6n78pd74ci2ip2/Mg6shrQEcObAdzdDZnLVXk99p047Hk1KvfPOOyQnJ9O3b1+io6MLvr788ksA/Pz8mDVrFv3796dly5bcf//9DB06lJ9++qngHN7e3kydOhVvb2969OjBddddx4gRI3jmmWc89bJERERERETkdGDPvOflA/4VbAVjJ6XSD0NebvF9qUUm79r+Z8XOXxl7lxWuJ+2Enx+oumt9MxomtIYDa6vuGlLjeHz4XlliY2OZO3fuCc8TFxfHtGnT3BWWiIiIiIiIyInZlVKBtSs+tC7YbmBuQfohCM2fXd7pLJ6U2vEndLq+wqFWyN7lZhlSD44egFVfwRkXQPth7r1OXg6s+Q4sJ3x3C9z8O/j4ufcaUiNpQKeIiIiIiIhIRdiVUoEVHLoHEFpkgq6UPYXr6Ylg5RU+9mSl1Nl3QetLzfrP95uqKXdK2WMSUgAHVsGcF917fqmxlJQSERERERERqQi7Uqqi/aQA/IIhKL9aKnlX4faUvcWPS97p/mRQWbLT4eB6sx7TEYa8BmENICsFln/m3msl7Sr++M/XYOff7r2G1EhKSomIiIiIiIhURPohs6xMpRRAeKxZFk062UP3/MMKz1+d1VIHVhdWatVvb4YoNutnHh/a7N5r2a+7Viw06GKqpv53C2Qdde91pMZRUkpERERERESkIjKOmGVQ7cqdp5adlCpSMWTPvBcaDXFnm/Udf1TuOuVh95OqcwYE5Ddxj2hslofdPBOgXSFWOx4u+z/wCYQj22HR/7n3OlLjKCklIiIiIiIiUhHpbugpBRDeyCyLDt+zK6VC60N8L7NenZVSdj+pmA6F2yKamOURNyel7Eqp8EZQtxl0HW0eb/jFvdeRGkdJKREREREREZGKyHBDTylwoVKqp1k/sq1kr6mqsm+5WcZ0LNxWO79SKv0QZCa771pFk1IALQaa5e4lkJZYeJwzD35/DhZ/4L5ri0cpKSUiIiIiIiJSEW6rlMpPSiWX0lMqtD7UawMBtczjHQsqdy1XZKcVNjmP7lC43R6+B+4dwle0pxRAbLf812vBppmFx22YBvP+Az/f5/4hhOIRSkqJiIiIiIiIVERBpVSdyp3HTsZkJkNmilkvWinl5Q2N8vtKba+GvlL7V5tm4zggun3hdv/QwpkC3TWEz5kHKXvMul0p5e0LTc836xt/LTy26Kx/q75xz/XFo5SUEhERERERESkvp7NIo3M3VUpBYV+popVSAPH5Q/h2VENfKbufVN0zTCKqKHc3O0/dD85cs170fWh+oVlu+R3ycuBoAmycXrh/5ZdgWe6JQTxGSSkRERERERGR8spKzq8movLD9wLCwT9/hrukXSYJk3bQPA6LMUt7GN2hLVWfjCmtn5TN7ivlrkope+iewwvCGhRub9YPcEBWCuxcCCu/AisP/ELM/kObCuOUk5aSUiIiIiIiIiLlZfeTgspXSjkchUP4knfB0QNAfuLJrpSyr2HlQVZq5a53InalVNF+UjZ3V0rZlWGhMWbYni24DsSeZdY3Tofln5r1TiOhfv6QwpVfuycG8RglpURERERERETKyx66BxBYu/Lns4euJe0sHLoHEFLPLAPCC7dlJlX+eseTdRQSN5r1Miultrvnekk7zNLuJ1VU8wFm+c8USFhr1jtcA+2Hm/XV35ieVHLSUlJKREREREREpLzsSin/sOIVPhVVtFLKbnIeVAd8/M160cRX0YSYu+1ZUtjkvH67kvsjmuTHuRtysyp/vaT8Sqmi/aRsZ+QnpbKSzbJ+e6jfFtoONfEdPQDb5lY+BvEYJaVEREREREREyiv9kFm6o0oKSq+UCo0u3O8bCN5+Zj0jyT3XPFbCevjuFrMe0wH8Q0oeYw/fw4IjOyp/TbunVK1SklL12kBYw8LHHa41y7BoaHyOWV/5VeVjEI9RUkpERERERESkvDLyK6Uq20/KZidlkopUStn9pMD0nbITYFVRKXVgLUwebKqPgurAxW+WflxwJPgGm3V3NDu3e0qVNnzP4YDm/c26ly+0u7Jwnz2Eb91PkJ1e+TjEI5SUEhERERERESkve/heZWfes9lJmbSEwn5NRZNSUNhXyt09pRLWw0cXQXoiBEfBDT+bYXKlcTjc1+zc6Sx7+B5Al9HmPe5+u2l+bms1BHwCIPsobJ1TuTjEY3w8HYCIiIiIiIjIScfdlVJFK4V2LzbLosP3oOoqpaY9YIYjhtSHkT9BZPOyj68dDwdWV75SKu0g5OX3pQqPK/2Y+m3h4VKuExBmhvftWQqHNlUuDvEYVUqJiIiIiIiIlJe7K6WCI03lDxT2WTq2Uiow3Czd2VNq23zYPt+sX/HBiRNS4L5KKft1AoQ1KP/za8ebpbtmApRqp6SUiIiIiIiISHm5u1LK4YBaDYtvq+pKKcuCOS+a9cbnQHwv155XOz8pVdlKqeT8pFRIffANKP/z7eoqdzRcF4+ocFIqJyeHXbt2sWHDBg4fPuzOmERERERERERqtvT8xJC7KqWg5Ax0xyalKtNTyumEL66FKZcVzu63fT7s+NOs933U9XPZlVJHtoMzr/yx2OxKqeP1kzoRVUqd9MqVlEpNTeWdd96hT58+hIWFER8fT6tWrYiMjCQuLo6bb76ZxYsXV1WsIiIiIiIiIjWDuyuloGRyxp2VUgfXwfqpsOV3mHgBHNwAs/OrpJr0hbgerp8roolZ5mVDyt7yx2JLKmPmPVfYSamknZVLjonHuJyUevXVV4mPj+fDDz+kX79+fP/99yxfvpyNGzeycOFCnnzySXJzc+nfvz8XXnghmzap0ZiIiIiIiIicotKrIClVq0hyxuFl+kwVVZmeUgc3FK4n74T3zoWdC8zjvmPLd66whuCVP29aZYbw2ZVSx1aIuap2/vA9Z07lkmPiMS7Pvrd48WLmzZtHmzZtSt1/1llnceONN/Luu+/y4YcfMn/+fM444wy3BSoiIiIiIiJSI+RkQG6GWXfn8L2ilVLBUeB9zEf2gkqppPKfO3GjWdZpBtnpkJqfxGlyLjTqXr5zefuY6qbDW02z88bnlD8egORKVkqFNQSHN1h5kLSj4sMAxWNcTkp9/vnnLh3n7+/PbbfdVuGARERERERERGq09CJ9ld1aKVUkqXLszHtQmJSqSE8pu1Iqvhec8yB8NhwSN8H5j5f/XGCanR/eWvFKKcsq0lOqgkkpbx+TiDqy3Xy52qhdagyXk1KlycrKAkwiSkREREREROS0kFEkKeXWSqkiyZlj+0lBYaPzrBTIyy1ZSVUWu1Kqbgszy9+t8yArtXBIYHlFNIYtwKHNFXt++mHISTfrFU1KgekrZSelKuPgBpjzbzMk8PwnzWyIUuXKPfvezJkzGTRoELVr1yYoKIigoCBq167NoEGDmDVrVlXEKCIiIiIiIlJzpB8yS28/8At233lDo81wNCi7UgogM9n18zrzTFUUQGRzs/TyrnhCCiCmo1nuWGBm9iuv5J2F67UaVjyO8Py+Ukd2VOz5OZkw+wV4pyes+Q7+mADb5lY8HimXciWlPvroIwYNGkStWrWYMGECU6dOZerUqUyYMIHw8HAGDRrElClTqipWEREREREREc+zh+8FRri3osbbB8IamPXSKqWKJpHKMwNf0g7IMyOdqNuiwuEV0+Rcs0w/BPuWl//5CevMMjS6cok9ewa+ilRKJe+Bd3vB3PGmWbojP0Uy9z8Vj0fKpVzD955//nlee+01xowZU2LfDTfcQK9evXjmmWe4/vrr3RagiIiIiIiISI2SUQUz79ka94bln5befNwevgfl6yt1MH/onm9w5aqSiqrVACJbwcF1sOU3aNCpfM/ft8Iso8+sXByVSUr9+Toc2mQq3nrfb97zjy+BHX+YCrC4sysXm5xQuSqldu7cSb9+/Y67//zzz2f37t2VDkpERERERESkxkrLH74XVMf95774Tbh/IzTpU3Kftw/4hZr18lRKJeY3Oa97hnsru5qdb5ZbZpf/uW5LSuUP30tLgOy08j132zyz7PuI+WrSF+Lym6XPfalycYlLypWUatOmDR988MFx90+aNInWrVtXOigRERERERGRGuvwFrO0EyLu5OUFofWOv9/uK5WR5Po57UqpSDcN3bM1Pc8sd/0NmSmuP8/phH0rzXqlk1KNC9eTdh7/uGMdTTBVXgCN+xZu7/OgWW6dDbuXVC42OaFyDd975ZVXuOiii/j111/p168f9eqZH5QDBw7w22+/sXXrVn7++ecqCVRERERERESkRrBnnKvTrPqvHVgLkqlgpVRz98YSdzb4BEBuJmyfDy0Hu/a8w1sgJ7+qqbJJqcDa4B9mZiQ8sh2iWrn2vO3zzdIvtHgMjftAw7Ng9yJTLXXtV5WLT8pUrkqpvn37snr1agYOHMjSpUuZNGkSkyZNYunSpQwcOJBVq1ZxzjnnVFWsIiIiIiIiIp5lWZBoJ6XOqP7r25VSrvaUsqyqq5TyDSzsu7T5N9efZw/dC6pT2Ni9ohyOIjPwbXf9edvyk1JxZ5thkUXP1/t+s75pBmSnVy4+KVO5KqUA4uPjGT9+fFXEIiIiIiIiIlKzpR2ErGSzXtcDSSm72bmrlVJHDxSJ181JKYCm58OW302zc1fZs/VFn+meHle14+DAKjiyw/Xn2JVSjXuX3BfTIX/FgqP7IaJJZSOU4yh3UgogNzeXNWvWsH//fgCio6Np1aoVvr6+bg1OREREREREpEaxh+45vApnfqtOBT2lXExKHcwfuuflAxGNyz62IpqdDzMeM1VKh7e6lsBxV5NzW3ln4EvZV/h9jC8lKRUcab6/ltP0nlJSqsqUa/ie0+lk3LhxREZG0rFjRwYOHMjAgQPp0KEDUVFRPP744zidzqqKVURERERERMSzEjeZZXgc+PhX//UDw83S1UbniflD9yKagncVFJJEtoTQGLPuyhA+y/J8UsqukgoIh/rtSu738oagumY9dX8lg5OylCsp9cgjj/Dee+/x73//m61bt5KWlkZaWhpbt25l/PjxvPfee4wdO7aqYhURERERERHxLE82OYcKVEqtN8tINzc5tzkchbPwbfn9xMcn7YDM/OGE7k5KJe0wSa8T2TbPLON7mQRUaewZEI8eqHR4cnzlSkp9/PHHTJkyhVtvvZX4+HgCAwMJDAwkPj6eW265hY8//pjJkydXUagiIiIiIiIiHmYnpTzRTwoKe0q52ujcHr5XFf2kbM3yk1Lb5kFebtnH2lVS/rWgtpuGE9pJqZx00/PrROxKqdKG7tlC6pulklJVqlw9pVJTU4mJiTnu/ujoaNLS0iodlIiIiIiIiEiNVFAp1dQz1y9vpVRiFc28V1T8OWaZfRQS1kJ0++MfWzB0r717mpwD1IoFHIBlhvA5vOCPCZC0E7LTTLIqvBGcN84cbw/zK63JuS0kv1IqVUmpqlSupFTfvn154IEH+PTTT6lbt26xfYmJiTz88MP07dvXnfGJiIiIiIiI1Ax5uXB4m1mv46FKqaI9pSyr7MRORlJhpU/dKhq+BxASaXpsJe2A3YtdTEq5aegegG8AhEZD6l5Y+RWsnwqp+4ofs3MhrJsKTc81j4PqQGSr45+zYPieekpVpXIlpd59910GDRpEdHQ07dq1o1498006cOAAq1atonXr1kydOrVKAhURERERERHxqKQd4Mwx657uKZWXBTkZ4Bd0/GPtKimo+uGGDbvmJ6WWQNfRpR9jWbB3uVl3Z1IKzBC+1L2w+H3zOKAWnHkN+IeCtx8snQwpu03CCvL7SZXR0ShEPaWqQ7mSUrGxsaxYsYLp06fz119/sX+/yRieddZZvPDCC/Tv3x+vsr6pIiIiIiIiIicre+iebzCEHb+1TZWye0qB6SvlFwSbZsGSD6Dv2OJVSltmm2WtRuAXXLVxNewKq78xlVLHk7oP0hPNelUkpXYuMOsNusAVk6B2XOH+brfCrKfM+wTQ5Nyyz6fhe9WiXEkpAC8vLwYOHMjAgQOrIh4RERERERGRmqloPyl39UMqL7tSCkxfqbAY+O0p2L8KdiyAET9ATAfTdHzueHNcqyFVH1fDrmZ5aBOkH4agiJLH2FVSvkHurzRrNxS2zoZ2V8J5j4OPX/H9AWFw0avQfjjsWQodri37fKH5jc7TDpphm97lTp+IC9xa1pSWlsa8efPceUoRERERERGRmiFxk1l6augemOFoDm+znpEEWalwYI15nJkEH18Ma3+Er28AKw8adIbzn6j6uOq3A29/s77nn9KP2fGnWcZ0Ai9v916/WT+4fz30f7ZkQqqoRt2gxx1lHwMQEpW/YhVWd4nbuTUptXnzZs499wQlcCIiIiIiIiInI7tSqqr7M5XF4SjS7PyIqfqxnCZRFdYQMpPhq+sh/RAER8HwT0wj8Krm41c4JO94Q/i2zjHLJn2rPp7KsofvAaSq2XlVUQMoEREREREREVcUDN/zYKUUFPaVykyCXfkJoOj2cMNUqBVrHnv5wLCPq7f3lT2Er7SkVNpBOLDarDfpU30xVZRfMPiFmnU1O68y5RoUGRFRypjQIvLy8ioVjIiIiIiIiEiNlHXUNOoGzyel7L5SGUdg199mveFZENHYJKbmvQwtL4K4HtUbV8MuZrlnCTidxWe325bf6scv1AzfOxmE1oNDqUpKVaFyJaWysrK4/fbbadeuXan7d+zYwdNPP+2WwERERERERERqDLtKCmpAUircLNMPw+5FZj32LLOsHQ+XvOmJqAqTUpnJ5v2KbF6wy2EnpeJ7nTxNw0Pqm9ehGfiqTLnuhA4dOhAbG8vIkSNL3b9ixQolpUREREREROTUYyelQuqZmdw8ya6U2rXIJIAAYrt5Lh5brVjz/hw9YKqliiSl2DbHLE+GflI2u9m5KqWqTLl6Sg0ePJikpKTj7o+IiGDEiBGVjUlERERERESkZinoJ+XBJuc2u6fUzoVmGRoDtRp6LJwCDkepfaW8U3bhSNppHpwM/aRsofXN8qganVeVclVKPfroo2Xuj42N5cMPP6xUQCIiIiIiIiI1TuIms6zT1LNxQGGllJXf1zn2LJMQqgkadoH1U4slpfx2LzArIfUgsqWHAqsAewY+Dd+rMpp9T0RERERERE5fudmw829IWA85Gcc/7uAGs6xbAyql7J5SNrufVE1gV0odWGOawwN+e/Iruhr3qTnJM1fYSSkN36syFU5K7d69G6fTWWJdRERERERE5KQx5wWY1B/e7gbP14dXWsKfbxQ/JusoJKwx6zEdqz/GY9mVUraa0E/KFtMRvHzBcsLXN0BWKv57/jL7TqZ+UmBm3wOTlLIsz8ZyiqpwUqp169Zs3769xLqIiIiIiIjISWPTzOKPU/fB7BdMBZVtzxKTZHF4Q0yn6o2vNHZPKQBvf6jf3mOhlOAXDP2fM+ubZ+J4/1y8Mo+YxydTPykorJTKzSxsKC9uVeGklFUkS2gpYygiIiIiIiInm+w0SFhr1od/CjdMAxyQmwF7/yk8btcis4xuD35B1R5mCUUrpRp0Ah8/z8VSmu63waXvgsMbx+EtAFgRTWtGM/byCKlfuH40oeLnObIDvrsVdiysfEynGPWUEhERERERkdPT3uX5FVBe0PRciO8J9duafdvnFx6362+zjO1e7SGWqmhPqZrUT6qoDlfDNV9i+eYn8U62oXtgkn9evma9ojPwWRb8eCes/AJmPem+2E4RSkqJiIiIiIjI6WnPUrOMam2GnQHE9zbL7X+YpdMJu/JnkqspCaCilVINa0hMpTnjAqwbfuHomaOxej/g6WjKz8sLQqLMekVn4Ns0E7bNM+t7lxcfFipKSomIiIiIiMhpyk5KNSjSJyqup1nu/NskEA6uh6z8fkI1paF4cKRJpIXUh/heno6mbNHtOdrjIQitf+Jja6LKzMCXlwszHy/yOAsOrHZPXKcIH08HICIiIiIiIuIRe/L7RjXoXLgt7mwK+0otK5x1r1Ys1GpQ7SGWyssbbplr1mtaP6lTjZ1Mq8jwvWVTTFLT4Q0BtSDjsEmENqgBzfJrCFVKiYiIiIiIyOnnaAIk7zTrDboUbg+KgHpF+krZTc5rytA9m4+fElLVoaLD97KOmlkcATqNgGbnm3W7Ok8AJaVERERERETkdGQnB3yDILJl8X3x+UP4tv8BO/8y6zWlyblUL3sGPnv43vqf4b2+sPgD08T8eP58DdISwC8E+o4tTHzuXlKV0Z50KpyUevTRR4mIiCixLiIiIiIiIlLj2Ump6A7gfUxnG7tP044FcGSbWa9plVJSPexKqaMHIP0wfH+HGdb5833wyVBI2VvyOTsWwvxXzXrPf0FovcIhooc2QcaR6on9JFDhpNTYsWMJDw8vsS4iIiIiIiJS49kVK6X197GbnedlmaVvcOGQPjm9hBaplJr3MmQmgXf+sMktv8HbPWDVN4XHHz0I34wCK88kPHv+y2yv3w68fM263ctMNHxPRERERERETjNOJ+zNTww07FJyf9G+UgANO5esppLTgz18L+MILHrPrF/wDFz9JQRHmSTVt6Ph61GQlgjf3QSp+0xj82EfgY+/eY5vgElMQfGkVG425GRW28upacqdlFqxYgXPPfccb7/9NomJicX2paSkcOONN7otOBERERERERG3O7wVMpPNetGZ94qyq6UAYrtVfUxSM9nD9wCcOVCnGXS9CVpcCHf8Ba0uNvvWfAcT2sLWOebxZf8HteOLn8tOgO7Jr9LLyYQPLoCXz4DUCszudwooV1JqxowZnHXWWXzxxReMHz+eli1bMnv27IL9GRkZfPTRRy6f78UXX6Rr166EhoYSFRXFpZdeyoYNG4odk5mZyZgxY6hTpw4hISEMHTqUAweKd73fuXMngwcPJigoiKioKB588EFyc3PL89JERERERETkdGH3kwqOhFqxpR9j95UCJaVOZ0WTUgAXPAve+cPwguvAsI/h8vfBvxbkZpjtPe+BFgNLnqtos3PLgj8mwL7lkJVi+pedhsqVlHrqqad44IEHWL16Ndu3b+ehhx7i4osv5tdff63QxefOncuYMWP466+/mDlzJjk5OfTv35+0tLSCY+69915++uknvv76a+bOncvevXu5/PLLC/bn5eUxePBgsrOzWbBgAR999BGTJ0/miSeeqFBMIiIiIiIicopJ2gXz/gMzn4Alk2Ddj2Z7gy7gcJT+nMa9IaguhNRTUup05uMPgbXNenzvkskmhwPaD4M7FkD7q6DLaDjv8dLPZVdKpSfClt/hj1cL9x3a4v7YTwLlGhS7Zs0apkyZAoDD4eChhx6iYcOGXHHFFXzxxRd07dq1XBc/Npk1efJkoqKiWLp0Keeccw7Jycl88MEHfPbZZ5x33nkAfPjhh7Rq1Yq//vqL7t27M2PGDNauXcusWbOoV68eHTp04Nlnn+Xhhx/mqaeews/Pr1wxiYiIiIiIyCli0yxY/D5smgGWs+T+4w3dA5OIuH2BSToEhFVdjFLzdbjWJDIH/ef4ScxaDeHy/yv7PBFNICDc9KH65kbIyy7cd2izu6I9qZQrKeXv709SUlKxbddccw1eXl4MHz6cV155pVLBJCebMb0REREALF26lJycHPr161dwTMuWLWnUqBELFy6ke/fuLFy4kHbt2lGvXr2CYwYMGMDtt9/OmjVr6NixY4nrZGVlkZWVVfA4JSUFAKfTidNZyj9UJwmn04llWSf1a5CTi+45qU6636Q66X6T6qZ7TqrTaXO/rfoar//dUvDQqtUQIlvCkR2QtAO8/bFaXmSanh9PcKRZnurvVRU6Je63C541X1Dpe8HRoDOOLb+ZxBRgNTkPx9bfsRI3YZ3M79ExXP1+lysp1aFDB2bPnk3nzsWzyVdddRWWZTFy5MjynK4Yp9PJPffcQ8+ePWnb1sxysH//fvz8/AgPDy92bL169di/f3/BMUUTUvZ+e19pXnzxRZ5++ukS2w8ePEhm5snb9d7pdJKcnIxlWXh5aWJFqXq656Q66X6T6qT7Taqb7jmpTqfL/Rbx10T8gJzIthztchdZsb3By9vsdOYBFlg+kJDgyTBPeafL/eaqkPCWhPAbABnNBpPZdBC185NSCQcOHL8S6ySTmprq0nHlSkrdfvvtzJs3r9R9V199NZZl8f7775fnlAXGjBnD6tWr+eOPPyr0/PIYO3Ys9913X8HjlJQUYmNjiYyMJCzs5C3LdDqdOBwOIiMj9cMu1UL3nFQn3W9SnXS/eUhWCo7fn8WK7104m9FpQvecVKfT4n5LS8Sx38xw5t3/GWo1PdfDAZ2+Tov7rTzaDISlb2H5h+I/5GX88yumvLJTiArxhuC6no3PTQICAlw6rlxJqcsuu4zLLrvsuPuvueYarrnmmvKcEoA777yTqVOnMm/ePBo2bFiwvX79+mRnZ5OUlFSsWurAgQPUr1+/4JhFixYVO589O599zLH8/f3x9/cvsd3Ly+uk/yFxOBynxOuQk4fuOalOut+kOul+84DZz8PiiTj+mQJjzoSIxp6OqFrpnpPqdMrfb5vze0gF1MKryTlwqr7Ok8Qpf7+VR5PeMPxTHHWa4qgVY2bwwwFYeB3ZCqFRJzrDScHV73WF7ojc3FxWrFjB9OnTmT59OitWrCAnJ6fc57EsizvvvJP//e9//P777zRuXPwXj86dO+Pr68tvv/1WsG3Dhg3s3LmTHj16ANCjRw9WrVpFQpGSy5kzZxIWFkbr1q0r8vJEREREpLod3ACLPzDreVkw8zgzF4mIuGLdVLNsfiF4+3o2FpFjtboIolqZdR9/CG9k1k/DZuflSko5nU7GjRtHZGQkHTt2ZODAgQwcOJCOHTsSFRXF448/Xq7mZWPGjOGTTz7hs88+IzQ0lP3797N//34yMjIAqFWrFqNHj+a+++5j9uzZLF26lFGjRtGjRw+6d+8OQP/+/WndujXXX399QaJs3LhxjBkzptRqKBEREfatgB/uhMRNno5ERGwzxoGVZ2YlAlj3E2yd69GQ5BSVtBOyXOt1IieprKOw5Xez3nKwZ2MRcUXdM8xSSamyPfLII7z33nv8+9//ZuvWraSlpZGWlsbWrVsZP3487733HmPHjnX5fO+88w7Jycn07duX6Ojogq8vv/yy4JgJEyZw0UUXMXToUM455xzq16/Pd999V7Df29ubqVOn4u3tTY8ePbjuuusYMWIEzzzzTHlemoiInC4OboSPL4FlU2D+q56ORkQANs8y07UDDJ0Ijc8x67+Ohbxcz8Ulp57dS+H1DvDhoPxG13JK2vKbqbj0CYBm/U58vIin1WlmlqdhUqpcPaU+/vhjpkyZwoABA4ptj4+P55ZbbiEuLo4RI0Ywfvx4l85nWdYJjwkICOCtt97irbfeOu4xcXFxTJs2zaVriojIaSxlH3xyOWQcMY8PrPZsPCJikk7THzPrTc+HMy6AsBh4txckrIF/JkPXmzwaopxCln1sKvL2r4S130PboZ6OSKqCPXSv6XngF+zZWERcUZCU2uLZODygXJVSqampxMTEHHd/dHQ0aWlplQ5KRETE7TKT4dMrIXkXppkkkLhRfykX8STLgnkvwcH14PCGAc+b7fXaQJcbzfrvz0G2fr8UN8jLgbU/Fj6e9wqUo/WInCTycmDjdLOuoXtysqjT1CwPbTnt/l0qV1Kqb9++PPDAAyQmJpbYl5iYyMMPP0zfvn3dFZuIiIj7/Hw/HFgF3n5w2f+ZbbmZcGS7R8MSOW1lHIEvr4O5+RX2XW4sbPoKcO5jZuhNxhHY8ItnYpRTy7a5kHEYHPkfgRLWwMZfPRuTuN/2+ZCVbL7PzQd6OhoR19iVUnlZkLLbs7FUs3Ilpd5991327t1LdHQ0nTp1Kmh03qlTJ6Kjo9m7dy/vvPNOVcUqIiJSMc68wg+1F/4b2g8D/zDzOGGd5+ISOV3tXQb/dw6szx9i0/kG6P9c8WOCIsysWQArv6rW8OQUtfp/ZhnfG1peZNbnv2wq9sA01p8z3lTWyslr5ddmGdcTgut4NhYRV4U1BO/8idpOs75S5UpKxcbGsmLFCn788UeGDBlCo0aNaNSoEUOGDOGnn35i2bJlxMbGVlWsIiIiFZO4EbKPmvUWA8HhKKzIUFJKKsqy4ItrTe8ju0+ZnFhWKnw23MyA5hcCQz+AIa+Db0DJY9sPN8stv0FayUp9EZflZsP6n8x628uh9/1mfc9SUy318wPw8cUw5wX47pbCRJWcXDbNhBWfmfV2V3o2FpHy8PIqPoTvNFKuRucAXl5eBRVSIiIiJ4U9/5hlaLRpoAwmKbXrb0hY67m45OS26+/CSp8130OXUR4N56Tx5xtw9IBJSN0yp3Aa7NI06weBtU3Sb83/4Kybqy1MOcVs+d1UQHn5QKuLTSVe0/PM9s+vBookoTb+Cn+9DT3GeCxcqYCjB+H7O8x64z7Q8XrPxiNSXnWamt9LEzd5OpJqVe6kFMCiRYtYuHAh+/fvB6B+/fqcffbZdO3a1a3BiYiIuMWepWYZ06lwW2R+pdTB9dUfj5waln9auL7+55M3KZV11PRu8q7Qr4Xlk7IXFvzXrPe6p+yEFICPH7S5DJZMMkP4lJSSilrznVk26WsSUgC9HzBJKSwzbKbfU3Bok7nfZj4JjXpAg07HOaHUKJYFP9wBaQkmkX3Zu6byRORkUjAD3+k1fK9cv30kJCQwdOhQ/vzzTxo1akS9evUAOHDgAPfeey89e/bk22+/JSoqqkqCFRERqZC9+ZVSRT9c2MP3EjeZmXq8fas/Lim/Q1sgdR/E9/JsHNnphf1pwDRQzkyBgDDPxVQRe5bClMvBPxSu+Qrqta7a681+HnIzIDQGurtYhdJumEkS7F4Eh7dBROOqjVFOPTmZsH6aWW87tHB7fE/oeQ8c3ADnP2Hu/5xM2LUIDqyGb0bBrfMgoJZHwpZyWPQ+bJph1i9+s7AqWuRkcpompcqVPr7jjjvIy8tj3bp1bN++nb///pu///6b7du3s27dOpxOJ2PGqMxVRERqkNws2L/arJeWlHLmnHZj909aRw/C++fB5MGw82/PxrJ+KmSnmgoj3yDIyy78QHSySNoJn10FmUmQvAs+vBB2LKi66+1fDcvyq8vOGwd+Qa49L7YbhDcy66u+rprY5NS2eab5efX2g5aDi++74Gm45ovChKxvAFzxofm5PrIdvh4F2WnVHrKUQ2YyzHrSrHe+AVpd5NFwRMorx5nD4czD7A6qxUZfX5Zn7OdAyi5Ph1VtypWUmj59Om+99RYtWrQosa9Fixa88cYb/PqrplUVEZEaZP9qk3gCiOlYuD04EoLyZ+VRX6mTw+znTAIFYPW3Hg2lYOheqyHQ7Hyzvv5nz8VTXpnJ8OkwM9QlqI6Z9SczGT6+FNZNrZprznwcsKBeOzjzKtef5+VV2LB45VdqQC3lt3WOWTbp61rVU2RzuGiCWd/yG3x0MaQfrqroClmW+fnbt7Lqr3UqWfsj5KSDXyj0f97T0Yic0NpDa7lvzn1c+v2l9P6iN52mdKLPl30Y+NejDG0YzfUx9Zi+7ktPh1ltypWU8vf3JyUl5bj7U1NT8ff3r3RQIiIibmMP3YtoavpM2ByOwr5SmoGv5tu3ApZ+VPh4/c+eS04k7TJTxwN0uAZaDjHrm2aayrzy2DQL3jsXNvzi3hjLkpcDX98AB9eZPjpXfwGjZ5ifh7ws+PJamHgBLJ7ovg/iu5fm9+4B+j8DXt7le367YWZ5aFP1vldyajiwxiwbdHb9OWdeBZe8DQ5v2LMEPuhvqgurimXBLw+Zn79PLgens+qudapZmf/hvfUl4B/i2VhEyrA/bT+P/fEYV029ipk7ZrIleQtJWUmlHpt9dH/1BudB5eopNXz4cEaOHMmECRM4//zzCQszfRNSUlL47bffuO+++7j66qurJFAREZEKsZucl/ZhJKoV7PjDfDiXmsuy4JdHAAtqN4Yj2yBlN+xbXrz6rbqs/MLEEtbAzPCUlWJm9MpONcmq5v1dO0/WUfhhDBzdD19eD8M/gRYXVmnoACz5sDBBdNm7EHuWWb/xFxPH9vmmf9PuReZ9v+QtOHN45a654nOzjOloZjwrr6iW0LAr7F4MX1wDve6Fcx9VLzg5McsqTErVa1u+53a8FoLrwlcjTUJ08mAYs9gM8XMnpxN+vg+Wfmgepx00vfNqNXDvdU5FSbvMv1lQ+X+nRCrBsix2pu5kwd4F7Du6j/TcdDJyM0jJTuFwxmEOZR7iQPoBcp25ALSKaMXlZ1xOZGAkdQLrEO4fTrBvMEGfXknAnmV4N23o4VdUfcqVlHr11VdxOp1cddVV5Obm4ufnB0B2djY+Pj6MHj2al19+uUoCFRERqZA9pTQ5t0WpUuqksOZ/sDO/19Glb8O0B00T4nVTqz8pZVmw/DOzfuZVpuInsLZpvL51juk15WpS6s/XTUIKzBDTr66Hqz+HZv2qJHRzHSf8/a5Z73oTtL28cF9gbRj5E+z6G1Z8Yd73zCTzQbkyH/Zys2H1N2b9zEr88fLKyfDNaNj1F/zxKmybB8M+glqnzy/uUgFJO0ziGKB+OZNSAM0HwMgf4cNBplJq0wxofbH74nM64ae7YdmU4tsPb1FSyhWrvjLLsAYQ5+EJMOSU4rScJKQnkJ2XTZBvEIE+gfh7++PAAUBGbgabkzaz8chG1h1ex197/2L30d0nPG+9oHr8q9O/GNxkMF6OUgauNRsAtZtC3ebufkk1VrmSUv7+/rzzzjuMHz+epUuXsn+/+UWqfv36dO7cuaBySkREpEbITIHEjWY9poyk1OGtZsYld//1WyovNwtmPmHW21wOcWdDy4tMUmr9VDj/8eqNZ/dic78AnHlN4faWF5mk1IZp4Jxw4uFpybthwX/Neo87zex9+1fBF9fCtd9A497li8uZZ+71A2ugfnvTE6c0W343H3YBut9Rcr/DAY26m6/mF8Lnw2HvMpNY8vErX0y2zTMh44ipJis681l51WoIN/wM81+GuePNkKqfHzBNqkWOx57owi8UajWq2Dliz4Iz+sOGn00SxJ1JqdXfFCakzr7b/BtyaLP5anyO+65zKrIsWJE/dK/dlab/nEg5ZeRmsDpxNduSt7EzZSc7U3eyM2Unu1J3ke3MLvf56gXVo1WdVgT7BhPoE0iIbwgRARHUCaxD3cC6dIrqRIBPGb9vnju2Eq/m5FSupJQtLCyMc889192xiIiIuNe+5YBleoJEty+5P7KlWVpO84G+tGPEs3YvNjPDObzggmfMtlYXwdx/w8H1kLgZ6jarvng2zzLL6DOLX7flYJj2gBl2s2sRxPUo+zyznobcDNNg/LxxkH2fGRp0cB3MeMxMQ++KnAwzO9i2uabRL5jG5fesAr/gkscv+j+zbNYP6jQt+9wNu5plbiYcWFW+fjxFrfii8JrBdSt2Dpu3D/R9BMLj4PvbTJItO931mfzk9HMgPylVr03lkhbth5mk1MbpJslatEdhZdhDzJuca/6NS9yYn5TSrLAntG85JG4w6+WZPEFOazl5Oaw9vJYl+5ewcO9CliUsq1DyCcCBg9jQWM6ofQYdozrSq0EvmtRqgsPhcHPUp7ZyJ6UyMjJYunQpERERtG7duti+zMxMvvrqK0aMGOG2AEVERCrM/mW/XmvwDSy5PygCQuqbIVQJ66B+O9Mw9egB8xdr/VLheftWmGXdFhAea9brtYXwRmYozfqfTH+h6mLP4nVsX6SwGGjQxVTvrP627KTU7iWFQ04ueNrcm76BMOg/8NFFprLD1UTL1jmw8ZjG3+mHzH3c5cbi2w9tMc3YAc669cTnDq4DdZqZD8i7FlcsKZVxBDbmz8zc3o39XlpdBD/6msbs2+ebIVYipdm/yiwrMnSvqOYXgn+YGQq49kfoPLLysQEc2WGW9dqY/3Mi8pPFSkqdmF0lVb99YeWzyDGSs5JZnrCcZQnLWJawjDWH1pCVV3xSEh+HD7FhsTQKbUSjsEYFy9jQWIJ8ggr6Q2UVmczEx8uHuLA4gnz1R5HKKldSauPGjfTv35+dO3ficDjo1asXn3/+OTExMQAkJyczatQoJaVERKRmKOgnVcaH6ahWJim14w9Y813hB+i4ntCwS9XHKGWzk1LRZxZuczjMjHd/vWX6SlVXUiozxSSUwEwtf6wzrzJJqVVfw4DnwaeUGYktC6Y/ZtYbdi0+nC2mA+AAKw/2rzRD6E7EHp4a2Qqu/58Z1rb0Q/j7Peg8qnhidfFEwIKIJq73rWp4lklK7V4E3Obac4pa8z3kZZsP8y0Glv/5x+MfahJ/2+aZRJuSUnI8BZVSlUxK+QaYYXvLPjE/425LSm03y9rxZmlXMB5WUqpMeblFetWpSkpKmrtrLv9d9l82HNlQ6v6mtZrSPaY7PaJ70KV+F4J9S6kuzleHOlUVpgDlqmF9+OGHadu2LQkJCWzYsIHQ0FB69erFzp1VOD2qiIhIRe1dZpal9ZOy2X9d/efjwoQUmJ4/4nl2UiqmQ/HtrS4yyz1LIGVv9cSy40+TMPIJgNhSEkZth4K3n2kOvuGXkvvtc+z6y6wPeKF40sg/FCJbmHU7oXoiiZvMsn5bCIuGs24xjw+uK5yRCsxMf8s+Metdb3Z9GFNs/hC+XYtdO/5Y9lTtbS4tvVqxMppdYJabZ5pkn8ixMlMKkz7121X+fO2GmeX2+e75P8KyTCN2MENSoUhSaptJvEjpFk80w6UdXtD2Ck9HIzXI3qN7ufv3u7nz9zsLElL+3v50rteZ0W1H8+Z5bzJ/+Hy+v/R7HjnrEfrE9ikzISVVr1yVUgsWLGDWrFnUrVuXunXr8tNPP3HHHXfQu3dvZs+eTXCwvpkiIlJDHD1oehHBiSulbAHh5hfcjMPml13xrOy0wkqgopVSALHdIKgupCfCzCeh31NVP1OVPXSvUffSm+IHRZhqoLU/mBn62lxa8pg/XzfLxueY5snHiuloemXtdTEpdWizWdqz9NRrDfG9zYfmv/+vsFHyis/NsCPfYDPNvasa5seYvBNS9pnEl6sSN8HOhWa9fRVUMpxxAcx83CQdDm2p3t5icnIomFnV4Z7hXfG9IDQaUvfBqm+g1z2VO19aYmEvuNp2Uir/PnbmmP/DIhpX7hqnokNbYNZTZr3LaAit59FwxHMOZx7ml22/sPLgSg5lHOJQ5iF2puws6BF1TsNzuLndzbSp0wZfb18PRyvHU65KqYyMDHx8CvNYDoeDd955hyFDhtCnTx82btzo9gBFREQq5OB6s3R4F1aflOaM/lAr1vQIuv3PwmbnaYlVH6OU7cAa04QeSlY5eHlDh6vN+qqv4PUz4fsxprqgqthJqdKG7tk65Cd8Ns+C1P3F9x1YY6aTB+j5r9Kfb1f1lbdSqk6RhEy3/H5RG6ZB0i7TmHnGOLPtzKsgoJZr5wbzQd4v1KzvXuT68wBmP2+WdZtDoxM0fq+IyJamUTyYaimRYx3I7ycV0aT0xv/l5eUN7fKrclZ9Xfnz2VVSYPrkAYTGmGpMUF+p0jjz4Ps7zEQRtePNHyTktJHjzGHTkU38sPkHxvw2hvO+Oo9/L/o307ZN4+/9f7M5aTPZzmxigmN4/dzXefO8N+kQ1UEJqRquXJVSLVu2ZMmSJbRqVfwvDW+++SYAF1/sxulRRUREKsPux1E7Dsr6ZSS0vpmpzB5GFRxplqqU8jx76F6dZmZo27HOf9J8gFv4JqTsgeWfmL5gFzxj/nruzunBU/YVJjrLSko1PR+CoyAtAVZ+BT3vLty34L9mWa+tOa40DfKTUoe3QEYSBIYf/1oZR0ylGEDdMwq3Nx9oEq3Ju+B/t8Kuv8GZa5I4fcs51bSXt4lp21wzq2DrS1x73t7lsOZ/Zv3cx6pmqnaHA87oB0snm75S3W93/zXk5LY/v59UZZucF9VumPlZPrDaVGJVpgLLHloYUr9weKuXl2l2nrAm//8xF/u/nS7+frdwCPQlb4F/iGfjkSqVlZfFsoRlLNi7gEX7FrHxyEZynDnFjgn3D6dvbF9igmOoE1iHqKAoukV3I9DHzUPGpcqUKyl12WWX8fnnn3P99deX2Pfmm2/idDp599133RaciIhIhdl/YbZnMipL0b4+SkrVHHuXm+WxQ/ds3r7Q4w7oepNpeDvnRTMj37QHYN1PcPF/C4fEVNa2uWYZWNvM9HQ83j5w5nDzoXX5Z3D2Xeb+St5dWFnR81/Hn9mxXlvw8jFJpL3LoOm5x79W4ubC9aL3ubcPdB1thrfs+NNsi+kE131rhhiWV+xZ5vXvLkdfqd+fM8voDq4nsiqi2QUmKbX9D9dnLJTTR0GTczf0k7LVb1c4hG//Kvckpewm57Y6TUxS6tDmY59xekvcDL89Y9a73WaGU8opJzsvm/l75vPz1p+Zv3s+mXmZJY6pG1iXzvU6c1GTi+jZoCe+XqqEOpmVKyk1duxYxo49/l/Y3n77bd5+++1KByUiIlJph7eaZR0XklJFBdc1Sw3f87zSZt4rjY8fdLgGWg2BGY+b2ee2zYXX25sEUtNzzWxzcb0qXrFjD91rfI6pHirLmdeYpNTBdSax1KAT/PWOSTTVioU2lx3/ub4BZmr4fStMX6myklKH8ofu1YotmYzpNBLm/BtyM02Pqas/L73azBV2X6m9yyE327zfZdmxoHA43flPHD8B5w5N+oCXL+RlmcRU8/5Vdy05uTidcGCtWXdnpZTDAbUbm6SUnVSqKHv43rHJc3s4robvFbf4ffNvWu3G5t8WOWU4LSfLEpbx89afmb59OinZKQX7vB3etKvbjh4xPegQ2YHmEc2pG1jXg9GKu5UrKSUiInLSKE+lVFGqlKoZcjJNUgdMtY0r/ENhyGtmZr4f/wUpu2H/SvP15+sQ2co0Jm47tOwhnceyLNf6SdnqtTYNy/cug8kXmeqqowfMvh5jTnztmE75SallZR9XWj8pW1AEDP/EvPbuY0pvzO6qhl3MMi/LnM9+bMvJMLEE1oaQKJj1tNke39v0aqtK/qGm8fz2+aZfl5JSYjuyDXLSzHq9Nu49d+042LkAjuw48bFlsZNa4cckpez/t4pWSqXsNX3iOlxXuZ/nk5nda+/Mq9zTI0w8bmvSVqZuncrPW39mb1rhTLo+Xj70btCbQU0G0TOmJ6F+FfyjipwUXE5K3XbbbYwbN46GDRue8Ngvv/yS3Nxcrr22HLO7iIiIuIvTaT6QgBkGUR4FSakE98Yk5ZOw1lQWQWHzeVc16wf/Wg57lsKW383X7sUmyfW/W83QsismlT77XWkSN5qqCHAtKQXQ9Wb44Q7zodj+YBxUBzqWbIFQQoNOptprzwmSUnalVNF+UkWdcYH5qqygCNOsPHGj6StVNCmVlwsfX2L6Vh3r/CertkrKdkZ/k5Ta8lvVX0tOHvvzm5wH1DLVhO5kD7dLqmxSyq6Uii++3U40J++C3Czw8Ydvb4Ydf0D6EejzYOWuezLKyy38nrr6hwqpkQ6mH+SXbb8wdetU1h1eV2xfx6iOXNTkIvrH9Sc8INwzAUq1czkpFRkZSZs2bejZsydDhgyhS5cuxMTEEBAQwJEjR1i7di1//PEHX3zxBTExMbz33ntVGbeIiMjxpewxJf5Q8UqpzGTXhipJ1bCH7oXHmQqc8vL2NRU0jbrDuY9CwnpTLbXqK/NB7/vb4Y6/Tf+lE1nzvVnWamSGjbiiwzWmYupoAmQfNf2OGnV3rSmvPQNfym7z/JCo0o+ze0rVOU5Syp0anpWflPrb9PGyzX+l9IRUqyEQ27Xq4wKIO9ssD2+FrNSKD1OUU8uBNWZZr637k6N2ZVNlKqXyck2vOShl+F7+/1uW01RTefuahBSYJPvpmJRK3GBm3AOI6eDRUKT8krOSmbl3JvNXzufv/X/jtGfWBeLD4rmoyUUMajKI2FA3J5DlpOByUurZZ5/lzjvvZOLEibz99tusXbu22P7Q0FD69evHe++9x4UXXuj2QEVERFxmz7zn5Vv+v5AHF+lTkJ4IYTHui0tc52o/KVdFtYTL3jGNx9/tZYbFLP8UOo8s+3lZqfBXfr/MDle7/uHW4TBD+CoisiX4BJoPYHv+gRal/F7lzCvsm1a3lOF77taom5ndcN1P8M8U6HS9iW3eS2Z/z3+Z6rCjCZCVArHdqj4mW2SLwvXEjdCgc/VdW2qugibnbuwnZbOTSCm7IS+nfMOBbSm7wcoz68cO3wuOBP8w87N0aAvsW164b88SM2TW9zSbWcye+CI02syaKzWaZVmsOLiCebvnsXDvQtYcWoOFVbC/TkAdBjYeyEVNLqJ1ndY4qqOqVmqscvWUqlevHo899hiPPfYYR44cYefOnWRkZFC3bl2aNm2qm0lERGoGu59U7XjXKmGKsiulwPSVUlLKM9ydlLLVa20SKksnw9zx0H542f1ZFk+EzCTwC4Xut7s3luPx9jGve9dfptl5aUmppJ2mxxNUT6VUuytNMmr3IvjxTlONuPo7M8Syfjs4d5ypKgz3wF+5/UMhrKH5kH9wg5JSYnrS7Vxo1uu7ceY9m51Espym2inCxQrKouwqKy/fkv/POBwQ0cQkow5tghWfF+7Ly4bdS6Bx7wqFftKyE3MauldjWZbF+sPr+WX7L0zfNr1YjyiAAO8Azm90PkOaDqFbdDd8vNTeWowK3wm1a9emdu0KlNOLiIhUtYrOvAemeapvsOkDpGbnnpGXUzj0pio+gPR5GFZ8YRIriyfC2XeWepgjJw3HX2+ZB91uqdgwwopq0MkkpezGvseym5z7BEJYg6qPxzcQRvwA34yCjb/CnBfNdm8/uOw9zw9zjWyRn5Ra79k4pGZY9xNkHAFvf2g52P3nD402935etukrVZGklN2PKjy29Bk96zQziZjln5kkNJif9ZQ9sOPP0y8pZVdKaehejbM1aSu/bP+FX7f9yvaU7cX2ta7Tmp4xPeke3Z1oK5oG9RvgVdFZcOWUpfSkiIiceio6854tuC4kpUFaovtiEtcd3FBYBeTuSikwVQln3QwL/mt6InUaAQFhJQ4LXPMFjvRDJknZfYz74yiLPfRv7z9m9r9jq9EPFZl5r7p+wfcLguGfwtR/wbJPzLbzHjfVZ54W2dI0Oj+40dORSE2w9EOzbH2JadTvbl5eZmj44S0V7ytlP+/YoXs2+48qdqK1UQ9TBbjwTdj+R8WuebJSk/MaKT0nnfGLx/Pdpu+KbW9euzkDGw9kQPyAgh5RTqeThARNICOlU1JKREROPXZPqYpUSoEZwpe0Q5VSnmIP3QtrACGRZR9bUb3ug6UfQcZh0zOq7yPF9+ekE7ziA7N+1k0QXKdq4jgeewha+iEzVOfYpuF2pVR19JMqytsHLn7TND7PSoHud5z4OdXB7iulSik5uNFUEgF0vqHqrlM7Lj8ptb1iz7efd+zMe7Y6x/xsn3m1+YPJwjfNbKKn00QcanJe4yxPWM6jfzzKrtRdAMSFxTGw8UAujL+QpuEV/N1LTltKSomIyKnFmVf4y35lklKgpJSn7F9plvXbV901giJM0/PZz8PCt6DHncVnxvvnY7wzDmH5BOLocVfVxXE8EU1M4mf3IjNU7vrif4nmUDXOvHcsh+PEDeKrW2RLszyy/fRsAn2q27cCFr0HfR+FWscMV83NNo3G7WrCfz4yy7rNC2dmrAp2hVNSBSul7OcdO/OerWilr08AtLnU/P+Gw8wuu/cfM6Pn6cAeuhdSX03OPSjPmcff+/9m6pap/LztZ5yWkwDvAB7s+iBXNr9S/aWlwjSgU0RETi3Ju0yfD6jc8D3Q8D1P2Z8/a1b9Kpg1q6hut5mheVkpsPqbwu25WTgWvGHWO4+qumqtsjgccO6jZn3Lb7Dzr+L7CyqlPJCUqokim+evWIXvjZw6fnvGDBmd+Xjx7emH4c3O8J9mZvKC7HTTgwlMlVRVfki2k0lVNnyvSeF6y8EQUMsk0+u1MdtOpyF8dpPzis5oKpWSmZvJO8vfof83/bl15q38tPUnnJaTNnXa8NWQrxjWYpgSUlIpFUpKrV9//NLo6dOnVzgYERGRSrP7SfkEVLwBtCqlPMey4EB+75CqmMq9qIAwaD/MrC+eaK4NsPJLHKn7sLx8sY7TBL1aNOkLjfIrPX5/rnB7Zgoc3W/Wjx3ic7oKrG2qKMD0JJNTiz2kd/3PkJlcuP2fj00T8PRE+Olf8N/OZkiut78Z7laVKlMplZ0Gafn9dY43fC+wtqn2guLDEON6mqU9RPF0oCbnHrNw70Iu//Fy3l7xNgkZCThw0K1+N57t+SxTBk2hca0KNPkXOUaFklKdOnXirbfeKrYtKyuLO++8k0suucQtgYmIiFSInZSq3bjiDaDtpNRRNeWsdsm7Cz90VsVU7sfqOtos968yvZucefDn6wBktLjMzLLlKQ4HnPeYWd8+H7bNM+v20D1QUqoou1pKfaVOLakHCv9AkJsJa743605nYUPzui0AB6TmT0FfVQ3Oi7KTSWkHTZKpPOzZ9IqepzTXfgM3zoDG5xRui89PSu3828xUeqpTk3OPSMxI5LE/HuOWmbewK3UXfl5+3Hbmbcy4YgYTB0zk0maX4uvl6+kw5RRRod/WJ0+ezBNPPMGgQYM4cOAAy5cvp2PHjsyaNYv58+e7O0YRERHXVbbJORSplNLwvWp3IH/onm+wSSxWtfrtTO8mMNVS66fCoc1YDi/Szhxd9dc/kfhehR9If38ODqyFXX+bx6HRpc4aeNqy+0opKXVqsSsnbSu+MMutv+f3D3TANV/CLXMgrhcER0Gve6o+rqLJpKJJJlfYQ/f8Qk1F1HGvEQeNuhXfZldK5aQVVpCdytTkvFpl5Gbwfyv+j0HfDeLHLT8C0K1+N7675DvGdBhD/WD19BL3q1Cj82HDhnH22WczatQo2rRpQ1paGjfccAOvvPIKQUFB7o5RRETEdXalVESTso8rS0iR4XuWVbV9SaQ4u59UvdYVr3Qrr66jTUPxNf8r/JDX6hLywuOr5/oncu442NbfJKPe6VG4XVVSxRXMwFfFw/csyyRP10+D0HpVO8ObFFbJ+IVA9lHYuQAOb4PFk8z2ZudDRH4Ce9TP1RdXYG2TVMpONUmmqFauP7fozHvl/f8luK5JwB5cb/pKNexSvuefbNTkvFrsSNnBL9t+4ZuN33Ag/QAAdQPrck+ne7i46cXqGSVVqlKz72VnZ5OXl0deXh7R0dEEBAS4Ky4REZGKcWelVF4WZKWqGqU6VVc/qaJaXwq/jjW9aA6uA8DqeU/1Xf9EGnWDjtfDsinFtzcf4Jl4aiq7UurwVsjNAh9/954/LxfmvwIrvzDXsNVre+onBjzJTlS3vhS2zzNVSfNfgY2/mO1dPFTR6HCYSqYDq8vfV+pEM++dSFxPk5Ra8QW0vrhyf4Sp6fYuM0tVSbndvqP7mL59Or9s/4W1h9YWbA/wDuCGtjcwqs0ognxVcCJVr0JJqS+++ILbb7+d3r17s3HjRpYvX86oUaOYPn06U6ZMoUmTU/gfRhERqbnycgqHRVR05j0oTEqBqZZSUqr6VNfMe0X5BkDH68Ceca/p+RDdHhJqUE+xS96EIa+b3jU56WabqgaKs5NSVp6pmKzX2r3n3zwT5rxQ+NjLF5w5sPJLJaWqkj2kt347qNUA5o4vTNCGNYAz+nsutvD8pJRd+eQq+/+psvpJlaXt5bDkA5NEf7sH9H0E2lwO2+bC5llmFsJL3jw1/o3Ys9QsG3T2bByniIzcDH7a8hNTt05lWcKyYvsahjTkwsYXMrzFcA3Tk2pVoaTU6NGjefnll7n99tsBuOCCC1i1ahW33norHTp0ICUlxa1BioiIuCRpp/lACpWrlAqMAByAZfpKVeZc4rrstMIKlHrV0OS8qC6jYOGbYDmh173Ve21XeXmbBKmSpKULrgtBdSD9kKkicXdSavsfZlm/HQydBFt+h18fhtXfwYAXwFtNf90uJxMSN5n1+m1NH7W54wv3d74BvCs18KNy7EqnI+WtlMrvQRXeqGLXje8F130HU+81VVeznjJfRf30L7j6i5N7+HlOZuHwTSWlKiUxI5Ev1n/Blxu+JCkrqWB7VGAUAxoPYFDjQbSp00bD9MQjKvSv+D///EOLFi2KbatduzZfffUVU6ZMOc6zREREqpjdT8o3qHKzpnn7mJmb0g8VzvokVe/AWsAy6+5OKJxIRBO45mvTH6ZxbzOzl5x8IlvCjj+rpq+U3WC++YVmpr/AcJj+KKQnwpbZ0NyDFTunqoPrCv/QUK+tec9ju8Ouv8DLBzqN8Gh4hOcnpco7fC9lt1mGNaj4tZudD3f8BXP/DQveNO9TUB1o0AU2TYeNv8Lqb6HdFRW/hqcdWG2qEQEadPJsLCcZp+Vkd+pu5u+Zz9xdc1l8YDG5zlwAAn0CGdxkMIMbD6ZTvU54Oaqpf6PIcVQoKdWiRQtyc3OZM2cOW7Zs4ZprriE0NJS9e/dy2WWXuTtGERER1+xbbpZ1m1f+r8PBkUpKVTe7n1TtePAPrf7rn9Gv+q8p7hXZIj8p5eYZ+HIyChsux3Y3y5AoaHquGS616islpSorL8dU99RqCOc+arbZVTK1GpmEFECPMSYp1fE6zw9PK1op5eqkGNnpkHHErNeqRFIKwC8ILngGzrrV9MSLamMmiPh6FKz5Dn55CJr0NVWEJ6PdS8yyTrOyZyk8TWXlZfHPgX/YmryVQxmHOJR5iIT0BHan7mbP0T3k2Am9fHUC6nBNq2sY1nwY4QHhnglapBQVSkrt2LGDCy+8kJ07d5KVlcUFF1xAaGgo48ePJysri3fffdfdcYqIiJzYzr/MMrZb2ce5IjjSfLBNS6z8ucQ1BTPvVWM/KTm12H2l3F0ptXdZfsWGA2K7Fm5vN8wkpdb/DFlHwT/Evdc9nWz+DZZ/atZbDTHDJEvrMdf6YnhgU/4waw+zK6WyU02iKciFmFL2FK6HNXRPHLUaFE9wDfoPbJ1j/rDyy0NwxST3XKe6qZ9UCWk5aUzdMpW5u+eyeP9iMvMyyzw+KjCKc2LPoU/DPvSI6YG/t5sngBBxgwolpf71r3/RpUsXVqxYQZ06dQq2X3bZZdx8881uC05ERMRlzjzYvdisN3JHUir/L8uqlKo+RRsai1REZH57iUObTeWNu/o82QnvqNYQUKtwe8vBZrhwTrpJTJ053D3XOxllp5sEXbN+poKnvHYuLFxf+hEMfrnw34RjE9UhURWP052Kzp53ZLtrSank/KF73v5VV8EUXBcGvgTf3WSG8EW1hrNuLrx3LQsObsA75ShE1ZD3sjQFSSlNJHAg7QCfrf+Mrzd8TWpOasF2b4c3TcObEhkYSZ3AOtQJrEPDkIbEhsbSMLQhDUIaaHie1HgVSkrNnz+fBQsW4OfnV2x7fHw8e/bsOc6zREREqlDCOsjKn2jDHl5TGcH5v6in1aAZ2E5lTiccWGPWVSklFWVXSjlzTNP8yBZlH+8qu5/UsQlv/xCTmFr1tRnCdzonpf58zTQh73yDmSmyvIompVZ+BRc87ZnZOMvDL9hU1aYdNH2lXOl7ZFdKhcVUbRPydlfA6m9Mb6nfn4X5r0L7KyE3G7bOxit1H3W9fLFumAaNzqq6OCoq/TAczu8TeRpXSqXlpPH28rf5bP1nBT2hgnyCuLDxhfRu0Juzos8izE+TX8jJrUJJKafTSV5eXontu3fvJjTUAz0gREREduVXMtRqVPk+HWA+aICG71WXpO2QfdSs19QPoFLzhdQD/zCToD60xT1JKcsqTEqVlvBuN8wkpbb8DkcTak4VT3Xbtcgs1/wPBr1cviq1nAzY80/h46xk07w7K9k8rsmJ6vA4k5RydQa+5PykVC03Dd07HocDLn8PZr9ohkVmpcDSycUPcebAj2Pg1vngG1C18ZTX3vz7wdvvtPw/wbIsZuyYwUuLXiIhw/xxLCooiutaXcfQ5kOViKrBnE4LC/D2Kp50tiyLtOw8Nh5IZe3eFNbvT+FIWg5p2bmkZ+fh7+NFy/qhtI4Jo3V0LZpEBuPrfXpUuVUoKdW/f39ee+013nvvPQAcDgdHjx7lySefZNCgQW4NUERExCX2ByJ3DN0DDd+rbjsWmKV/WGGfFpHycjjMkKr9q8o/I9rxJG4qbExd2r8vTc+FoLpmFr51P0HX0e657snm0GazzEyG7fOh6XmuP7doz66Wg2H9VPhjgtnnFwK1G7s9XLepHQd7lhRW9ZyIO2bec1VALRj4bzhvnKnkW/WtaRjf9FyctRrh+OIaHIkbYc4LpmF6TbI7f+he/Xbgc/r0QTqYfpBp26YxdetU1h82EzYE+QRxR4c7uKblNfi6a0iyVIplWexJyiAlI5eMnFxSMnJZtSeZJTuOsGzHEVKzcvHz8SLYzxtfby8ysvNIz8kjz2mVed75mwr/EDq6V2Mev6iaZyL2kAolpV555RUGDBhA69atyczM5JprrmHTpk3UrVuXzz//3N0xioiInJg7m5xDkUopJaWqjGWZ5sYL3oBtc822+u2qdkiLnPpqx5uk1JHt7jmfXYUZUr/0hKm3LzQ+x8x2tn+le655sslOg+RdhY/X/VS+pJSdlK7XFs6+yySlcjPyt+XPKFdTRbUGvi2cKfBECiqlqiEpZfMPgS43mi+b08nRLncRuuhVWPBfaDmkeBN/TztF+0lZlsWOlB38ufdPFu5dyD8H/iHPyiPIN4gA7wD2pu3FaTkLjh8QP4AHuzxIveB6HoxabEezcvnfsj1MWbidjQeOlnlsdq6T7Fxnqfvi6wTRKjqM6FqBBPl5E+TvTUpGLuv2pbB2XwoHU7NoHX36VMNVKCnVsGFDVqxYwRdffMHKlSs5evQoo0eP5tprryUwMNDdMYqIiJQtdX9hVUQjN/STgsKkVPphyMsF7wr9lyll+eUhWPRe4eP67WveX+vl5GMnjlwdTnUiO4v0kzpewjSqtUlKJaxzzzVPNoeOqRJaN9UM4fPydu359h8VGnU3f1iIbGlmP4WaPXQPIKaDWR5YY/o1+fiVeTgpe82yOiqlTiCtw2hCds/Bsfcf+P52uG0++NaAz3KWZarP4JToJ5WWk8bCvQsLElF7jpbswZyem16wXiegDoOaDGJIkyG0qtOqOkMVICE1kz82JfLX1kMs2naYo1m5BPn5EOTnze4jGRzNyi3xnCA/b5pGhtAlvjZd4yOIrhVARnYeadl55OQ5CfT1JsjPm2B/H+LrBhPiX/bvlAdTswjwrcHJeDer8G/YPj4+XHfdde6MRUREpGLsDzT+Yfl/tXaDglmRLMg4fPr2iakqqfth8Qdmvel50OteiO+tKimpvNrxZunuSqmyJlCIym+wnrDefKA+3e7jQ5vM0jcYctLMBBG7F7v2RwJnXuHw67ge5r3rNBKmjzXbano/oeiOZpmXDQfXQfSZZR+fUk09pVzh5YN1yVs43utjvof/TIFut3g6KvNHpvRDZr3hyVsplZ6TzqfrPmXymsmkZKcU21c/uD49Y3rSPaY7ob6hpOemk56TTlRQFF3rd8XHS38I84SvFu/i8R9Wk1Wiwim7YC3Yz5vLOzXk6rMaEV83iAAfb7y83PtvfmTo6TNkFcqRlPrxxx9dPunFF19coWBEREQqxG5C3LCL63+ZPxG7UgrMED4lpdxr+adg5ZlZDq/5qnxNkUXKYielknZUPkGUlljYK6msfnV2Mjwr2VTCVOfQrOqQdsj8jAYcZzhJYv57FN0e8nJMlcu6n1xLSiWsK2xobif+zrwKfn/OJLga1sCZ4YoKrgO1Ys3wxb3Ly05KZaYUzhIbFlMt4Z1QZEtoPsB8v45s83Q0xu78KqmAcIho4tFQKsJpOfl8/ee8t/I9DmceBiDAO4Cu9btydszZnN3gbBqHNcZxuiWv3WBvUga/rt7PrHUHyM510igiiEZ1gmgWFULvMyKpFVj4u0RKZg5/bkokNbOwsiki2I9uTSIIDSj+O0dGdh5P/LCar5eanm/hQf/P3lmHx3We6fs+wyiYEaNlW2amxLGTOA45TG2TNm3TFFKG7bbptpv+dpvSbtvd8jblpm2glKRBhxM7tuOYGWWLLJ6RNMzn98enmZEsyRaD9d3XNdc5c/Cb0dHAM8/7vHournBy8XQHBZkmApE4gUgci0HL1fPye+0vGR4DFqVuvfXWHvcVRUFV1V7LgD4780kkEolEMmrUDsDJMFiMdtAaIR6WuVIjTSIBu/8o5pfeLQUpyciSFKWigeELyknBW28R5aXnOqfOBLGQEFkuJFHK0wA/WyUe0ye39i38J51SzpnilhSlrvnW+UXB2m1imtWtc6rFAR96VoiCE90pBUKI6qyDxn3n3s7TrWxrApTvpbA4xTQZ6D/eJDsxFi+fdK7DznAnX3vra2yq3wSIkPL3z3s/H5z3QTKNmeM8usmBqqrsqG7n4W3VvF3lwqDTYDZo0SgKJ1t65jjtrElfszqNwuoZTi6e7mRntZu3TrYRjfcOFtdpFJaXZ7OsPJt4QsUfjrGj2p3KiLppcRH/dftCrOcpsZOMHAN+phOJtIXtlVde4Stf+Qrf+c53WL16NQDbtm3jgQce4Dvf+c7Ij1IikUgkkv6IBNLhwiPVeQ/EB2Fbnvii4W87//aSgVO9KV1ateyD4zoUyQVIZimgAKq4zoYjSiUF7+Ll5xZPNVrImSVei1qPQOVVQz/nROPUmxDxioynM7v7DsNu6xKlciphzo3wyn8Ip1rTAeGeOhdJUapsdc/lxcuGP/axonCJCGdv3Hvu7ZIh53oLmLNHe1QDJzmWgHt8x5FkkuZJHXMf41/e+BfqvCL0/87Zd/LpJZ8m2zSB/tYTGLc/wnP7G3hkey1Hm7z9bpdrN7JhfgH5GUZq3QFqXAEOnOkkEImz+URbjw52FoOWwkwTACpQ3x4kEkuw/bSb7ad7Xu96rcIDN8zjg6vLpYttjBmS/PeFL3yBhx56iLVr16aWXXvttVgsFu677z6OHJmiIY8SiWRqEY/Bq/8JRUthwR3jPZrxw9MoXEVG2/ic/8wuSMRA0Y58lx6LU4hSvpaRPe5UZ9cfxLTi8klZmiGZ4OhNYC8Eb4MQpUqHUf5V/ZaYni2Y9EXePCFKXWhh592FlqpXe4tSqpoucXRWgnOGeC5aDgu31LlEKVWFmn5EqclEMuy86aAoX+xPwPSI0iAyiieWAygpSk0Ep1Q0BA17xHzJBOoGeB6eqXqGB7c9SCgewqwz8+AlD7KhYsN4D2vCE0+ovHy4ib/vOsMbx1qIJdLOposqHLx7RSkmvYZAOE4wGmdeUQbLy7J7ZTiFonG2nGzjpUPN7KlrZ0FRJtctLOTSyhxM+rS7MxiJs/20izePt3KyxYdZL8LHM0w63rW8lIUl0s02HgxJlKqqqiIrK6vX8szMTKqrq4c5JIlEIpkknHpdtFE22GDebRO7ZfVo0bgffr1efHC89/nx+ZCdDCEuWDDywljyg3qoY2SPO5Xxt4nOXADL7xnfsUguXLKndYlSw+jAF+pMCzIVl51/+1TY+QUmSjXsTc+ffBXW/VvP9d4miHSV1ORUiuncm4QodfgpuOJr/b83dNaJvxNMblEqmSMVDwtHWcHCvrdLOqUmWnmn2SGmE0GUatgtQuNRRtb9PEpE41G+v/P7PHb0MQCmZUzjh+t+yMzsmeM8solNNJ7gqT1n+MUbVZxq86eWZ5r13LS4kPdfXM6cgn4y7PrApNdy5dx8rpybf87tzAYt62bnsW62zAmdSAxJlFq5ciVf/OIX+dOf/kR+vvjDNzc38+Uvf5lVqyZ4GKFEIpGMFG3HxTTiEzkRWaXjO57x4PA/IRGF2q3ii1j+CHW+GwwnXxXT0fhCY84S01DnyB97qrLvMXHNmB2izEciGQ2yp4nXpeF04KvZCmpCZMsNxLGRDDtvPSpy0y6EHyoS8XR5NIiyqmBH+rUR0nlSGl06z2vBHfDmf4v3yTO7oaSfMqykS8qcLcofJyu2PLAXCYGtYW//olQyUypjAnTe685EckrVbBHTggVgmtiulSOuI3xn+3fY27oXgPWl6/nW2m9hN9jHd2AjSKs3zJaTbbx1so12fwSnzUCOzUimWU8omiAQieELx3D5IrT5wrT5wmRbDawoz2bFNAez8u1oFBEFVNMS4KVTNeyt62RrlYsmTwgAjQJXz8vn9mUlXDE7D4PuAnjtlAyKIYlSv/vd77jtttsoKyujtFR8Caurq6OyspKnnnpqJMcnkUgkExdXVbf5k1NTlDq9KT1/6MmxF6Xcp9N5JPNuGfnjJz8QBztG/thTEVVNB5wveR/oplbLY8kYkl0upsMRpU5vFtPSVaIk8HzkdjmlogGRp+SoGPq5JwptJ8TjAdDohaB8+s2er7fJPKnsinTZWu5skQd0ZpfotNmfKLX/cTEtXzP5RbyiJXCsoSvs/AN9b9PZVb434ZxS3USp4XasHC5JobJ8zfiN4RyoqspbZ97i4UMPs71JNELQKBo+t/RzfHjBhy+ILCJfOMaTu+v5y846Dp7xDHr/aleAPbUd/Hrzubs56jQKty8r5pPrZlKRYx3qcCUXAEMSpWbOnMn+/ft5+eWXOXr0KABz587lqquuuiD+ESUSiWRAuE+l510nYcYVYz+GqtdFPo+vBfwtIovh2m/B/NtG/9xhr/jCkeTQk+cu0xgN9nV9ockqH9nOe0lMWWIqnVIjw4G/px2GMuBcMpokHTsdwyjfq+4S3SsuH9j2maWinDviE26pC0GUSpYvWvOE6HLiJeFO7S5KJfOkkqV7SZa8T7xHHPw7XPud3sJe3Q6oek3Mr/zoaIx+bClcAseeP3fYecopNUFFKTUOYc/4OZTisXTHywlWzlndWc2zp57l2VPPcsaX7qI41zGXf13xr1xUOPFLDfvCG4pyvNmXcjkdbfTy5J4z+MKx1DZ2k45LZjiZlmNNOaI8wShmgxaLQYfVoMVhNZJjN5BjNVLfEWRntZs9tR0Eo/Ee5yvJNovOd2XZXDUvn+Is81g/ZMkEZMh9DhVF4ZprruGaa64ZyfFIJBLJ5OFsUWqsScThyY+Dr7nn8t1/GhtRqmab+ACraESJi+sENB8au/bdqipKwQAWv3d0fmVPle91jPyxpxphL7z0gJhf+B7hpJBIRousLqdUZz3EIqAzDG7/gFt0jgOouHRg+2g0wi11ZqfIU5p93eDOORFJ5kkVLYEZVwpRquq1nm6apFPKeVaGzoI7YONXhah/7HlYcHvP9Zu+J6Ylq2D6ulF6AGNIj7DzGGjP+pqlqhM3U8riSM8H28dPlGran84nK79kfMbQjUg8wks1L/H40cfZ17qvx7rLSi7jnnn3sLJg5aQzZZxs8fLioWY2HW9lV017j3DxJFaDltuXlXDr0mIWl2Si0w7+M1Y0nqAzGAVE+V6H28XMsiI0k90VKRlxhixKvfrqq7z66qu0tLSQSCR6rPvd73437IFJJBLJhCYWEQGtScZDlKrfkRak1n0VWo/BoScg0Hbu/UaK02+KafkaEV7dekS4pcZKlKrdlnZBLL5zdM6R/GAunVLD583/Bl+TcJJc/eB4j0ZyoZN0SqGK12rnjMHtn+y6p7dA0bKB75eXFKWODu58wyURhxfuFyV2G747co7VpOuncAnMvFLMd9YJISq3KwMqmSl1tlPKnA1zbhDvC3sf7SlKNewRAhfA5V+ZWJ3ohkoy7DwWhLZjkD+/5/pgu1gHEy9TKukKBjHO1P/PGJMsx3dWipyuccAT8XCg9QDbm7bzz5P/xB1yp9aV2ku5afpN3DD9BsoyysZlfMPhcIOHn7x6go2Hmnqty7boybEZycswcs28Am5fVozd1E8XyQGi12rIsYky/UQiQSIwZOlBcoEzpCvjG9/4Bg8++CArVqygsLBw0qnDEolEMmw6aoQ7KEnyl+Kx5GhXB7Pi5aIb0v6/dYlS7nPvN1Ik86QqLhO/AL/RJUqtf2BsvmDsfVRMy1aDY/ronCP5QV1mSg2P1mPw9i/E/OVfgYzC8R2P5MLHlg86E8RCIldq0KJUV55U2erBuaySYedj3YGvejPs+I2YX/LetEAyHBJx0WEVhAvIORMyy6CzVrilcmdBLAwdtWIbZ2XvYyx+n3hfqHoVPI3p//03v9913GVpsWuyYy8AW4EQ3xv29halknlSMPGcUnqTEGCjgbH7DNEXNVvFtHxsS/d8ER9/P/53/ln1T6o6qlBJO4f0Gj0bpm3gPbPfw+LcxZPue6+qqmw75eIPW6p56XDaWV+RY2X9nDwun5XLqgoHJr12HEcpmeoMSZR66KGH+MMf/sAHPtBPiJ9EIpFc6HQv3QPxoTwWHrvgZlWFI12i1JwbxNTqFFP/GDilepS2XCZ+EX/jO+CuEssLF43u+aNBOPSUmF/83tE7j3RKDR9VFQ6OREx017roE+M9IslUQKOBrDKRYTaUsPNkyPlAS/eSJMPO2471XcI1Whx8Ij1fvWVkRKm2ExDtatVeuET82DDjCtj9sBCZLv6EeC9M/kBztlMKYMZ6IRD6mmH/X2DtF4TQdew5sf7y+y8Ml1SSoiVwfKMIO196d891yTwpYyYYJ2B3NnO2EKXGqwOfqnYTpUY/5FxVVao91Txd9TR/OfoXvFFvap1eo2eecx7rStdx28zbcJqdoz6ekURVVapafbxypIW/7KjjdJs/tW5eYQafv6qSq+fmo9FcQP97kknNkN4pI5EIl1wy/nW+EolEMm4kRalkC2hU0Qkub87YnL/lMLR3dTWZc5OYWro+NMWCEPGDYRQ7mVS/Baigt4pfunUG4RBoOSx+FR9tUerocxDxCifE/FtH7zzJTKmoH+LRdGcpycDZ8Rs49YaYv+57g8/2kUiGSva0oYlSvhZRjgww7bLB7Zt0SsUj4jW6L6FmpIlH4cjT6fs1W2D1p4Z/3FTIeS5kFIn5mVcKUar6LfFDTNIlbMpKvwd1R6uDRXfC1p/Atp+Lkr2Ww2JdwUKYtWH445xIFC4RolTt1t5d7JJOqeRzOdEwZwvhbLxEqdZjEOxyaY1wyLmqqjT6Gznefpxj7mMcaDvAvtZ9dIQ7UtvY9DbePfvdrC9dz1znXIzaidMdtjMQ5cXDTTR1hnD5wrj8EWbk2njvqjIKMkUDgWg8wabjrWw82MRbJ9to7Az1OMbq6U7uXTONq+flTzq3l+TCZ0ii1Ec/+lEeffRRvv71r4/0eCQSiWRy4KoS05IVIlsp1ClyNcZKlDra9SuzszKd62HJSa8PuEZXlEqW7pV3K22Zf5v4snHwHzD7epEHYS8YUfeYznUUZed/i1JFEC6x0Qxk7Z6zEeoEa06/m0r64OQr8MJXxPyiu8anQ6Vk6jLUDnzJ0j2DffCOI3uBeE0KdYrXw7EQpU690VNIqNkCicTwmz+kQs6XpsWVistB0QpHzZv/LUq+QDzO/r7oLrlbiFL+ri6xSdZ//cJySQHMugbe/C/hGK59u2cZmmeChpwnSXbgG0q5eiIB9e9A3tyhvyfXdrmkMkqEy3GYhGIhtjZs5eWal9lUvwlPxNPndvmWfN475728Z/Z7sBvGxsHmD8do8YZp9YZx+yOUOSzMLrCjPcu55A1F+d1b1fzmrVN4Q7Fex/nZ6ye5Zl4+uXYjz+5vxO2P9FhfnGXm+oUFvHdVGdNzbaP6mCSS4TAkUSoUCvGrX/2KV155hUWLFqHX9/zl+H//939HZHASiUQyYUk6pZwzxAfNM7vGNuz8yDNiOvfG9LLuv1L720bkQ12/dM+TSjLvVnj92+IL4G+vEsv0VvjgP6F05fDOFw2h/PUD5CSDcUEIRmv/ZXjHPR89wl87pCg1GJoPw9/uFR0aS1bCTT8a7xFJphrJDnznc0ol4vD3DwshwZYH/laxfNqawZffKYpwS9VuE2Hn824Z9LAHTbJ0L+lWDbYLp9fZmUZ9seXHojPmuq/1FrG6h5wnMWcJ59O+R2Hz/4jGBdB3nlSSvDlw/Q/gzG5wVIgMwPz5QsC40CheDuVroeYt8dx2F6WSnfcyJrooNQSn1JYfwqsPimNc9mVY+dHB/yDVPU9qGGJlo6+R3x38HU9XPU0gFuixzqwzU5ldyVzHXJbkLWFJ7hKKbcVj4hxSVZWtVS5+vfkUbxxr7bXeatCypCyLwkwzwUgcfyTG3roOOgKie12GScfcwgxy7EYyTHpeP9pCkyfECwfToeVajcKllTlcOSePtZW5THNapCtKMikYkii1f/9+lixZAsDBgwd7rBvMhb9p0ya+//3vs2vXLhobG3nyySe59dZbU+s/9KEP8fDDD/fY59prr2Xjxo2p+263m89+9rM888wzaDQa7rjjDn784x9js0k1WCKRjCJJUcoxQ4S3jrQodfR5eOZzoqX4FQ+APT+9rr1GtE2GdOkeiKBSg020Ux7NoFJvk8hLgZ6iVO4s8UH04BNpC37UL0o9hitKbfwKSpcgpebPR1nxEVj0ntHP5TBlpOdlrtTA8bXAo3dC2CPE0bseBb15vEclmWoknVLt53FK1e+Ew0+JeXdVennF5UM7b95cIUo1Hzz/tsMlFk47Zy/+JGz6gfhhoHrL+UWphj3w8v8T8+VrejoZzw45787NPxVO3B2/Fu83cH5H2KqPDejhXBCs+bwQpY6/IITJvDkiB/HMLrE+c4J13kuSEqUG+flBVWH3n7r2bYcXvwZvPwSXfAZmXiVEyPN9P+yRJzW0iJgaTw2/PfBbnql6hpgqXEU6jY5Lii7hqrKrWJG/gmJ7MRplmA7CIfD60Ra+/+IxDjf2dGtpFLCb9HQGo/gjcbacdPXa127U8ZFLK/jw2goyunXDi8UTvHKkmcfeqSMYiXPdwgJuXFRErn3ilB1KJANlSKLU66+/PiIn9/v9LF68mA9/+MPcfvvtfW6zYcMGfv/736fuG409/9HuvvtuGhsbefnll4lGo9x7773cd999PProoyMyRolEIulFPJruNuSYLkQagLYRFKUO/kP8Wr/7j3DgHyIcdtV94lfqY8+LbeyFoqyiOxZnlyg1imHnSZeUKRMKzsqOuuF/xC0eFd3WXv46nHh5eKUk+x6HXX8AwHPx/diu/jcU7Rh1idHq00JfaJxyNiYjG/9NdOgyZsD7/jpurb0lU5zsLqdUqEN8WU5+6T6b+h1imlkKK+4FX6soS17+oaGdN/m6fOx5Ifyc/To9kpx8FcKdoNHBnBtFydjeGiGKXHTfufftel0FhCjXXZRynewZct4drQ5u+IEQojb+mwg6L1g4Ag/mAqHy6rRrbetP4Jafw7NfFIKnRjdxc7SG6pRq3JfOuFz6ftj/V/H6/8L9YllWmfgBbf2/9x8r4KpKlzcOMuT8ePtxfrP/N7xY8yKJrtD9ImsR9y64lxum3zBmJXn98deddXzlH/tRuxr6XVqZw4fXVLCgOBOH1YBWo9DiCbG7tp1dNe14gjEsRi0Wg5Ycm5HblhaTZemdxajTatiwoJANC2Q3W8nkZ4xagvTNddddx3XXXXfObYxGIwUFBX2uO3LkCBs3bmTHjh2sWLECgJ/+9Kdcf/31/OAHP6CoaIIGCUokkslNR60oSQIhSvm6WuyOpFOqs65rRhFfDF7/Nrz+HfELfNKxM+eG3kKPxSl+JR+tDnyJOGz7mZivuBw0/YhDWr0oW3n566I9dtO+oX0xaz4Mz4oSPXXuzQQWfxjbWFvRTZldopR0Sg2I9hoRdg9w4w8vzBIdyeQgWb4H4ro8nyg1/XK49F+Hf95Fd8H2X0HzAXji4/DxN0fPKXjwH2I640qwOMQX+r2PCNfJ2UHb3Qn74MDf0/ePPAPX/0+6XDGZJ9U95PxsLvq4EKyaD4rzSwSKApd8Dp76hBBoMopEuSPAtd8d/UYgQ8XiENPBilLJ1/v8BUKAu/wr8NYP4dhG0Qimoxbe/rlwWN/1WN/NLnb+VkyzK0SX1vPQGe5kU/0mNlZvZFP9ptTyaRnT+MjCj3DD9BvQa8a/Mclj79Ty1SdEp+JV0xz8583zmVeU0Wu7vAyTFJgkU5pBiVL9uZnO5oknnjj/RgPkjTfeIC8vj+zsbNavX8+3vvUtnE6Rm7Jt2zaysrJSghTAVVddhUajYfv27dx22219HjMcDhMOh1P3PR5hpUwkEiQSiREb+1iTSCRQVXVSPwbJ5GLKXnNtJ9EAqt6Cas0Dxww0AIE2En53umPbMFA6alGAxI0/RPE0wrafoUT96a5FQGL2DcKB1H0/ixMFUP1tqKPxd9n9JzSN+1BRUNf+a6/z9yCzFCV3DkrrURLHXoSCQQYGh70of/0gSjSA6phO/IYfo3rDY369KaZMFM8ZEoH2cz9eCQDK2w+hqAnUrHLUubdM2udsyr6+XUgYbOI1MeAi0V7d29nZhVK/Q7zeFq8cmetVo4PbHkL59RUobcdQX/kG6rXfOe9ug77mogGUYy+Isc+/VYy97BLxfuRvJdFyFHJn973vgb+jifhQ9RaIhcRzdHoTTF8HgHJ8o3gvKVyCqqqkbB5nU7JS3MQDGNi4pwLzb0N59UEUbwNs+j4A6qI7UVd8ZMI8T72uN1OW+GwTbB/45wdVRTn8lLgG53W93meUCIHzuh9A23GUQ0+ibPpvOPkK6hMfQ739Nz1/0Ap7UHb/SRxj1cfFtdbP9bazeSe/PvBrdjbtTJXoAczKnsVHFnyEq8uuRtt17PF87Y7FE/zx7Rq+9dxRANbMdPKr9y/HbNBO2fcU+Z46NRno33tQolRm5ih2OOqDDRs2cPvtt1NRUUFVVRVf+9rXuO6669i2bRtarZampiby8nqWBOh0OhwOB01NTf0cFb773e/yjW98o9fy1tZWQqFQH3tMDhKJBJ2dnaiqima4HVckkgEwVa85S+0+MoBYRimu1laUuJ1k4lP7yR1E8wcpvpxNPEJ+V0lguzaP6LzrYNbd6FsPom/eg6F5L3FbIV7rHGhp6bFrpsaGGQi66vGctW64KGEPOa+I187g3Hfj0Rb0Ov/Z2IovxdZ6lNjhZ3HP/dDAT5aIkbXx05hcJ1C1Rlzrf0jEExqX682htWAA/G1n8I/wc3qhoYS95O5+GAXwzrubQFvvfIzJwlR9fbvQcFiLMARc+OoOEnBe3Gu9xtdMXlfZkNsyndiI/Y/nYFn5BTLe/h7K9l/Q7lxGpPTSc2brDPaaM1ZtJDvqR9UaaHWsQm1pAdVErq0Qra8R78GNBOf37Q5zbP8NBiA44wa0nlqMDdsJ7XoMj20e2s4acroytjqnXU9Ivu4NCcuCD5Cx7b8BiDrn4lr5VWjtHXA9Xpx9vRkjGrKBhK+N1gH+zXWtB8npaiTgyr+UeK/9smHeh7HENGRs/S7K4acIqgY8lz2Y+l+w7H+YjIiXhMFGa/HV4jo+C1fYxa+O/YrXGl9LLbNoLazKXcWVhVdyUe5FKIqCa5zfc1p9EZ4+2MY/D7bR4hMB5ReXZ/DtDWV4O1x4x3V044t8T52aeL0Du+oHJUp1z3YaC+66667U/MKFC1m0aBEzZszgjTfe4Morh24T/upXv8oXv/jF1H2Px0NpaSm5ublkZPS2VE4WEokEiqKQm5sr/9klY8JUveaUqPhQqcudlRLG1YxiFM8ZslUX5A0zP8d9GgXxK2F2xWLI6DpeUSmQLnnuqxhEcYiuPmY1gGm44zj72C/+GCXkRjVmYLr+25gG0olu8a2w99cYWg+QZ2HA2ULKxq+g1L4BgHrTj3HMu2zcrjfFnguNYNPFsI7wc3rBse1vaKJ+VKMd26WfwDbaQfSjyFR9fbvQUHKmQ+sB7PEObH39/7reBkA12HHMWt1/SfJQuOp+1Ia3UGq34nj+Y6hmhwgfL1uNetmXhaOqG4O95pTXXxQzldeQWzI9vbziUjjwVzLc+7Hnfb73jk0H0LSKkiLTmk+ILnsN2zFXv4Lp9p+hvPMd4XZ0zCDj4g+QMZLPyVTisk+hHnkc4hG073uMvOzy8+8zhvS63gLTANCEO8nLzR1QBzxl/5sAqAULcc66qP8Nr7ofVRtD3fx96k4+yTF9kOMli6nqrCJcuwUK8iCjGPvJn1JiL6HUVopZZ+ZExwmOtx9nb+tegrEgACvyV3Dv/HtZVbAKg7aPUsBx4h+76/nakweJxsXnN40Cty4p5tu3zseol/9D8j11amIymQa03bhmSg2W6dOnk5OTw8mTJ7nyyispKCig5Sw1PRaL4Xa7+82hApFTdXZgOoBGo5n0/ySKolwQj0MyeZiS15xbBHoqzhkoycftnAmeM2jcp4Ye6J3EUy+mGj2ajKLBHc8mhCIl4EqPbSRoPQY7fiWOve6rKPYBijNlF4MpC0IdaKpeESGo52P7L+EdcS4u/wqaJe9NrRqX662rHFMJdY7sczoRCLihsx78LSKHLLMEys7xxTweE+HNpkwRbJzMIEmue+eXACjLP4RiHlt39WgwJV/fLjSySgFQOuv7/v89s1OsL16GohvhDBqNBm57CP54C7SfRgm6oXozVG9GcVTAkvf12mXA11zALZpIAMqiO3s+tmlr4cBfUWq2iq7YZ4sLe/4opvkL0ZQsF4HwL3wZJehGOfg32Cvyj5S1/zLyz8lUwpwFn9kJagJFP7AvZmNNj+vNKl7PFTUu4gJM5/mhXlXh8D/FPvNuPef7YzQe5bmyBfx+5lxOxf3g3QdH9omVekT34KgL6l7r9xi55ly+tOJLXFdx3aC6vY8Fz+5v4Cv/OEBChfwMI3etLOOuVaUUZsqus92R76lTj4H+rSeVKFVfX4/L5aKwUITArV69mo6ODnbt2sXy5csBeO2110gkElx00TnUeolEIhkO7lNi6kj/Mo1zJpx+E9pODP/4yc5+mcWDF7gsInOPwAhb2F99EBIxyJk9uNbeWp1oCX3w73D8xfOLUlWviW5OAAvfDeu+OvQxjxSmLDEdzaDzeEx0CBuI+2yk2PcXePLjwFnZHdZcEaK/7INQvLznurd+CK9/K33fXiicH/kLRPh/Zx0oWlj18VEfvkQyIDKFKEVnfd/r64UoRemq0Tl/drkQJtynRCD4O7+G2q2w77E+RakBc/AfkIiK16dZ1/ZcN22tmPqa4NQbPbvqddaL8G2A5fcIwcqWC9MuFe9hz/2rOG5GMSy6c+jjkwj6CvWeqJi7/cgQbD+/KNWwRzRWAZjfO8e3I9TB/rb97GnZwzNVz9AcaE6ty4vFqIxEmRVXsEdDkDsHddG7aQ+1U+eto85bhz/qZ2bWTGY5ZjE7ezbrStdh1ffTvW8cefVIM194fC8JVYSZP/zhVZgN0hklkQyGcRWlfD4fJ0+mu1WdPn2avXv34nA4cDgcfOMb3+COO+6goKCAqqoq7r//fmbOnMm114o337lz57JhwwY+9rGP8dBDDxGNRvnMZz7DXXfdJTvvSSSS0SEeS38I6y5K5VSKqatq+OdIdt7LKhv8vpYuUSMwwt33akWJC5d+UXTWGwyzNghRqup1iEWEC6fqNSGgFS/rue1r3xbtxUsvhpt/NqDygVHH1OX4CXWM3jleuB92/g7e+zjMHqN24Vt/SkqQ0lvE36OzHvytolX8nj/DR19Jd02MhVNOqBTeRnE7+Up62fxbU+4UiWTcSV6LnbW918Wj4os1pMO6RwOtDnJniZs5C/50G5zeDJ1nxI8PQ2H/X8R0/m2gO8v975gubu5T8KdbYfH7YO2/wN4/w9sPQTws/ucXvju9z/zbhCgV68pWveSzk0tQkZwXVVVpDjTTGe4kGAvii/g43XwaT6OHel89rkArWblOcuNx8o48wrzpVzPfOR+L3tLn8dz7H+eg2YSaXQGBesLeKk51nuJ4+3GOtx+nxlPTY3udRsdN02/iQ/PuYfq2h9KOaIDb/hOmrRnFRz/yqKrKE7vP8NUnDxBLqCwqyeS3H1ohBSmJZAiMqyi1c+dOrrgi/etNMufpnnvu4Re/+AX79+/n4YcfpqOjg6KiIq655hq++c1v9ii9e+SRR/jMZz7DlVdeiUaj4Y477uAnP/nJmD8WiUQyReisFY4h6O2UAnBXie4zw7Emd3SJUplDEKWSTptguxDQtCPwMh/xp0WupPg2GGZeCYoGIl549RvCMeU6AVoDfHZXWnxrPpwqpeGabwk7/0Qg2U1xtJxSiYQQ7VBh+y/GRpRq3Cfa1QN87LW0I8rbJFrDb/s5tJ8WIuH7u9rGH3pKCFZaA3xujygfaj4k3B/Nh8RNTcBlXx798UskAyWzRExDnRDy9HR/NB1IizDFK3rvOxpUXC4cht5GOPA3WPuFwR/DVQX1O8T84rt6r1cUuOtRePqzYrt9j4pbkowSuOlHPTvFzr1JuKTUuBCol31w8OOSTAg6w50cbDtIW7ANV8hFS6CF4+3HOeo+ijdyntBhW5cT6cRjcOIxtIqW2Y7ZLHAuYLZjNrMds2n0NfLMqWfY0ryJeEEe4IfXPtPn4YxaI/Od81lRsIJ3z3o3BdaueJXrvgco4oeO4hVQfsmIPf6xoMUb4t+fPMjLh4X7a06BnT9+eBV2kyx3lUiGwriKUuvWrRNtZvvhxRdfPO8xHA4Hjz766Hm3k0gkkhEhWbqnM4svFkmcM8Q0GgBvQ/qL0FBIlu8NxW2SLN8DIUzZcoc+jiTdy16yhhDUanFA6UVQuw22/Sy9PB4R+VHXflvc3/MnMc2dCyVj9AVxICSdUsGO0Tl+69G04HXqTfA0QkbhufcZLl2ZMeQv7FmiZy8Q5ZnOGcLNcfJl4ZIrvUgIZgDzbxfXd2YJFC7qeVxVnRjuNokkSWa319HOejDNS99Plu45poPVyZig0cLCdwmn4v6/wJrPD/5/JumSyp4m/jf7Im8ufPglse0r/wG+ZvFadumXYNV9vUV/a44oAzz2PFzyOTBMvDIpybk55DrE40cf54XTLxCOh8+7vVlnxqazUZ5ZTllGGTnmHDq2P0RbIsSZnApOhFqJq3EOuw5z2HW49wEUMCUSGA020OrRoKEso4xZ2aLcbq5zLnMdc9H35a5WFLjuv4Vbzzlj0rxvqKrKP/c28I1nDtEeEN31bllSxIM3LyDTIgUpiWSoTKpMKYlEIhl3XMk8qYqebqjMMtFJKRETQejDEaWSZSaZwxSlAm0jI0olnVs6c8/jD4b5twlRStGInBKLUwhUu/8Il39FlJ/se1xsu+wDE+sD6mhnStVu7XZHFVkxl3T96hwNwqvfhIwiuPhTQ3Pgbfo+HNsIt/wc8uaIEspkpszSu/veZ/oVUHaJGNtr34Ir/yNd5nTRff2fayL93SQSAHM26K0Q9QtRKr+7KNXlNhrN0r2+WHSXEKVaDgu31tni7rlQ1bQotejOc//PaTSw5L0iI+70JuFG6d6c4Gxu/T9o2AvT1w18PJIxIxQLUe+tp85bR623lnpvPW3BNtqCbbQGWznjO5Pa1qg1kmPOwWl2kmPKYUbWDOY45jDbMZtccy4mnQlUaGlpIS8vLx1G/Pbj0HIQVn6VwJL3cbDtIHtb93LUfZSj7qPUeeswaAxcUXARN+5+gjXBIPrP7RWfiQaLokDpGP/vDYODZzr5z6cPsbOmHQCn1cC3b1vAhgWj/COSRDIFkKKURCKRDAZXVw5e99I9EGVyWWXCSdV+GiouHdrxE3HwNIj5oWRKmTLT4thIhZ0nM7SyyoYuOqz8mBDZcmeLX0VDHtj1MIQ9IrsooxCCbtDoxRe2iUQqU6pzdJxAybyuJAf+mhalNv8vvP1zMd9yGG7+ad+d8YId8No3RX5X5dXp5R118Pp3RUnOn++Aj74s3CHJ53rhe/oek6LA+gfgD9eLbmFPf1YsL1nVO/xcIpnIKIpwnbYeTef1JRkvUapggWgO0HxQCEyDEaXq3oH2ajE/0CByUwbMvfH825mze4aiS0aFWCJGrbeWk+0nOdkhbqc7T+OJeAhGgwRiAUw6E06TE6fZiYJCva+elkDLeY891zGX9819HxumbRDC0zlIqIneC83ZYhpsx6K3sKpwFasK000A/FE/WkWL6fRbsOUR0JmG9lllArKntp1n9jVS7fJT4/JT3x7EoNOQazeSZdazt66DRFeBz61Linjgxnnk2Hp3c5dIJINHilISiUQyGOq6BISCPr5EZE8TopT79NCP721MZ1YNpXxPUYQLydcM/hEKO08Frw8jvFqjgTnXp++bMkTnp20/g7d/kf6Vdc4NY1dGM1CSuStqHCI+MNpH9vhJUWrpB0QJY+M+aD0mAuW3/Di93d5HRL7X7b/uHUD8xndhx2+EA+rz+9JuiHd+KcYN4KmHR96dXjfr2nM/19PWCMfUqdeh9YhYdpHsqieZhGSW9Bal/G3iBwQYe1EKhKD08kGRK3XVNwae/3ewK+OtZGW6bFwyYWkLtnGw7SAnO05yov0EVR0iDDyaiJ5zP3/Ujz/qp9bbO6DfrrdTYi+h1F5KniUv5YiqzKpknnMeynB+OEm+3wXa+1yd6n7XdkxMnZV9/1AySVBVlTePt/KLN6rYftrda304lsAbiqXuzy/K4Bs3z2fFtHM4DiUSyaCRopREIpEMlGAHNO4X8305obK7hJX2YYhSyTwpRSNacg+FpCg1Uh34OoZRTnguLvq4EKQ6a9MlixMxXDfplAJxDYykKNVRl/6ifPGnRImj66QQl5oOiC5Z2RUig2bT9+HwU6Kk784/pTtueZtEtzwQzrNtP4crvw5hH+z6o1i+4A4RYN58MH3upe8///jWPyBEKQBbAcy7ZSQetUQytiRfu7rn4zV1Bf1r9JA3r/c+o83Cd8HL/0+8Vp9+A2ZeNbD9ku6uWWPUpVNyXkKxEC2BlpTzKBQPsbVhK6/Wvsr+1v397pdhyGBm1kwqsyuZnjkdh9mBRWfBrDMTioVwhVy0BduIJWKU2ktTtyxj1vCEp3PRzSl1TtqOi2nurNEZxwhT5w5wstVHnTtAjStArTtAbdc0GI2ntltSmsWqCgdlDgulDgvxRIJWb5hWb5hSh4UbFxWh1cgydYlkpJGilEQikQyU2rcBVdjV+yphSrp9huOUSuY32YuEU2YoJHOfAr1/9RvWmEbaop9VJkSOQ0+I+5mlwpkz0UhmSkFXrtQIinNJl5QpC3LnCPfE698WYl3UL9Zd99/C1WTKgpf+HU68KLa5+kGxfutP0x3EALY/JASug3+HcCcYbHDjD4UL7e8fFttY8wb2JbhkBcy9GY48Das/NfRrUiIZT5IZfx3dnFItXcHNubN7Ow/HgowiqLgMTr8JR54d2P9jPCa6lAIULh7d8Un6pMnfxJ6WPexu3s3x9uPUe+tpCZ67rM6sMzMza2b6li2muebc0ROXhspARanWLlEqZ/bojmcYhGNxXjjQxMPbqtlT23HObdfPyeMTl89g5bTsifc3kUimAFKUkkgkkoFSvVlMS1amXSrdSTmlqod+jqRjaDilctYcMR3x8r1RyI1Y/Zm0KLXk7qEFeY82BisoWlEGF+oY2WPXbhPTsovFY1/4LiE4JQWp2dcLQQpEzlTED298B7b8BGZdB86ZsOO3Yv36B+Dth4RD7q3/haPPiuVL3y/cXgvuENfESw+Ijl8DFZhu/zU0fEqMUSKZjPTllGo+JKb588d+PEkqrxaiVPK95Xy4Tgj3JPRdQi4ZNr6Ij6rOKtqCbbiCwql0xneGOm8ddd462oIDe18tthWzvmw960vXszRvKdrJUuJm7ipLO69Tqqt8L6dydMczCNz+CHtq2zna5OVIo4dtVS5c/khqvUmvocxhocxhpdxpEfNOC5V5NkqyLeM4colEIkUpiUQiGSjVb4nptLV9r8+eJqahDvGBLvmL42AYiVK5lFNqBILOY2GRcwWjI0qVLIdl90DDblj5kZE//kigKCJnI+Aa+Q58SadUUvBxTBeiZ/0O4cjb8N2e21/6r3DyZbH+yY+LL7WxIFhz4eJPi31eekBkdYnBw0WfSO9/0cdhxUcGnl8DonV8+eohP0SJZNxJivzeBohHhSCbLGUdT1Gq4jIxdZ0UDS5sBefePlk+bs0De/7oju0CIxKP0ORvwhf1EYgGCMQCBGPB1HyNp4Y9LXs43n687wDwbtj1dpbkLWFhzkLKMsootZdSaC1ErxFCv6IoZBgyJqfjJuWUOofT2u9Kf77IHV+nVIsnxIuHmnj+QBPbT7tSQeRJDFoNNywq5AOry1laOopljxKJZFhIUUoikUgGQqgTmrq+EJxPlAJRwlc8FFFqBFxJli6n1EhkSnV3Fox0plSSm38yOscdSUxZ4kN4sGPkjhlsT5cQlV2SXn7Zl+EfHxO5UN2vKRBi0m2/hIfWiq6IO34jll/yOTBYhOC09acipwZEyd7ZrboHI0hJJBcCyfI9NSFEdnuRaCYAkDeOolT+QvHaEuqA05th4bvPvX3yPWgw3foucOKJOFWdVZzqOEW9r76XmykQDVDvq6fZ34yKeo4j9STLmCUCxE1OimxFqWDxGVkzmJk1E40yAV29I8FAyveSLilFI9y6Y0xDR5CNB5t44WAjO2vaUbv9WU16DbMLMphXaGdeYQbXLSyUHfIkkkmA/GQqkUgkA6H2bfGFRmuE4hV9b2O0iV+w/S0i7Lx42eDPMxKd7kbSKZV0bmkNYJvCv8wnw85H0ilV9w6gimuqaEl6+axr4au9Oy6lcM6Aa74Jz/2ruG9xwoqurCiDBdZ+ETZ+Rdxf/emRG69EMlmxF4kv0GpCCO3RUDqHbTydUhqN+JHj6LNwetPARamChaM/tgmGqqq4Q+5UGV2Np4b9rfvZ37Yff7LceYBoFa0IFNebsegsWPQWcsw5LMldwpK8JSzIWYBZZx6lRzLB6S5KqapwCp9NMuQ8e1rfUQajQJ07wAsHG3n+QBN76zp6rHNaDVwzv4DrFhSweoYTvfYCFQwlkgsYKUpJJBLJQOieJ6U39b+do6JLlKoe/DlUNe1MGo4rydolSvlHQJRKimSZJRMz72msSLbJHslMqWSeVPHywX+wX/EROPEyHN8Il35JCKKpdfeKzmJWJ5TJsjuJBK1OCFOeeuFG9TaJ5WYH2M9TMjfaVFzeJUq9SQ/Lx9moarpj4AUuSgWiAfa37WdPyx5OtJ9ICVHnEp+cJmeqO12uJRetIjKc9Fo9JTbhciq2FZNpzESv0csyrv6wdGVKJWIQ8fXdbTYVcj66nfdOtfp4ocsRdfCMp8e6XLuR6xYUcN2CQlZOy0YnhSiJZFIjRSmJRCIZCNVbxLS/0r0k2dOgbvvQOvD5WtK/3o9U+V5/v3QOlGQ54WiV7k0WRsMpdXae1GBQFLjzz9BypPcXVJ0Rbv358McnkVxIZJUKUaqzDqJBsSx//vBeH0eCZK5UZ50oyaWfwOXO+nRJVcHYdd5TVZUmfxP72vYRS8Rwmpw4zU6seiuBaAB/1I8n4sEVdOEKufBEPGQYMlKlbyqqyG+KBlFRU86keCKeChBv8Dfgi/gIxoL4o37qvHXE1Xi/Y8q35FNqL2Wecx5L8pawOHcxeZa8MXtOLmi6Z2EG3H2LUqmQ86GJUqqq4gnFaPOFafdHyLIYKHWYMeq0tHhCPL2vgSf3nOFQQ08hqjDTxHULCrl+YQHLyrLRaKSwKJFcKEhRSiKRSM5HyAONe8X8eUWpYXTgS7qSIJ2BMhSS5XvxSP+/dA6UZPneaIScTyZMWWI6UplSsTCc2SXmh+pm0upltoxEMlCSr6mddeIHABjf0r0kubPTZd+nN0HJhr63S7qk9FbREGGEiCfiNPobqfPWUe+rp8nfRCAqQsDbQ+0cbDtIS7BlxM43ULSKlrmOuczPmU95Rjml9lLK7GUU24sxaqdmRlAoGqfa5SfTrMdpNWLQDd8d1BmKsfNAI2+ddFHrDnDlTDupliPBdsgu771Tl1OqRlPCU6+cIN7l8FOAbIueHLuRHJuRTLMei0GLxaCjxRvizeOtbDreyp7aDsKxnmHyigIFGSaaPaEeYeWlDjPXLyhkw4ICFpdkSSFKIrlAkaKURCKRnI9UnpQBSvrJk0qSDJXuzykVcMNvrxFfKt73l56/0icFIGse6IeRZ2HNSc/724YnSnWOQPD6hcBIO6WaDgrREKB05cgcUyKR9E/S7dlZn87EmQiilKIIt9TBv6NUD0CUKlgwpFJqtVtpYCwRY3vTdl6ueZnXal+jI9xx3v3tBjs2vQ1X0EUkEemxzqq3phxUmYZMPBEPbcE2XCEXGkWDWSeymwDR8S4WABWK7cWU2EpSZXVmnRmzzkyJvYSFOQux6PtxjV2ghKJx3jntZtspF6oK5U4LZQ4LnmCUFw428drRFnzhWGr7LIue+UUZXFqZy6WVOWRZDBxp8HC40UOrN4zDaiDXbsRpNaTK2+IJlRqXnyONYrsTzb4e8e9bq1zcbdRjUqIcrKphXsHinkJQxA+d4rPKF14Nskc9PiKPXVWhsVM4xXNsRm5eXMStS4tYWJwpSy0lkimAFKUkEonkfPTIkzqPWJR0SnnOCDfM2VlBx54H1wlxaz/d8xfvlCtpmKVyZkd6PuDu3X1tMCTHNNXL90Y6U6pht5g6pvcsl5BIJKND0inVckS8PsP4dt7rTsWlcPDvogPfmn5ypQYQch6IBjjefpxj7mMcbT9KnacOV8hFW7DtvMKTXqOn2FZMka0Im96GRW/BqrcyK3sWS3KXMC1zGhpFg6qq+KKi1M6is2DWmdFqtEN84FMbVVU53uxj84lW3jzeyjun3b0cROeiIxBly0kXW066+K8Xhj4Oi0HLxdOdFGeZee5AIx0xGwW084sXdnJgWwZ3riwl06zncKMHf/Vufty1X5VaxNzCDHLt4nNOIqHi9kdo84Vx+SPEEz2v5em5Vi6flcuaGTmUOizk2AxkWQy4/RFq3X5qXAFybEYumeGUGVESyRRDilISiURyPqrfEtPyNeffNnta14wqBJ2cyp7rq15Pz9ds6ylKjZQrSWcAYyaEO0Wu1FCJx8DTMDJjmuyMtFOqYa+YFi0dmeNJJJJzk3wNSwpSKJA3Z9yG04OuXCnF34K2vQry++h02iVKRfPnEwh34goKsak50MzBtoPsbd3LMfexc2YxdUeraFlZsJKry69mTfEaCiwFAxKXFEXBbrBjNwzDgTvFaegI8vDWap7ae4ZmT7jHOp1GYVlZNmaDllp3gPr2AIqicFllLtctKOCyWbmEonHafGEaO0NsP+Vi88k2TrWKEHiLQcucAjuFWWbauwQitz/SoySuKMvEnIIMZufbKLEkuGJRBSaD+Er4wI1zCf8kFzztZCk+at0Bvv/isdS+N2uOgQHaNdn830fWs2ams08nUyKhEozG8UdiBCNxjDotBZl9N4nJtRvJtRtZXu7oc71EIrnwkaKURCKRnIvWY2lXy4wrzr+9LU9kfkT9ooSvuyiVSMCpN9L3a7fC0rvT90cyVNzqFKKUfxiilLcBkl9whuvemuyMdKZUwx4xlaKURDI2nJ3T55gOBuuon1ZVVVqDrbiCrlTpWiAaEOHfsaCYjwYIFJYSjPjxHPwekcYygrFgt/U+AhlxgpmlRA79EA798JznLM8oZ3b2bKZnTSfXnIvT5CTblN1DdCq3l5OVfF2TpIjGE5xpD2LSazEbtFgN2hFx7aiqyv76Tn6/5TTP7m8k1k0lKndauLQyh8sqc1k9w4ndpE+tiydUEqqK/qwxlDosLAWuX1gIQGNnkFA0QZnDgnaAuUuJRIKWlpYe2VRGnRZjdh54jvOlS3PJYAZP721Aq1GYX5TB+8MhqIOssgWsrczp99gajYLVqMNqlF81JRLJ+ZGvFBKJRHIudvxGTHNmDyyQWlGEW6rlkCjP607L4Z7OpZptPdePZH6TxQnuUxBwDf0YydI9RSvaqU9lUk6pjuEfK+KH1iNivmjZ8I8nkUjOz9miVP68IR+qM9xJnbeOOm9dSmwKxoKE4qHUNrFEjNOdpznmPkZ7uP38BzUpYLJB8DTU95FJqO3tYtIpOpxmpyixy1vCktwlzM+Zj1U/+mLbhYKqqnjDMXZWu3n+QBMvH26mMxjtsc30XCvLy7JZVp6NVqNwuMHDkUYPoViCiyscXFqZy4pp2Zj02l7H7ghE2XioiUe213DwTLqbXFGmiXsumcaGBQWUO/v/e2k1ClrOLzIVZg4jh/JsusrVsxU/X7l2Dl/Z0M1R+JcfAaDkzh6580kkkimPFKUkEomkP8Je2PuYmF/1sYG3DndUCFHq7LDzpEtKZ4ZYENxV4G0Ge74oC2s7IdY7Zwx/7JauXzCHU76XdG5lFIN2ir9dJDOlogGIRUSJ5FBpOiCC81Fk9zyJZBQJRAO4Qi7aQ+3CoZSZQygWIAFgtaJWPYM/6qcl0EJrsBUgFdht09sIxUMEogG8ES/1vvqUEOWNeIc0HgUFi96SymJKzevNWHxtmOt3YdYYsKy6D6velt7m9FtY9j6OObMUyx2/w6K34DQ5yTBmoFFk9s65aPGGONTg4XCDhxPNXjyhGP5wDH8kRrs/SqsvTOQ8OU6nWv2cavXzt131vdbtq+vgl5tOodcqZJj0WIxazHot3lAMly9CJN7z2ItLs/jwmmlcv7Cwl/tpwmDpKqPry2md/JySI0UpiUQyckzxbxkSiURyDvY9DhEvGOyw+K6B75fMlWqv7rk8KUoteg8c/AdEfFC7DebfCidehkRUlP6VXTL8sVucYjoSTqmpXroH6fI9EAKiLXfox0qW7uXMGl5nRIlkEtPsb2ZPyx7agm20BdvwRX2UZ5QzK3sWM7Jm0BHq4Gj7UY67jxNJRFJikd1gJxTrEouiXhp8DdR6aqn31ROKpZ1KqS5v3XFYgK6Obq6t8NbWYT2GXHMuuZZcLDoLFr0Fo9aI0uVqURSFYlsxcxxzmO2YTYGlALPO3H8nsfYa+LEQqRMlG9AUdAthP/QKBINQuRRy+w86n+okEirbT7vZfKJVCFFdXegGgk6jcMnMHK5fUMCls3JRVZVgJE5HMMr++k5217azp0Y43uYVZTCvMANFUdhyso09dR1E4youfwSXv/exzXotty4t4u6LyllQnDmSD3l0SDqjvQ09l8dj4Dop5s/Oy5RIJJJhIEUpiUQi6QtVTZfuLb5rcOJBSpTq5pSKhaFmi5ifeZUo1at6LS1KHXu+a9160PcdBjoorF2ilH8YolRnUpSa4iHnMDqiVLEs3ZNMPY66j/LwoYfZeHojMTV2/h1GCJPWhDkexRwNo0EV5XwaPWa9mTxzHjnmHBRFwRV04Qq58Ef9wqnUJTgV24optZdSYi+hzF5Gib0Es24ES6ayylAzilE8Z8T7QndRqumAmBZIZ2VfnGzx8o/dZ/jnnjM0dIZ6rTfqNMwpsDO3MAOnzYDFoMOs1+KwGsixGcmxGyjOMvfIcurOymkOPkLfXWz/5epZeEJRDtZ34g3HCERiBCJxbEadCPC2GSnJtmA2TKIOhRldopTnLFHKc0b8eAbgnDm2Y5JIJBc0UpSSSCRTl5ptsPkHcOX/g8LFPddVb4bWo2J+1ccGd1xH14fX9moRbq7RQP0OUfqFItp/tx4TolTNVlEOduJlsc/sG4bziNKkyveG45QaweD1yY4pIz0/3FwpGXIumWJ4Ih5erXmVZ049w46mHanlmcZMim3FOE1OzDozpzpPUd1ZnRKrHCYHcxxzsOqtuIIu3CE3vqivh1hUYC2gzF5Gqb0Um96WOrZBayDXkg751ml08PyX4Z1fgd4CHz4sXpsnCooC5ZfAgb+h1G6FVR8Vy4Md0NKVQTeFyn1VVSUcS9DQEeRIo5ejTR4aO0PMKbCzcpqDmXk2XjnSzCNv1/JOtbvHvivKs1lalsW8ogzmF2UyPcc6ImHl/ZFh0nPJzP5DvycdGcVi6mkUP9Al3X3JzpWKBuyF4zM2iURyQSJFKYlEMnV56QE4s1PkOn18U88vKO/8WkwrLoPBBnpmd4lSsRD4msSvjsnSvaKlYM6G8q7Q9OaDcHwjhD3ig96sa4f1kFKkyvfOkykVj8HBv8P0K0S2VZKwDxr3inlH378QTym0ejDYRMnlcESpkCedySFFKckFRjwR54j7CLubd9MSaMEVctESaGFPyx6iiXR49FzHXO6Zfw/XTLsGvaanOyUSj1DrqSXLlEWOeYS/6OfMEtPCJRNLkOpCLVuNcuBv4geTpBhQ9Zrogqq3QOlF4z3Ec9LYGeRwgwdPKIo/HCcYiaPXKlgMOixGLXqtJhXZHU+oBCJxApEYnlCM+vYgtW4/te4AHf4o/kiMbg3qzsuMXCu3LyvhliVFlGRbRuXxTRkyugSnqF84g5OZip1dopS9UOZMSiSSEUW+okgkkqlJe7UQpACaD8Dhp2DB7eJ+4z44+pyYX3Xf4I+dWSoEJjUhztNdlJq+TkyLl4NGL6zwr31TLCtbnQ4YHS7Wri9z5yvf2/w/8MZ3RFnIfW+mv6jtfUR8GNVbYfZ1IzOmyY4pU4hSwY6hH6NxH6CKjob5C0ZqZBLJmOKP+tnVvItaT60IEI8GqPPWsb1pO53hzj73seqtXFl2JbfMuIWVBSv7zVYyaA3MzB6l0qDF7xXdL0dK/B9puvIEFW8DdNSIUvATL4l109eBzjhuQwMIReO0+cK0esWtpWta1epjd017n6VzI0GmWc/cQjv5GSYOnOnkVKsIbjJoNVy/sIC7Ly5nRXl2/3ldksGR0a3brqchLUolnVIZU7wbr0QiGXGkKCWRSMaO9mp4/n646D6RqzSeHHqy5/3Xvw1zb4Z4BP7xUfHLdN48mDUEQUZnEHklHbWw7zHhWjqzS6ybcYWY6s0iU6huO7QdF8tmXz/0x3M29gIxDXeCq6rvjn7xKOz8nZhv2i/C1xe9GxJx2PZzsXzZB4WzSyJypTxnhFg3VJKle3nzwCB/zZdMXEKxEK6Qi7ZgG66gmDYHmtnVvIt9LfvOmQc1PXM65Rnl5JhzcJqdzM6ezdritZh0I5CXNxyMNlj7hfEdw7nImUXClI0m1C7cUpll6dLuymv63S0QiVHnDlLrDhBPJMixGcm1G8mxGbEa+/6oH47FqXMHqXH50WoUlpZmk2npnakUiSX42646fr3pFNWuQB9H6onVoMVhM2A16DDptcQSCeGICseJdutEp9EoWAxaLAYdVoOWoiwz5U4LZQ4LOXYjNqMOi0GL02okP8PYQ3By+cIca/YypyADh3UYnVAlfWPKEs68aECEnefPE8tTolTxuA1NIpFcmEhRSiKRjB07fgsnXhQOpc/vF18QxouDT4jpnBvh2Auio8y+x4R41HYctEa44zdDt6jnLxSi1O4/ihuAzgwlq9LblK0WolSSOSMoSuUvBMd0cJ8SwtO13+69zdHnRHlhktcehHk3i+ejo0a4vS7+5MiNabJj6uqaNJzyvVSe1JLhjkYiGRDheJhANEAgFsAX8eEKuUSYd9BFva+eWk8tdd66Hp3qwvEw/mgfbcS6odfomZk1E7vBjllnJsuYxcqClVxceDH51vxz7ivpB0UhUrgC0+mXRWOMnFnpEuwuUSoci3OsycvO6nZ21rjZXdNBk6d/h5LFoCXXbiTbYiASSxCMxvGFY7T5wqhnlcfNzLOxuCSLoiwTuXYjkViC3711uk8HVLZFL4K87UaKMs0sLctmeXk2lXk2NJrRdSw5bUYusY2va+yCRlGEG8p1smfYeXJeilISiWSEkaKURCIZO9ynxDTggu0PwWVfGp9xuKqEMwjg0i8KJ9Puh+HFfxfOIoCrH4T8+f0f43zc9CPImQn7/pIWfqat7dlZr/wS2PIjMZ87V4hII4VGAys+Ai/9O+z5M6x/QLizupPsLlh2iRAKO2qFgHXg72L5vFsgu3zkxjTZSZYwjIRTSuZJSfpAVVU6wh0AWPQWDBpDD4dINB7ljO8M9b56GnwN+KK+lOAUiIqbP+anI9SBKySCwYOx4LDHlWXMwmly4jQ7meOYwyVFl7Asf9nIdp+TAKRFqdptqTKpdvss/vOFFo42VlHV6iPWT9iS3aTDoNXgDkRSglMgEqfGFaCmH5eT1aAlmlCJxBKcbPFxssXXaxu9VuHOlaXcvqyEggwTOTYjBt3Ey+SSjCD2wt6iVGe9mGZKUUoikYwsUpSSSCRjh/t0en7rT2DlR9Nf9MeSpEsqexoULQNbvnBJJQWpmVfDRR8f3jlseULYWv//4NTr4gvG0vf33Kb0IkAB1NHJbVryPpFXFeoQ5YpL3pde13pMdBgEuOJrwh319s/h1Qe7ugQCl3x25Mc0mUk6pYaaKRVsh/au/wEpSk1Y4ok4zYFmajprqG6ppixWRo4lhxxzDg6TY0C5Naqq0hnu5IzvDK3B1lT5W1uwLeVUiiaiOEwOnGYnFp2FU52nOOo+ijuU7iSmUTToNXqUrnjocDyMyiDSn/vAqDWmBKYiW1Gqc12GMd1hUq/R4zQ7yTGJx6zX9i7rmkp0BCJUuwLUugPUuQN0BCL4I3EC4RjRbgKRVlFwWA3k2o04rQbiqkogHMcfiaFRRLma1agjx2bkoukOMky9n1df3nIyAFwncW39I07g0fY5/LO1ocd203OsrJiWzYpyB7ML7JQ7LWSa9SiKQiyewB2I0OoN0+YT03Z/BKNeI0LHDVryM4yUO604rQYi8QSHGjzsrmnnSKOX1q7cKG8oyhWz8/jkuhkUZUkBckqR6sB3Jr0s5ZSSmVISiWRkkaKURCIZG1Q1/YUchNvk7f8TgshYc6hLlJp/u7CpZ5YIgezt/wNrLtz6f+kWyMNFq4PKq8XtbMxZwo1U9XpvwWoksDhgwR0itHzHb3qKUsksqZzZwsGVPx/2/El0AQQoXyPC2CVpTFliOhSn1Kk34OX/EPMa/fBceJIBoapqnwLSqc5TbGvYhkVnwWl2km3MptZby56WPexp2cOpzlPEEn3nJZl1ZkrtpZRnlGPTp8uPo4kowViQQDRAR7iDem893qh32I8hoSYIx8O9lpu0JopsRWQaM7HoLFj0ltQ0WUqXY87BaXKSacrEqrNi0Vuw6q1YdBYZCN0HnlAUty8ico6MOlq9YV442MjGg03srx+GO7IftBqFZWVZLCvLps0Xodbtp8YVwOUNstdoxq4EcUYbAXhbs5zblhazsDiTOYV25hZkkH2OLCWdVkOe3USefWAZXkadlmVl2Swrk/mBki6SwpNHXIPEwuBv6VpXMj5jkkgkFyxSlJJIJGODrzntwFl0F+x/HLb9H6z6OFidYzeOlqPQcljMJ7vtAaz/unA3VV4jpmPFex6GRGL02pOv+IgQpc7sEqVjRUtF96m9j3Wt/7AQ4CwOEQD86oNi+erPjM54JjNDyZTqqIVnPi/auidZ92/j3kXrQqMt2Mbelr3sa91Htaeaem89Z3xnMGgNLM5dzJLcJVj0Fp479RwH2g4M6Jg6jY5MfSa+mC8lDAVjQY63H+d4+/EBj81usOM0OVOh3zlm4brSa/Qp15Qv4qM8s5w52XOozK7EoDWIUryov0eguElrothWTI4554IRlhIJlcONHvbUdeALxQhGYgQicaxGXSqzyGrQpX4niCVUgpEY/nCcQCSWciwFo3EKMs0sL89mXmHGgMvLTjR7+c3m0zy55wyRbkHcfZFnN1LmsOC0GbAadVgNOvTa9Hmi8QQuf5g2bwSXP4xeq0mFeSdUVQR+R2LUugOEogl2VLezo7r9rLNo2Z2o5HKtKDGP6DN46IufwGoe55B4ydQiJUp1uaO8jb3XSSQSyQghRSmJRDI2JEv3FA1s+K5ocx10w9YfizK3sSLpknJWQv6C9HKDBdb+y9iNozujJUiB6PBXuAQa98Lbv4Ald8PBv4tSRb0FFt+V3vaiT0LdO2DMgFkbRm9MkxVbrph6Gs+9XZKQB/58R7q74vQr4OpvQOHi0RnfBYiqqlR1VLG9aTuuoCu1PBQPibDukIsGXwN13ro+9w/Ggmyq38Sm+k09lhfbitFpdEIQivrIMGSwNG8pS/KWMN85n7KMMvJMebjaXOTm5hKIB2gLtlHnraPWU0utt7aHg0mraFMuJJvBRrGtmBJ7CSW2Eix62WWxO/5wjKNNXo42edhZ3c7mE620+SIjeg6jTsOKadm8d1UZ184v6CEcJdlb18FPXj3Ba0db+j1OcZaZ6xcWsGFBAfMKMzEbtCMyvlA0zs7qdt483sLRJi/5GSbKHRZKss3YlTArXDfAW0KUMsy+BoMUpCRjzdnle51dU0Wb7u4rkUgkI4QUpSQSydiQLN3LKOly5fwLvPx12P4rWP3Z9Bf+81GzVXwoKrto8GNQ1XSe1ILbR65EbyKjKLDyI/D0Z2H/X8QtycJ39cz0MljgfX/pdQhJF8kg+vbTkIiD5hxfUBMJePITQpDSmeA9f4JZ/bd0n0w0+5ux6C3YDfY+13sjXt6oe4N9rfuw6W04zU4cJgehWCiVqRRLxDDrzFj0FoxaYyozSUVNlcG5Q252Nu2kJdi/aNAdi87CotxFVGZXUmIrocRegjfiZV/rPva27MUVcrGuZB23zLyFhTkLU06jcDyMXqNHo/QULhIJ4ZpRFAW7wY7dYKcis2KIz9rUprrNz9P7GnhufyPHmvsua6zIsZJjM2Ax6DDrtfjCMVq9YVp9YYKReGo7rUbBbNBi7XIgJcvtjDoNp1p9VLX6CccSbDnpYstJF3l2I+9aXsKMXBs5duFQ/N1bp3nzeGvqmLPybXx07XTWzcklHE0QiMTRahRm5FpHxZFm0mtZW5nD2sqcHssTiQQtLS2Ys9bCW10LKy+M1w3JJCOjUExDHRAJpMUpe8G53/skEolkCEhRSiKRjA3JznuOaWK68qMi7NzfKgK2r/rP8x+j7QT84UbxgeizuyGrdHBjaDkCrhNifv5tg9t3MrPgXfDat0UXQEULhYtEx71LvzjeIxt3VFWlxlPDIdch8ix5LM1bik7Tz1ujY4aYxiPiA3pWWf8H3vwDOPacmL/px5NekFJVlc1nNvPbA79ld8tuQHRkK7GVkGPJEQKTzkJzoJm3G9/uN5NpqBTbiqnIrEiJVwatIRXWnWPOYUHOAmZlz+rzb3fD9BvOeWyjVpZSjgaxeIKNh5r43Vun2V3b0Wv9NKeF+UWZrK3M4bJZuRSPUJB2uz/Crpp2ntp7ho0Hm2jxhvm/N6r63HZpWRafW1/Jutm5E6scsni5yPuL+Cf9a4dkkpLRrcOetzEtSmXIznsSiWTkkaKURCIZG5Lle0m3icEicote+Q9459dwyeeEg+pc7H0E1DjE46IMb83nBzeGI0+LqbMScucMbt/JjMEC970h8o0KFoDBOt4jGlfiiTiv173OP0/+k72te+kId6TWZRmzuLzkchblLiISjxCIBfBFfF3ZP224iwuJqyq8/DEw2sgyZlFqL6XEXoLD5CAYCxJsPkB076MsNplYuege9N1LJCcJbcE2jriOUOeto95Xz7aGbZzsONljm45wh3juXL33t+gsrCpcRTwRxxVy4Q65MWlNPfKUgrEggViAUCzUY1+z3oxVZ8VmsDHPMY9Lii6hNGOQAvQUJhQVLp/uJWuJhEqLN0yTJ4Sqim5xKhCKxEUmUyRGLJ7uIheOJWjzhWnzhekMRsmzi05t05xWrMa0S8Jq1FHmsGDSa7v2i3Oyxce2Khd/2FpNfXswtW2pw8wti4u5Yk4ecwrsWI2j8xE022rgqnn5XDUvn2ZPiMfeqWXT8VZafSLrKRiNs6I8m89fVcnamRM0m0tnhE+8BWoC9LJ0TzIOWHJEU45EVAhSyfK9TClKSSSSkUeKUhKJZGxIlu9ldyt/WfkR2PIjCLbD9ofO3YkvEYd9j6fvH/jbEESpZ8R03s1To3SvOxmFaTv+FCUcD/Pcqef4/cHfU+2p7rEuy5hFZ7iTjnAH/6z6J/+s+mffBzF0tXAPNEBXbv87Te/03i5bhKJbOzax5o1/5QPzPsCSvCUj80BGAVVVOeI+wpv1b/Jm3Zscch3qc7t1pev40PwPYdVbqffWU+etoz3cTjAqBCaD1sBlxZdxSfElU8aBpKoqqgoazci8pvTXNbA/TrX6+J+Xj3O00UOLN4w3JFxqWRY9OTYjGoVUsPZoUZBhwmrUUu0KEE+kxS2tRuH6hYV86JJpLCvLGnMBKD/DxBeumsUXrpqVWhaJJQYcgj6u6PrvrieRjDoajfjM0FErws6TgefSKSWRSEYBKUpJJJKxIVW+102UMtrh4k/D698SotTqT6c7nJ1N1es9u780HRCd9PIG6HhyVUHzQTE/96bBj18yKfFEPGw5s4VXa19lc/1mAjGhJCkoXFV+FevL1rMkdwnFtmJcIRdv1L3Ba7WvUe+rx6KzYNFbsOqtOE0iF8l56Bn0LYeh4nLUebfQFmpLiTOd4U4sGj3mxgNEFYWDJhP+qJ+Xal7ipZqXWF+6ns8v/zzTM6eP6ePf37q/RxC4goJJZ8Kqt6KqKtsat7GpblOv7CaT1kSJvYRSeynTMqZxy8xbmJE1I7V+jmMKuQ27EY7FOdTgYVd1Oztr3Oyq6aDNF051Wcuy6FlUksny8myWlWWTn2HCYtBi1GnoDEapdQeocQVo6AjS5gvT6g3T5ot0TcO4AxGyzHrKnFbKHRbmF2Vw7fwCpuX0dDhGYgl++WYVP339JJFYb8GpIxClIxA952Mx6DSpbCa9Ni0Y6bUanDYDuXYTGSYdTZ0hql1+6tzBPjvUNXl6ut0KMkxcv7CQD6+dRkn2xAp6nxSClEQyEbAXdROl6sUyKUpJJJJRQIpSEolk9Am2ixuky/eSrPqYyJYKdYoyvsu+1Pcx9j4ipjPWQ2e9CJA++HdY/8DAxpB0SWWViW50kguS9lA7T5x4ggNtBzjqPsoZ35ke6/UaPTfPuJkPzf8Q0zKn9ViXY87hXbPexbtmvav/E7Q2Q9U7EIrDnDt7r9/+S9jzKlhy6PzA62xu2sbfjv2N3S27ea3uNd6of4MZWTNS2Ug6jS6Vx5RpzGRBzgKW5i1lVvYsgrEg9d566n31KCjkmHNwmpwoikKdt446bx2N/kYC0QCBWIBgLEhCFYKBqqpUe6qp6qhCRe09zn6YmTWTy0su5/LSy1mUswjtBR5oG0+oBKNxLHptv06n9kCEPbWd7KxpZ1eNm331nX2KQIFInEAkTpsvzMkWH0/s7nntaRRIDPBP0R6I0h7oYF9dB0/va+C7LxxlbmEGq6c7iSUS+MNx9ta1U9XqB0Q+00fWVlCUZSbPbiKaSNDWFRKeSKiUOixMc1opyjKj6/Y4B+vuSrrCknQEo9S4/NS4AnhCUWbm2ZhbkEG2Vbp8JJJJT0aRmPZwShWN33gkEskFixSlJBLJ6JPMkwLIntZznTkLLvo4bPp+WpzKXyDCuHPniDK7YDsc7QqNXnK3cD298R1RwnfFvw+sFC+ZJzV3CpbuTQHcITcPH3qYx44+RjAW7LHOrDOzpmgN68vWc1nJZWQa+3HjDYSkqJp0/p1NsrvjvFvItDi5cfqN3FBxA2/Wv8mPdv2Iqs4qTrSf6Pfwz556FhBi1UiFhes1eqZlTkOniLf8hJogFA/hj/oJx8MszFnIZSWXcXnJ5ZTYS0bknKNFizfE03sb6AxGuXpePguLM/ssCUskVJo8IWpcAercAWrcfs60B2ntcia5fBF84RjhLnEpx2Zkw4J8rl9QSF6Gid017eyodvPOqVZq2sN9jqUyz8aKadksL3cwzWkhGI3jD8dp7Ayyu7aD3TXtnOlIX4vdBakcm5HibDN5diM5NiO5diO5NgO5diPZFgMuf6TLUeVn84k26tuDHGn0cKTR02MMOo3CJy6fwWfWz0zlOo0miqL0ePl0WA04rAaWlmWP+rklEskYkxSg2k+LpjQAmRP7PUIikUxOpCglkUhGn2SelDVPlOydzcWfgrd/IQSprT9JL599Pdz4Izj6LMTDYMyEOTeIX+ze+A60V8OZXVCy4tzn76wX24Es3btAiCainGw/ya7mXexq3sWWhi0pMSrbmM01065hjmMOs7NnM8sxa+TyjZxd5Wvt1SLnrLuTqLMe6t4W8wtuTy1WFIV1petYW7yWN+vfxBVMJ4NHE1ER+B0N0BxoZm/LXmq9tT0EqWyj+MLfEe5IuZ70Gj0l9hKKbEXY9XYsegtmnRmNki5NyjPnsSRvCXOdcydtvlMklqCq1cehBg/PH2jkzeOtqcyin752khm5Vq5bUEhCVWn1hmnxhqlrD1DfT5lZf7T5wvz57Vr+/HZtn+tNeg2LS7K6RChRlpdl6d8NdO8aMXX5wnhCMfzhGIFInAyzjtJsy6BCvlVV5eAZDy8cbOREiw+zXovVqCXDrOf2pSXMLujjNVUikUiGS7JUL/n5CaRTSiKRjApSlJJIJKNPqvNeRd/rLQ744NNw+CloPiRuviY49jzUvp0WshbcDnqzEAaKlkHDbuGWOp8odUS4T7DlQ8mqEXlIktFDVVXO+M5wzH2MEx0niMQjgHD41LprqQ3WcqrzFNFEz7wch8nBh+Z/iDtn34lFP0o5No4uUSoegc66ns6/Q0+Kqb0Qylb32lWn0XFl2ZXnPUVbsI3j7cdxmpwU24qxGWwAxBIx2kPtxBIx8ix5F3Rp3UuHmvjpayc52uQhGu9Z85ZjM5JnN3K40UNVq5+fvX6yn6MIJ1Fxtpkyh4WSbAv5GcKV5LQasZt0WAxa9FoNO6rdvHCwiR3VblQV8uxGlpdnM8uhY938UuYXZw0pi8hpM+K0DU8QVBSFhSWZLCwZhsNPIpFIBktSgErGLyha8TlKIpFIRhgpSkkkktHH3UfnvbMpWS5uSQ4/Dc/+CwTaIOgWy5bcnV6/8N1ClDr4BFzzbdCe4+UsWbo350bRUUYyYTjZfpK/HPsLB9sOpnKROsOdqUDy81GeUc6yvGWsKFjBVWVXjZ4YlSSjGLRG4dxzVfUUpVKle7f2dFANkhxzDjnmnF7LdRoduZbcIR93MuDyhfmPpw/x7P7GHsvz7EZWTnNwx/JiLqvMRafVcKLZy5N7zrD9tBu7SUduVxlccbaZcoeVcqeFwkwTOu35/+cXFGdy75oKXL4woViCokwTqqrS0tJCXl4WGvm6IZFIphpnu6LshcN6b5NIJJL+kKKURCIZfZLle2eHnJ+LeTcLt8lz/yJCyouX93RELbgdXvwa+FugepMIQO+Lmq1Qs6XrmLcMbfySEaE10EpzoBlX0EVzoJkXq1/knaZ3+t3eqrcyK3sWdoNwyqmqilE1sqhgEbMdoiyvL/FmVNFohOOv9WhXrlSX88l9Woik0KN0T9I3vnCMGpdf5D25AtS4A9S6Auyv78ATEqWLV87J4941FcwttPfpNqrMt3P/hpHtANj9PKo68IB4iUQiueA4W5TKlJ33JBLJ6CBFKYlkMhINibK1zOL+xZiJxPnK9/rDlgvv+ZPotGfL7xlQbi+Aisvg9Jtw4B99Pw+JOLxwv5gvXyO2l4w6qqrij/ppC7ZR1VnFtoZtbGvYRq2377yemVkzuWH6DWQbszHrzNgMNioyKyi2FffISEokEl3Olbzxda44ZnQTpbpIlu5llkLJyvEZ1zgTjSeocfnZUS1Cwg83ePB1ZSkFI3HiSZFH5Zx5Tw6rgf+8eT43LSrsM8RcIpFIJGOALR8UDXR1dU1lTEkkEskII0UpiWQykUjAwb/Dq9+EzlrQmeD+U2CwjvfI+icaBG9XK+HBOKWSKArkzu573cJ3C1HqyNNww/+A3tRz/e4/QtMB8aFqw3/JrnsjiKqqPQSDjlAHL1a/yLOnnuWo+yiheKjffe16O06zkzmOObxn9ntYkb9icokPzq7r2FWVXpYs3Zt/66S/zlRVpcUbxhuKYTFosRp0BKNx3jrZxpvHW9lZ7UYBzAYtFoOOUDROmy9MeyB63mOfTbZFT5nTSpnDQrnDQrnTwlVz88m29h8iLpFIJJIxQKsXwpS3q5xahpxLJJJRQopSEslkIRaGP98B1Zu7LQtB4z4ov2T8xnU+2qvT8+fKlBoKc2+C574IYQ+ceEmU/CUJtsNr3xTzy+6BwkUje+4pSCgW4o26N3j21LNsadiCSWsix5xDhiGDw+7DPTrGJTFqjSzPX87qwtWsLlrNtMxp494Jrt0f4Uijh2pXALtJR47NSK7dACgEIsLZk2MzMjPP1vcBkuKqu0uUatgLzQfE/MJ3j/bwh0VScIp2OZUisQQnWnwcbfRytMnD6TY/Na4AwWh8yOcoyDCxssLBktIsnFZDl3ilRatJi3UZJj1lTgsZJv2wH5NEIpFIRgl7YVqUyiwZ37FIJJILFilKSSSThRMvpQWpRXdC3Tsiq+nMroktSiVL94wZosveSGLOgspr4Oizopyxuyj1xn9DwAWmTFj/9ZE97xRAVVWCsSANvgZ2Nu9kR9MOtjRswR/1p7bxJXz4or7U/QxDBtdOu5Z1pesosBaQY84hy5jVowRvPEgkVLafdvPUnjNsOtFKY2f/Lq7uXL+wgH/bMJcy51nh6ckOfO3VEI/B7ofF/cLF4jZIOoNRjjZ6aPWFmZlnY0auDf0AwrmTROMJ9tZ1sKPaTZbZwLyiDGbn29Fo4Ex7kBp3gGNNXnbVtLOntp02X2TQYwTINOu5tDKHtTNzsBp1BCIx/OE4Rr2GHJuRHJuRoiwTBRmmyeV8k0gkEknfZBSl8xKlU0oikYwSUpSSSCYLJ14S08pr4PZfwT8/nRalJjLJ3B1HxeiUNS18lxCljr8IoU4hQjXsgXd+Jdav+xpYnSN/3kmOqqqc9pxmT/MeDrQdoCXQQluwDVfIhTfiJRQLodI76NmgMbCudB3XVVyHQWvAFXThCrmoyKzg0uJLMWgnTtlVRyDC7946zd931dNwlhCl0yiUOSz4IzHafBHiifRj1SiQUOH5A028criFuy8u4/JZucwtzCDPbkRxdolSiZjIltr/N3F/2T0DGlc8obL5RCtP7D7Drpp2znQEe6w3aDVMz7ViMfTd5UirUbAYdFiNWkLRBO+cduML93SpaRRQgYFkdduMOuYU2JlTaGdmro3yHCvTnFayzHqC0TiBSIyECjNybT3cThKJRCK5wOmeI5UhnVISiWR0kKKURDIZUFU48YqYn3m1mBYvhz1/njyi1EiX7iWZtQEMNoj44Miz4v5fPgBqHPLmw8qPjM55JyjReJSj7qPsbd3L/tb9tAZbCUQDBGNBool05o834sUT8QzomIXWQlYWrOSiwou4ovSKVDe8iYonFOW3m0/zu7dO4+0m1lxU4eCmxUUsLctiZp4No06IPomESmcwiqKAxaBDr1XYeLCJ775wlFp3gN9vqeb3W6oBEcJ9RaWT72mNaONh2PwDiHhBb0mV7tW4/LxxTDiyWjwh3IEIBq0Gq1GHTqOw6UQrzZ5wjzFrFMiyGHD7I0TiCY42eQf1mBUF5hRk4A/HqHUH6KaxYdZrmZZjZWlZFsvLsllcmoXdpOs6r0KOzdCvsyl7UKOQSCQSyQVFd3eUdEpJJJJRQopSEslkoPlQOiy8spsoBdBRC75W0aluIpFIwJYfpUub+gsrHy56s8iW2vcY7H8c9v8FOutEueB7/iiCOic5qqpyuvM0KmoqwykYC3Ki4wTH3Meo6qiizltHva+eem99D/HpfOSZ81ict5gyexk55hycZicZhgwsegsWnYVMYyYF1oJRfHQjR1Wrj0e31/K3nXV4QkKMyrbouXdNBbcvK6Yk29LnfhqN0itY+7qFhayfm8efttXw9L4GjjV5CccSuP0R/rG3kY8Z8pijqUt13asr2sAbe9t5cvcBdtd2nHesGgXWVuZy/YIC5hdlUplvw6TX0uYLc6TRw8kWH7F43zanWEIlGInhj8SJJ1SWl2ezZmYOjq7H4AlFOdbkRQHKnBZybUZZTieRSCSSwZMUojQ6sOWN71gkEskFixSlJBOX7b8UTqA7fgu5s8Z7NONLsnTPOVOUwQHkzRPd92IhUe8/69rxG9/ZeBrhyY+LzngABQth1X2jd76F7xKi1OlN6WW3/RJyZo7eOceAZn8zz5x6hqernuZ05+nUcp1GRzwR77O8LklFZgVLcpdQllGGRWfBoreg1+hREOKEXqtnrmMuxbbiSSlYqKpKqzfMoUYPhxs8bD7Rytun3Kn1dpOOj106nXvXTMM+xDBto07LRy+dzkcvnU4snqDaFWB3bTtP7TlDTW0+c6hLbfu544vZc+xg6n5xlpnZBXbyM4w4rAaicRV/OEYwEqcy385tS4spyDT1OmeOzcillblcWjl0kTnDpGfltBHOb5NIJBLJ1KP8EjBmQsWloOm7pFwikUiGixSlJBOTSABe/aYoi3n1G3DXI+M9ovHlZFfpXuU16WVavQhVrtsuSvgGK0qpqujop+/9xXhY+F3wmyvBc0bcv/jTcNV/gG4UO65VrANLDgTaxP3L7oc514/e+YZBQk2wuX4zLcEWzDozZp0ZgLZAGy3BFpr8TdR766nz1tEabO3zGN273JXaS5mVPYsyexkl9hJK7aXMdcwly5Q1ao9BVVVOtfnZVd3Orpp29tV3AHSFXYtua4FIHH84jqqqFGebKXNYKMk2E4zGafNGaPOFMeg0lDutlDstWAxa6twBat0BGjpCeEOxVCc8m0lHrs2I06qn2e2hxlPDkUZPn4Hd03Ot3H1ROe9aVkKmZeRccjqthpl5Nmbm2XjPilK8z6yCXTsBqNGWU2ueh0NR2LCggNuXFrO8PHtSin0SiUQikaTILIEvn4AJlBcpkUguPKQoJZmYHHteCFIAR5+DthOQUzm+Yxovgh1Q+7aYn3lVz3XFy9Oi1GB55vOitK7sElj0Hph/K5iHmSCjqvD0Z4QgZcyEd/02XW44mmh1sOR9sPUnInNr3b+N/jmHwIHWA3z3ne9yoO3AgPfJNedy04ybuHnGzeSYc3AFXbQF29Br9VRmVWIz2EZxxGlOt/l58VATO6vb2V3bjtvfVwe3weUgjQRGnYY5BXbmFWVy8+IiLp7uGBMxyF40G7r+7cqv/hS7Lr7m3DtIJBKJRDIZGc0fFSUSiQQpSkkmKvv/0u2OKsSGm386bsMZV069LkK79RYoX9NzXTJX6sxuIQgN9Mt4LAL7/yrma7eK2wv3ww3/A8s+OPSx7vq9EBQBbv7J2AhSSdY/ADOvFCLbGFvM/VE/m+s381rta1R7qgnGggRiARJqgiJbEaX2UuKJOBurN6b2mZ45nXA8TDAWRFVVnGYneZY88ix5lNpLKbGVUJZRxhzHHHSa9Et1pjGT6VnTx+RxqarKO6fd/HrzaV492tyrk1uu3ciK8myWlWVj0mto9YZp9UUIx+JYDTosRi2oUNcuHFBn2oNYDDpy7EZybQaC0TjVbQEaOoOoKmRZ9JQ7LJRkW8gw67AYdJj1WryhKG2+CK3eEEoizuJpTuYXZTKvMIOKHCs6rWZMno8e5C8UU51ZiLoSiUQikUgkEolk0EhRSjLx8LXAyVfF/Nyb4cjTsO9xuOLfwT45ApdHlGTXvYrLe5faFS8T06Ab2qvTeVPno3EvxLra0C+5W3StC3fCWz8auijVegw2fk3ML32/cF6NJTojTF83ZqfrDHfyet3rvFrzKlsbthJJ9OUcgrZgG/tb96fuL8xZyFdXfZWFuQsHfK5AJMbhBg9ajUKOzUiu3YhBqyEQjROIxPAEY7T5wrT5wniCMeYU2llUnDlksaaxM8g/9zbw5O4zHGtOu5/KnRbWzsxheXk2K8odlDrMI+JKCsfiRGKJ82Y/JRIJWlpayMvLQ6MZByGqOyXL4fZfQ1YZWGR+k0QikUgkEolEMhSkKCUZPGEv1GyF4hVgdaaXB9xCQMoqgxnrh378g/8QziBLDtz2ENS9A74m2P4QXPWfwx7+pCKRgJMvi/nKq3qvz64QJXfBdlHCN1BRqmaLmObNh1v/D1Z/Bn6xGtxV4KoC54zBjTMegX98RAhdjumw4b8Ht/8EpcHXwJaGLWw9s5U6b10qAyqSiLC3ZS9xNZ7a1qQ1sbZ4Lcvzl2PVWzHrRVbUGe8Z6rx1uEIuriy7kptn3IxGObegEorG2XbKxVsn2thZ7eZQg4dYov9Q877IMOm4ZEYOC0syye0SsnRahTZfmFZvuMt5FO4SsyLEEwkA4gmRF9XdFXVRhYP7LpvOFbPz0GhGvjTOqNNi1E3CAFXpkJJIJBKJRCKRSIaFFKUkg+fZf4EDfwONHmZvgDk3wak34NATohOcooFPvzP0DKh9j4vpgjvAYIWLPwmv/Afs+B2s/SKYMkbsoUx4mg+Ar1nMz+yjFE5RRAnfyVdECd/Cdw3suDXbxLR8tZjmzYXMUuisg+MvwupPDW6cb/8fNB0QLYPv+A0YxybnaKioqkpnuJM6bx1n/GdSOU2paciVCh4/Fza9jYXZq7kofx3vmX8ldqN1wOcHeriMTrf5eeNYC28ca+XtUy7CsUSPfRSFXuVz3dFqFJxWAxaDlmpXAE8oxsZDTWw81DSgMZ1NfoaRW5YUc+uSYuYVTaH/OYlEIpFIJBKJRDJmSFFKMjgCbjj0lJhPROHIM+LWHTUBm/8XbvvF4I/fekyUlgEsvlNMV9wLm34gyst2/QHWfG6Ig5+EHOvKIMqZDdnlfW+TEqUGGHaeiKeD08svEVNFEZ39dv4Wjm8clCil8Z5B2fR9cWf1Z9I5VxOAQDQghKZQGzWeGg67DnPYdZiqjip8Ud+AjuEwOVhTtIb5OfOJxqMEYgE8oRDt7cW8tT+bl/ZHeQn4H8NmlpRmMb8oo6sLnRGnzYDNKLKRVFR2nHbz1sk23j7lJhyL47QaybEb8IZi1LgCPc6r0ygsL89m9QwnK6c5WFKahV6rweUXTqdILIHVqMNq0GEz6cgy61MuplZvmC0n23jrZBu17gBt3q594gly7cZUCWByPsdmQN+t1K/cYeGi6U60o+CKkkgkEolEIpFIJJIkUpSSpIlH4cTLYHFC/jww2ntvc+hJIUaZsuDGH4qw7FNvQMECWPERiIdFV7f9f4HL7x94OVmSpEvKWQlFXXlJpkxYfg9s+5lwY10oolQ8CtpzZOioqnCkAcy5of/tkiJQ477zHxOg5bAQ+ECEgieZda0QpWq2ihLNvv7+fZCx5Vso0QBklom/+Siiqio1nhqOuI9wvP04x9zHaPQ3ptYn1AShWIhALEAgGug356k7OeYccs25OM1Ocsw5OE1iatNlYdcWYdeU4fJFqWsNcKTRy5FGDydbfF3ldNHUcQKROFurXGytcg348TR5QjR5Qqn7hZkm1s3O5fJZeayZ6ewzY6kw00xhpvmcx821G7l1aTG3Li0e8FgkEolEIpFIJBKJZKyRopQkzeb/hTe+k77vmC7K5ZZ9IL0s2RVv/m2w4HZx6048JsKy20/DWz8UHdgGSiKe7gi3+M6eneRmrBeiVNNBiIX7b08b8ohcq3m3TuwSsp2/hxf/HUpXwrv/IHKhzqZxL7hOiPlFd/Z/rKR4FwtCyxEoXHTuc9dsFdPsCsgoTC+fdinoTKIEs+p1mHfz+R/HsecxVb8m5q/7b1FuOUL4o37qvfXUeeuo8dRwoO0Ae1r24A65B30sBYVcSy7zHPOYkTUHU6KEeNiJz59Bc2cCf0cMX0ucpnAMV1fWUjAaBxq7br3JsRm4c2Upd60sw2LQsrOmnV017Zxq9adCx9v9EfyRdO5UUaaJSytzWVuZg9NmoM0Xoc0bRlHgkhk5zMq3jUhwuEQikUgkEolEIpFMBqQoJRGoKux9pOcy9yl45nNQuFgIHe5TULddrFt8V9/H0erg0i/C05+FvY8K50xmycDGUPUaeOoBpbcIU7RUTBNRaD6U7jp3Ni9+Ffb8WWyz4bsDO+9YEgvD81+C3X8U90+9Ab+/Ht7/D8go6rnt/i6XVMFCyJvT/zFtuZBVDh01UP3WAESprpDz8jU9lxssUHEZnHgJTrx4flEq4kfZ+BUA1NnXo8y5/tzbn4U/6udE+wlqPDXUemup89TR6G8UeU7BNoLJ7oB9kG3MZpZjFjMyK8kxFmM3GdBpNCgomHQmLDoLZr2ZLGMWdn027k4971R38uqRZl447e5yOXV23c6NooDDYqAg08ScggzmFtqZW5jBymkODLp0ydu18wu4dn7v7pCJhEooFiccTZBl0UvRSSKRSCQSiUQikUi6kKKURHBmlxA1AD7xFkSD8OwXRdD281+CezemXUxZ5VB6Uf/HWnQXvPk9EZq95cdw/fcHNoadvxfTmVeKDn7dsTggexq0V0PD7r5FqXgMjj4n5k+8PDFEqVgYareBr0XcDj2Rzn6afoVwLbUcht9eA+9/AnJniXWJOBz8u5hfOIAOXzOvhJ2/E536zpUHpaq9Q867U3lNlyj1suj8pzlHl7gdv0HprCehM8OG/6IvqSWaiHLMfYyj7qO0BdtoDbRS5W6gxltFW7hvB9LZ2PUZ5JiKyDNWkKufiyUxk9Z2K8cO+Xij1ZfqSpdh0pFjN2LvynAyG7Q0dLRwsuVUr851Wo1CUZaJcoeV4iwzmRY9FoMWi0GLwyoylnLtRnJtRhxWAzrtubvlnQuNRsFi0GExDPkQEolEIpFIJBKJRHJBIkWpqUCoE577V5h9fe9yuyQHugSQomXCmQNw4//Cb68W7qh9j6XznhadVVp3NjoDrPm8ELN2PSy2L1lx7jF6GkTANsDye/vepmiZEKXO7IGVfayv3wHBdjHvOgGexp7laWON3wW/3wBtx3suV7RwzTfh4k8JUeqx9woB73fXwsdeFWWTp9/s6rqnDKyjXuU1QpSqfgsi/v7L6FxV4O/qKFd+Se/1s64VfzdfMzTtSzvUgHA8TFVHFY2+RoxaA+Z9D2MyGGiuvBa/az/uhjfwRrz4o34CsQDVndUcdh0mFA/1Pk8XqqqgRjNJRHJIRJ2o0WwSMRtqzI6eDELBLLwJMw099vJ03XriCcXwhGL9nqs4y8z6OXlcOTePi6c7Mem1/W4rkUgkEolEIpFIJJLRR4pSU4GdvxeB2cdfhNnXgf6skOREXASYAyy4I728dBUsuVuU9T3/JYh2dQfrr3SvO0s/IFxSnXXwuw1w7Xdg1ceEmOVrgVNvirLApDNo959AjYO9EGZt6PuYxcuE06hhT9/rT7zY837NloEJOqNBJACP3SkEKUUrHpctT5ToXfwpmNZVOjdtDXz4BfjTbUIIevz98NGX06V7FZf2Luvri4rLQGuAeARObxJ/575Ilu7ZCkSmVBeBaABXyIUr4sZVUInL30jbO9/HlT+XtmAbdb46TnecJqZ2E32sgLUAArthy+5zDk+NOIlHM1FjdtSYHV08H6NailUpBtVAIBzHH4kRjiZS+8TPOka2RZ/qaleSbWZuYQZzCu2UZFloD4gcqDZfGH8kTjASwx+J47QamFeYwZzCDBxWaVWSSCQSiUQikUgkkomEFKWmAslg67BHlLedLdTUbAFfE6D0dlJd9Q048my6W1vxCnDOOP859Sb44D/hr/eIEm+Ip5QAACL/SURBVMAXvgwnXxGurbrtgArGTPjoK+J4yYylpR8QuVR9kXTttB7p2w10/CxR6vSm8RGlEnH4x0eFc0vRwnsfh1nX9L99/ny461H4/XXQcgie/ITI14I+S/cCkRg76k/xcvXrOM0ZfHTZzdiNVpi2Vux34iXOFC9ib8te9rbs5ZDrELFEDIvegsV1CjU/l6DNQeDZO+kMe3GHXITi3fKbzIDZAZ0HxO0sjIodTcJPmDgJRUFRdSgJO/GojXjMjJowQsJAIpZBPFhGIliGGreSYdLxnhWlvP/icqbl9O3kCkXjuPwi/NsTipJlFmV0DquhR37T2ZQ5Lf0/vxKJRCKRSCQSiUQimZBIUepCJxEXmUZJ9j7SW6hJlu6VX9LblWPLhfX/Di/cL+4PxCWVxDlDuH6e+xLs/XNvJ1O4U7iJLv+KCDhXNLDsg/0fr3AxoICagMb9PTOROmpFNhMIIefAX0Up20jSsBfaTgjhTtNP6ZeqiufqWFe21Y0/PLcglaRkhcjeeubzonsggNaYChs/1NjIH3e/zZvVe+lU9qCznk7t+ruj/0uZ4TJu1uXT6sxmc/PLNPzjlf7PZTFDwgfuI30MX0EbNzI94cEZT9AcLeF4dA6JaBaJcCHxUCGxhIYdxk9hVUJ8OfYR/h5bD90Spcx6LXML7cwstpFnN5FjM1CQaebyWbmYDecumTPptRRnmSnOMp9zO4lEIpFIJBKJRCKRTH7GVZTatGkT3//+99m1axeNjY08+eST3Hrrran1qqryH//xH/z617+mo6ODNWvW8Itf/ILKysrUNm63m89+9rM888wzaDQa7rjjDn784x9js9nG4RFNQJoPCodUkqrXofMMZBaL+7FIWgTpXrrXnRUfEft5G2Hhuwd3fr0Zbv25EJB2/k44rebeCAYb/OEG0dHvqU+KbWdeDVml/R/LaIfc2dB6VISddxelki4pWz6s/rQQpdxVIqtqIOVv5yMWFiV2QTccfQZu+5Vwg53Nrj/Ajt+I+cu/Asvv6fNwqqpyoO0AneF097fyyvWULbsHdj8MQLzyWv5r2yv8o/rnRDWtXY+v2z9tLANVE0TRhKiNvcTPADLs6XPELMSDZcSDZagJE0u1h1irF2V2j0eupTWRi5owocZsmDWiS53LoycSU7hR9yc+onuBmNrGh5TbaclchNWuw2rQcWX4FWytISIaE7kr7+JTGgPlBQ7yMkyUZluoyLGi1cgOcxKJRCKRSCQSiUQiOTfjKkr5/X4WL17Mhz/8YW6/vXcA9/e+9z1+8pOf8PDDD1NRUcHXv/51rr32Wg4fPozJJASBu+++m8bGRl5++WWi0Sj33nsv9913H48++uhYP5yJSbJ0L3uaEKC8DbD/cbj0X8XyU6+LcHBFC/Nu6fsYWh287/HhjWPp+8WtO7f/Cv7yfuF8AljRT8B5d4qWdYlSZ+VKnXhJTCuvgYJFYMqCUIdwSy0aQPe683HyVSFIARz+J/ha4a5HRFfAJA17046yhe+GdV8lnlDpCERSm4SiUZ488RxPnX6E5lDa7QSAqlAQv55f2RZT7tvPrfVWToceRNF0PT+qhix9EUvylnLHrJu5rGwV1W4XP9/xVzY1PUOIFpaFwqwPdNKuuZ7t1ntRjAoYVN7n/yM3dr4OQHXmKvIue4CyHCul2RZy7cZU6HciodLkCVHbspTAS/VY2g7w58xfwic2gzlbjOP33wDAsPB2vnzTClpaWsjLy0Nzrk59EolEIpFIJBKJRCKRnMW4ilLXXXcd113XdyCzqqr86Ec/4oEHHuCWW4RY8sc//pH8/Hyeeuop7rrrLo4cOcLGjRvZsWMHK1aI7m4//elPuf766/nBD35AUdEIOGQmO8kStmmXgjUX3vpf2PMIrP2iWL7nT2I64wqw5ozt2ObeBOu/Dq99U4hmM6/ud9NANEBToIn8/PlYAc50C9aOBER+FIjucRoNlK8RJXTVm0dGlDr0hJhmloLnDNRuFQHu1/0XVKwTpYh//SDEI3TYZvCD+MfY//MtHGt2EdNXozE2oTU1orVUoTF0pA6rxrvcVkocRROlSfccN1imY1DuJJ6zBQXI1MzkC0vu58a5SzHperqzpjtz+Z8NnwY+LRa88BXY/hCUn4F7V0PYC89/GRoeE+vn3sy023/NtL5cXoBGo1CUZaYoyww5f4SHLhNh9U/cB1f8u8jxSoalL717+M+rRCKRSCQSiUQikUimLBM2U+r06dM0NTVx1VVXpZZlZmZy0UUXsW3bNu666y62bdtGVlZWSpACuOqqq9BoNGzfvp3bbrutz2OHw2HC4XDqvscjytsSiQSJRKLPfSYDiUQCVVXTj0FVUWq2ogCJskugeDmat/4X3FUkqt9C2f9XlCPPiH0XvhvG47Gv+RdR0pc9TWRKdY0hGAvy12N/5bW616j11uIOuVO7lJUUMjvSSfZbXxcd5zpqMWeYKI6bKDEayHMdw5s/A3edmc4zb7Kyo5qyjLKhjzEaQDn6PAoQueyreBJGHBs/habtGPzpNjpMJXiUDMqCNfhVI7e77qMmuBV95h700w9h0ER6HE5VNeiDy5imu55Z2bMod1oozjbwYsPDbG79G1rrKeLWUwCsLljLD6/4AWadyFg67/U54yo02x9Crd2GuuWnKFt/jOIXpX/qqo+jXvNtkYc1kL911jS48YdonviocKKdeAlV0aAAanYFaunq3tecRDKKyOtNMpbI600y1shrTjKWyOtNMpbI621qMtC/94QVpZqamgDIz8/vsTw/Pz+1rqmpiby8vB7rdTodDocjtU1ffPe73+Ub3/hGr+Wtra2EQqHhDn3cSCQSdHZ2oqoqGo0GrfskuV0lZy7rLOKJTBz5SzE074FH34MSDQAQmHsnnrzLoKVlfAZunQMRoKWFSDzCc/XP8dipx2iPtPe5ea1eT61eD1VPpRdmZojp5i+ll+XnAir6p2/jvdPfx53T78SgMQAQV+Oc8Z/htO80Vd4q6nx1+GN+QvEQwXiQRFdJYUIFUzjKvEwjRTEDv3h1F1GDlwznMhy6RhwEsSQimNRWPFn5HNVkE9L9CosmmhqGVtFSYCqh1DKNMmsFG0quoNTe28V3UcF9bG9dzPcOfA9P1MO1xdfyhXlfwOv24sU7sOfSMot8nQklFkJ5+QHxGPQWfKu+SGDB+6HNNbDjJMm7FMslX8Vy6DF0ndUoXc+Lr/I2/K2tva45iWQ0kdebZCyR15tkrJHXnGQskdebZCyR19vUxOsd2HfYCStKjSZf/epX+eIXv5i67/F4KC0tJTc3l4yMjHEc2fBIJBIoikJubq74Z68VLig1swTnzGVioxUfhOf2oOkSpBJX/Dumtf+KSRmfYGpVVdnWuI0dTTvY17qPQ65DhOJCGNQrRgo06zDGK1AjThLRTGw2D1d0/jfNuGnImkdCaySr4yB+EhyzFtBCiDgRFDQ44zFUVcWlgz9W/ZHNrZu5Zto1HGg7wP7W/fiivgGP86RdBOcrvIwBCAENQAPGs7YMdG2ncFHBRdww/QauLLsSq946oPPclHcTa2espcZTw+LcxShD+bvMvAqOPouq0cGKD8OlX8JmzWXI0f9X3Q9X3U/C1wL170DAhXXx+7Bq9b2vOYlkFJHXm2QskdebZKyR15xkLJHXm2Qskdfb1CSZA34+JqwoVVBQAEBzczOFhYWp5c3NzSxZsiS1TctZ7p5YLIbb7U7t3xdGoxGj8WwxATQazaT/J1EUJf04areJZeVrUJKPa+Ed8NqDImvopp+gGcFcoEg8whH3EY64jnCs/RjH3MeIxCOsKFjBJUWXsCJ/BRa9JbX9ruZd/M/O/+FA24GejwE90faL8LVejjtu77YmAdi4UVfOA7oqwm3bMSoxABpVBzeGP00HGaAJQsLE9/Q/Zr12B/9RsJwXLO2c9pzml/t/2eNcWcYsZmTOItdURodXT50rTm1bjGhMiEEGJcInjY/SpFc4kFmJas2k1F7CjOxyim2FRBIRAtEAoXgIm95GjjmHHHMOldmV5Fl6uvgGitPixGlxDmlfAG78EVRchlJ5NTimM2JyY0YBzLu51+Ie15xEMsrI600ylsjrTTLWyGtOMpbI600ylsjrbeox0L/1hBWlKioqKCgo4NVXX02JUB6Ph+3bt/PJT34SgNWrV9PR0cGuXbtYvnw5AK+99hqJRIKLLrpovIY+MVBVqO4KpC5fk15uyoRPboV4ROQ4DZNTHad4uupp9rTs4WDbQSKJSK9tjrUf45Ejj6BRNDhMDpwmJwatoYcYlcEsOtqLCflKiQemQcKC3aTj6sX55NqNWA06DDoNTZ0hwtWLwf1aSpB6WnMlvzJ9iGkFOaywGTDqtBw408nb7XPZoN3Bxxvq+Qv/hqP0VcwmP6ZEBYQqCHhLaO00URdVe43ZoNNw5Zw8Pp69iyU72kFnho89AcYh+43GDlsuXPTx8R6FRCKRSCQSiUQikUgk52RcRSmfz8fJkydT90+fPs3evXtxOByUlZXxhS98gW9961tUVlZSUVHB17/+dYqKirj11lsBmDt3Lhs2bOBjH/sYDz30ENFolM985jPcddddsvOe+xT4unK1uotSABnDe24i8Qib6jfx2NHHeKfpnV7rTeRjVkvRxUrQKAoJ03Hc8WPE1RhtwTbagm2pbWP+CsIt1+MNlQKg1ypcWpnDHctKuHpePia9tvcAPNnwiz+ANQ9u/F9unraW3v4daD9phT//kWmaZrJDJtqq3tfHVmlBqsxhYW1lDpdV5nDJzBwyTHp47Adi5ewNk0OQkkgkEolEIpFIJBKJZJIwrqLUzp07ueKKK1L3kzlP99xzD3/4wx+4//778fv93HfffXR0dLB27Vo2btzYozbxkUce4TOf+QxXXnklGo2GO+64g5/85Cdj/lgmHCdeFlNrHjhnDHi3WDxBY2eIapefGpefYNyPqvESo5OqzuPsd79Dc+QwqpJ2RMXDecS884kHyokHy/AmLGcd9SJQImhNDSg6D4rOh6L1Ew+WEffPIj/DxIpKB9fMz+eKOXlCDDoXGUXwr8dBq4dzZC5lVywFvQWiAZ65zcgfXNNp9oTItRnJsRt7THPt4taDYAecfEXMz799wM+hRCKRSCQSiUQikUgkkvMzrqLUunXrUNXepVNJFEXhwQcf5MEHH+x3G4fDwaOPPjoaw5u8tB2H174p5mdfd07hJp5QOdzgYdOJZl4/cZIDrSdRjdVozdVozbUo2nDvnRRQVQ0x7zyi7auJB6aTZTFQ7rBQOsNCrt2IzajDYtDhC0c51uTlSKOXhk4DlXk2lpdns7Qsm/lFGUxzWrEah3AZ6gzn30arg+LlUL2ZIs9+vnb9HYM7x6nXRZmj3gqVVw9+jBKJRCKRSCQSiUQikUj6ZcJmSkmGhhINoDx9D56Ynx25ZezNms/Ox7/HiTY3Wg1kmfVkWnSE4yFaAq14om5UbScavRvFGMNQ0s+BVQ2auAOndgHzs1ewtuRipjtzU24j2wCEpURCRaMZ4y5/paugejPU9S4zPC/JTK6yi0FvHtlxSSQSiUQikUgkEolEMsWRotQFRIu/mRde+xTbtC52lJUQU4DTPxMru5rYBYCGUNcOetCcVSmnQcPMrNmsLFjG0vylTM+cjtPsJMuYhUYZXqeEMRekAEq7Au8bdkMsMjCHVZKaLlFq2tqRH5dEIpFIJBKJRCKRSCRTHClKXSBEYlFu+PsNRDRRMAtXj5owoEYzsegt5Fjs6DRaQtEEoVgcDQbyLbmUZeUzN7eEuTkVlNpLKbAVoD9bqZrMlKwU01gImg5AyfKB7ed3QcthMS9FKYlEIpFIJBKJRCKRSEYcKUpdIBgiXtYFvOw065kRyMBn/xjXz76MDfNLKcg0nf8AFyoWB+TMEjlbddsHLkrVbhVTvQWKlo7e+CQSiUQikUgkEolEIpmiSFHqQsHi4D1LvsdX9v6CrHv/isGaNd4jmjiUrBKiVP07wKcGtk/1W2Jaukp0+ZNIJBKJRCKRSCQSiUQyogwvJEgyoVi5+hYSd/wRnTljvIcysShdJaaDCTtPhpyXy9I9iUQikUgkEolEIpFIRgMpSkkufJJh554z0Fl//u2D7dB8UMxPWzN645JIJBKJRCKRSCQSiWQKI0UpyYVPziwwZYr5uu3n375mG6CCzgTFA8ygkkgkEolEIpFIJBKJRDIopCglufDRaESuFAyshK+mq3SvZCXojKM3LolEIpFIJBKJRCKRSKYwUpSSTA2SJXwDcUolQ86nyTwpiUQikUgkEolEIpFIRgspSkmmBqUrxbRxPwQ7+t/O2wRN+8V8ucyTkkgkEolEIpFIJBKJZLSQopRkalCyEowZoMZh8w96r48EYNMP4GcrQU2AzgwlK8Z+nBKJRCKRSCQSiUQikUwRpCglmRoYrHD5/WL+7Yeg7WR63elN8NNl8No3IewBixNu/yXozeMzVolEIpFIJBKJRCKRSKYAUpSSTB1WfRwcMyARhZf+XSyreh0eeTd4G0Fvgcu+DJ/bC/NuGdehSiQSiUQikUgkEolEcqEjRSnJ1EFngA3fFfPHN8KrD8Jjd0EsBLlz4DM7Yf0DYMoY33FKJBKJRCKRSCQSiUQyBZCilGRqMetamHm1mN/8P12C1Fy451nILB7fsUkkEolEIpFIJBKJRDKFkKKUZOpx7XdAoxPzefPgnmfAlju+Y5JIJBKJRCKRSCQSiWSKoRvvAUgkY07uLLjl51D9Flz1n2DNGe8RSSQSiUQikUgkEolEMuWQopRkarL4LnGTSCQSiUQikUgkEolEMi7I8j2JRCKRSCQSiUQikUgkEsmYI0UpiUQikUgkEolEIpFIJBLJmCNFKYlEIpFIJBKJRCKRSCQSyZgjRSmJRCKRSCQSiUQikUgkEsmYI0UpiUQikUgkEolEIpFIJBLJmCNFKYlEIpFIJBKJRCKRSCQSyZgjRSmJRCKRSCQSiUQikUgkEsn/b+/uY6qs/z+Ov4CjBCqapoiIN6xh3uJN6ZCVI28w0WlWo7KhqBkqS53ispxUmzFLrSitVgq62ErsxjSlmdwslZaaKIKa8yYsuSkbgqkIh8/vj8b5iVnf0nOuc+w8Hxt/cF1vr+vzHi/P2efN4RzLMZQCAAAAAACA5RhKAQAAAAAAwHIMpQAAAAAAAGA5hlIAAAAAAACwHEMpAAAAAAAAWI6hFAAAAAAAACxnc/cCPIExRpJUU1Pj5pXcmsbGRtXW1uqOO+6Qry/zRrgemYOVyBusRN5gNTIHK5E3WIm8eaem+UrTvOWvMJSSVFtbK0kKCwtz80oAAAAAAAD+G2pra9W2bdu/PO9j/tfYygs0Njbq3LlzatOmjXx8fNy9nJtWU1OjsLAwnT17VkFBQe5eDrwAmYOVyBusRN5gNTIHK5E3WIm8eSdjjGpra9WlS5e/fYUcr5SS5Ovrq65du7p7GU4TFBTEf3ZYiszBSuQNViJvsBqZg5XIG6xE3rzP371Cqgl/0AkAAAAAAADLMZQCAAAAAACA5RhK/Yf4+/srNTVV/v7+7l4KvASZg5XIG6xE3mA1MgcrkTdYibzh7/BG5wAAAAAAALAcr5QCAAAAAACA5RhKAQAAAAAAwHIMpQAAAAAAAGA5hlIeJi0tTffdd5/atGmjTp06adKkSTp+/HizmitXrmju3Lnq0KGDWrdurUceeUSVlZXNasrKyhQXF6fAwEB16tRJKSkpamhoaFaTn5+vwYMHy9/fX3fffbcyMzNd3R48jJV5a7Jnzx7ZbDYNHDjQVW3BQ1mZt6ysLEVGRiowMFAhISGaPn26zp8/7/Ie4Vmclblnn31WQ4YMkb+//w0fu/Lz8zVx4kSFhISoVatWGjhwoLKyslzZGjyQVXmTJGOMVq5cqYiICPn7+ys0NFTLly93VWvwUM7I3KFDh/TEE08oLCxMAQEB6t27t958880/3Yt9A6zMWxP2Dd6BoZSHKSgo0Ny5c/Xtt99q586dqq+v15gxY/T77787ahYsWKCtW7cqOztbBQUFOnfunCZPnuw4b7fbFRcXp6tXr2rv3r3asGGDMjMztWzZMkfN6dOnFRcXp5iYGBUVFWn+/PmaOXOmvvrqK0v7hXtZlbcm1dXVSkhI0MiRIy3pD57Fqrzt2bNHCQkJmjFjhkpKSpSdna3vvvtOTz/9tKX9wv2ckbkm06dPV3x8/A3vs3fvXg0YMECffPKJDh8+rMTERCUkJGjbtm0u6w2ex6q8SdK8efP0wQcfaOXKlTp27Ji++OILDR061CV9wXM5I3MHDhxQp06d9OGHH6qkpEQvvPCClixZorfffttRw74BknV5a8K+wYsYeLSqqiojyRQUFBhjjKmurjYtWrQw2dnZjpqjR48aSaawsNAYY8z27duNr6+vqaiocNS88847JigoyNTV1RljjFm8eLHp27dvs3vFx8eb2NhYV7cED+aqvDWJj483S5cuNampqSYyMtL1DcGjuSpvr732mgkPD292r/T0dBMaGurqluDhbiZz1/o3j13jxo0ziYmJTlk3bk+uyltpaamx2Wzm2LFjLls7bk+3mrkmc+bMMTExMY7v2TfgRlyVtybsG7wHr5TycBcuXJAktW/fXtIf0+X6+nqNGjXKUXPPPfeoW7duKiwslCQVFhaqf//+Cg4OdtTExsaqpqZGJSUljpprr9FU03QNeCdX5U2SMjIydOrUKaWmplrRCm4DrspbVFSUzp49q+3bt8sYo8rKSm3evFnjxo2zqjV4qJvJ3K3cq+k+8E6uytvWrVsVHh6ubdu2qWfPnurRo4dmzpyp3377zbkN4LbjrMxd//jFvgE34qq8SewbvI3N3QvAX2tsbNT8+fMVHR2tfv36SZIqKirUsmVLtWvXrlltcHCwKioqHDXXbtiazjed+7uampoaXb58WQEBAa5oCR7MlXk7ceKEnnvuOX3zzTey2XjYgWvzFh0draysLMXHx+vKlStqaGjQhAkTtGbNGhd3BU92s5m7GZs2bdK+ffv03nvv3cqScRtzZd5OnTqlH3/8UdnZ2dq4caPsdrsWLFigRx99VLm5uc5sA7cRZ2Vu7969+vjjj/Xll186jrFvwPVcmTf2Dd6Hn7IHmzt3ro4cOaLdu3e7eynwAq7Km91u15NPPqmXXnpJERERTr02bl+ufHwrLS3VvHnztGzZMsXGxqq8vFwpKSlKSkrSunXrnH4/3B6sek7Ny8tTYmKi3n//ffXt29el94LncmXeGhsbVVdXp40bNzqeV9etW6chQ4bo+PHj6tWrl9PvCc/njMwdOXJEEydOVGpqqsaMGePE1eG/xlV5Y9/gnfjzPQ+VnJysbdu2KS8vT127dnUc79y5s65evarq6upm9ZWVlercubOj5vpPcmn6/n/VBAUF8dsOL+TKvNXW1mr//v1KTk6WzWaTzWbTyy+/rEOHDslms/FbXS/k6se3tLQ0RUdHKyUlRQMGDFBsbKzWrl2r9evXq7y83IWdwVPdSub+jYKCAk2YMEGvv/66EhISbnXZuE25Om8hISGy2WzNNmy9e/eW9Menk8L7OCNzpaWlGjlypGbNmqWlS5c2O8e+AddyZd7YN3gnhlIexhij5ORkffbZZ8rNzVXPnj2bnR8yZIhatGihXbt2OY4dP35cZWVlioqKkvTH+6kUFxerqqrKUbNz504FBQWpT58+jpprr9FU03QNeAcr8hYUFKTi4mIVFRU5vpKSktSrVy8VFRVp2LBh1jQLt7Pq8e3SpUvy9W3+9Obn5+dYA7yHMzL3T+Xn5ysuLk4rVqzQrFmznLJ+3F6sylt0dLQaGhp08uRJx7EffvhBktS9e/db7AK3E2dlrqSkRDExMZo6daqWL1/+p/uwb4BkTd7YN3gpt73FOm5o9uzZpm3btiY/P9+Ul5c7vi5duuSoSUpKMt26dTO5ublm//79JioqykRFRTnONzQ0mH79+pkxY8aYoqIik5OTYzp27GiWLFniqDl16pQJDAw0KSkp5ujRo2bNmjXGz8/P5OTkWNov3MuqvF2PT9HwTlblLSMjw9hsNrN27Vpz8uRJs3v3bnPvvfeaoUOHWtov3M8ZmTPGmBMnTpiDBw+aZ555xkRERJiDBw+agwcPOj7xMTc31wQGBpolS5Y0u8/58+ct7RfuZVXe7Ha7GTx4sHnggQfM999/b/bv32+GDRtmRo8ebWm/cD9nZK64uNh07NjRPPXUU82uUVVV5ahh3wBjrMvb9dg3/PcxlPIwkm74lZGR4ai5fPmymTNnjrnzzjtNYGCgefjhh015eXmz65w5c8Y89NBDJiAgwNx1111m4cKFpr6+vllNXl6eGThwoGnZsqUJDw9vdg94Byvzdi2eXLyTlXlLT083ffr0MQEBASYkJMRMmTLF/PTTT1a0CQ/irMyNGDHihtc5ffq0McaYqVOn3vD8iBEjrGsWbmdV3owx5ueffzaTJ082rVu3NsHBwWbatGkMQb2QMzKXmpp6w2t079692b3YN8DKvF2LfcN/n48x/C0DAAAAAAAArMV7SgEAAAAAAMByDKUAAAAAAABgOYZSAAAAAAAAsBxDKQAAAAAAAFiOoRQAAAAAAAAsx1AKAAAAAAAAlmMoBQAAAAAAAMsxlAIAAAAAAIDlGEoBAAAAAADAcgylAAAALDZt2jT5+PjIx8dHLVq0UHBwsEaPHq3169ersbHxH18nMzNT7dq1c91CAQAAXIihFAAAgBuMHTtW5eXlOnPmjHbs2KGYmBjNmzdP48ePV0NDg7uXBwAA4HIMpQAAANzA399fnTt3VmhoqAYPHqznn39eW7Zs0Y4dO5SZmSlJWr16tfr3769WrVopLCxMc+bM0cWLFyVJ+fn5SkxM1IULFxyvunrxxRclSXV1dVq0aJFCQ0PVqlUrDRs2TPn5+e5pFAAA4C8wlAIAAPAQDz74oCIjI/Xpp59Kknx9fZWenq6SkhJt2LBBubm5Wrx4sSRp+PDheuONNxQUFKTy8nKVl5dr0aJFkqTk5GQVFhbqo48+0uHDh/XYY49p7NixOnHihNt6AwAAuJ6PMca4exEAAADeZNq0aaqurtbnn3/+p3OPP/64Dh8+rNLS0j+d27x5s5KSkvTrr79K+uM9pebPn6/q6mpHTVlZmcLDw1VWVqYuXbo4jo8aNUpDhw7VK6+84vR+AAAAbobN3QsAAADA/zPGyMfHR5L09ddfKy0tTceOHVNNTY0aGhp05coVXbp0SYGBgTf898XFxbLb7YqIiGh2vK6uTh06dHD5+gEAAP4phlIAAAAe5OjRo+rZs6fOnDmj8ePHa/bs2Vq+fLnat2+v3bt3a8aMGbp69epfDqUuXrwoPz8/HThwQH5+fs3OtW7d2ooWAAAA/hGGUgAAAB4iNzdXxcXFWrBggQ4cOKDGxkatWrVKvr5/vA3opk2bmtW3bNlSdru92bFBgwbJbrerqqpK999/v2VrBwAA+LcYSgEAALhBXV2dKioqZLfbVVlZqZycHKWlpWn8+PFKSEjQkSNHVF9fr7feeksTJkzQnj179O677za7Ro8ePXTx4kXt2rVLkZGRCgwMVEREhKZMmaKEhAStWrVKgwYN0i+//KJdu3ZpwIABiouLc1PHAAAAzfHpewAAAG6Qk5OjkJAQ9ejRQ2PHjlVeXp7S09O1ZcsW+fn5KTIyUqtXr9aKFSvUr18/ZWVlKS0trdk1hg8frqSkJMXHx6tjx4569dVXJUkZGRlKSEjQwoUL1atXL02aNEn79u1Tt27d3NEqAADADfHpewAAAAAAALAcr5QCAAAAAACA5RhKAQAAAAAAwHIMpQAAAAAAAGA5hlIAAAAAAACwHEMpAAAAAAAAWI6hFAAAAAAAACzHUAoAAAAAAACWYygFAAAAAAAAyzGUAgAAAAAAgOUYSgEAAAAAAMByDKUAAAAAAABgOYZSAAAAAAAAsNz/AYFaG+nr6GRmAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1200x450 with 1 Axes>"
       ]
@@ -352,7 +398,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1300x380 with 3 Axes>"
       ]
@@ -411,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "f0140722",
    "metadata": {
     "execution": {
@@ -424,7 +470,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x450 with 1 Axes>"
       ]
@@ -467,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "c9a34c2a",
    "metadata": {
     "execution": {
@@ -516,12 +562,19 @@
    "cell_type": "markdown",
    "id": "fa280c23",
    "metadata": {},
-   "source": "## Next: `02_cpi_backtest_demo.ipynb`\n\nThat notebook runs the full end-to-end backtest against the reference\nspec, compares a naive baseline to AutoARIMA, plots the predictions\nagainst the observed series with 80% CI bands, and walks through where\neach predictor fails and why.  Start there after this warm-up."
+   "source": [
+    "## Next: `02_cpi_backtest_demo.ipynb`\n",
+    "\n",
+    "That notebook runs the full end-to-end backtest against the reference\n",
+    "spec, compares a naive baseline to AutoARIMA, plots the predictions\n",
+    "against the observed series with 80% CI bands, and walks through where\n",
+    "each predictor fails and why.  Start there after this warm-up."
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": ".venv (3.12.3)",
    "language": "python",
    "name": "python3"
   },
@@ -535,7 +588,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/implementations/getting_started/02_cpi_backtest_demo.ipynb b/implementations/getting_started/02_cpi_backtest_demo.ipynb
index 3600196c3..856f046b1 100644
--- a/implementations/getting_started/02_cpi_backtest_demo.ipynb
+++ b/implementations/getting_started/02_cpi_backtest_demo.ipynb
@@ -131,9 +131,9 @@
               "      <td>StatCan table 18-10-0004-11</td>\n",
               "      <td>Index 2002=100</td>\n",
               "      <td>MS</td>\n",
-              "      <td>926</td>\n",
+              "      <td>931</td>\n",
               "      <td>1949-01-01</td>\n",
-              "      <td>2026-02-01</td>\n",
+              "      <td>2026-07-01</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
@@ -142,9 +142,9 @@
               "      <td>StatCan table 18-10-0004-11</td>\n",
               "      <td>Index 2002=100</td>\n",
               "      <td>MS</td>\n",
-              "      <td>570</td>\n",
+              "      <td>575</td>\n",
               "      <td>1978-09-01</td>\n",
-              "      <td>2026-02-01</td>\n",
+              "      <td>2026-07-01</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -156,12 +156,12 @@
               "1   cpi_shelter_canada   CPI Shelter, Canada (2002=100)   \n",
               "\n",
               "                        source           units frequency  n_obs      start  \\\n",
-              "0  StatCan table 18-10-0004-11  Index 2002=100        MS    926 1949-01-01   \n",
-              "1  StatCan table 18-10-0004-11  Index 2002=100        MS    570 1978-09-01   \n",
+              "0  StatCan table 18-10-0004-11  Index 2002=100        MS    931 1949-01-01   \n",
+              "1  StatCan table 18-10-0004-11  Index 2002=100        MS    575 1978-09-01   \n",
               "\n",
               "         end  \n",
-              "0 2026-02-01  \n",
-              "1 2026-02-01  "
+              "0 2026-07-01  \n",
+              "1 2026-07-01  "
             ]
           },
           "execution_count": 2,
@@ -342,6 +342,20 @@
         }
       },
       "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/home/coder/agentic-forecasting/.venv/lib/python3.12/site-packages/statsforecast/models.py:3806: SyntaxWarning: invalid escape sequence '\\h'\n",
+            "  \"\"\"CrostonClassic model.\n",
+            "/home/coder/agentic-forecasting/.venv/lib/python3.12/site-packages/statsforecast/models.py:3984: SyntaxWarning: invalid escape sequence '\\h'\n",
+            "  \"\"\"CrostonOptimized model.\n",
+            "/home/coder/agentic-forecasting/.venv/lib/python3.12/site-packages/statsforecast/models.py:4132: SyntaxWarning: invalid escape sequence '\\h'\n",
+            "  \"\"\"CrostonSBA model.\n",
+            "/home/coder/agentic-forecasting/.venv/lib/python3.12/site-packages/statsforecast/models.py:4468: SyntaxWarning: invalid escape sequence '\\e'\n",
+            "  \"\"\"TSB model.\n"
+          ]
+        },
         {
           "name": "stdout",
           "output_type": "stream",
@@ -349,7 +363,7 @@
             "Predictor                       Origins  Skipped  Mean CRPS\n",
             "------------------------------------------------------------\n",
             "last_value_naive                    301        0    10.1083\n",
-            "darts_autoarima                     301        0     8.4507\n"
+            "darts_autoarima                     301        0     8.4811\n"
           ]
         }
       ],
@@ -395,8 +409,8 @@
           "output_type": "stream",
           "text": [
             "Mean CRPS  naive:  10.1083\n",
-            "Mean CRPS  arima:  8.4507\n",
-            "Mean reduction:    1.6576  (16.4%)\n"
+            "Mean CRPS  arima:  8.4811\n",
+            "Mean reduction:    1.6272  (16.1%)\n"
           ]
         },
         {
@@ -436,9 +450,9 @@
               "      <td>2000-01-01</td>\n",
               "      <td>93.9</td>\n",
               "      <td>2.3</td>\n",
-              "      <td>94.612397</td>\n",
-              "      <td>0.959491</td>\n",
-              "      <td>1.340509</td>\n",
+              "      <td>94.570107</td>\n",
+              "      <td>0.959547</td>\n",
+              "      <td>1.340453</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
@@ -446,9 +460,9 @@
               "      <td>2000-02-01</td>\n",
               "      <td>93.3</td>\n",
               "      <td>11.4</td>\n",
-              "      <td>92.967194</td>\n",
-              "      <td>10.922756</td>\n",
-              "      <td>0.477244</td>\n",
+              "      <td>93.190228</td>\n",
+              "      <td>10.794090</td>\n",
+              "      <td>0.605910</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
@@ -456,9 +470,9 @@
               "      <td>2000-03-01</td>\n",
               "      <td>96.2</td>\n",
               "      <td>2.6</td>\n",
-              "      <td>96.986834</td>\n",
-              "      <td>1.055343</td>\n",
-              "      <td>1.544657</td>\n",
+              "      <td>97.058123</td>\n",
+              "      <td>1.130947</td>\n",
+              "      <td>1.469053</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
@@ -466,9 +480,9 @@
               "      <td>2000-04-01</td>\n",
               "      <td>104.7</td>\n",
               "      <td>4.0</td>\n",
-              "      <td>106.029623</td>\n",
-              "      <td>4.608166</td>\n",
-              "      <td>-0.608166</td>\n",
+              "      <td>106.110491</td>\n",
+              "      <td>4.558893</td>\n",
+              "      <td>-0.558893</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
@@ -476,9 +490,9 @@
               "      <td>2000-05-01</td>\n",
               "      <td>98.8</td>\n",
               "      <td>8.3</td>\n",
-              "      <td>96.902805</td>\n",
-              "      <td>9.282658</td>\n",
-              "      <td>-0.982658</td>\n",
+              "      <td>97.163162</td>\n",
+              "      <td>9.178505</td>\n",
+              "      <td>-0.878505</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>5</th>\n",
@@ -486,9 +500,9 @@
               "      <td>2000-06-01</td>\n",
               "      <td>100.7</td>\n",
               "      <td>6.0</td>\n",
-              "      <td>101.794445</td>\n",
-              "      <td>4.097880</td>\n",
-              "      <td>1.902120</td>\n",
+              "      <td>101.895335</td>\n",
+              "      <td>3.988190</td>\n",
+              "      <td>2.011810</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>6</th>\n",
@@ -496,9 +510,9 @@
               "      <td>2000-07-01</td>\n",
               "      <td>107.1</td>\n",
               "      <td>4.1</td>\n",
-              "      <td>107.504736</td>\n",
-              "      <td>3.639680</td>\n",
-              "      <td>0.460320</td>\n",
+              "      <td>107.469680</td>\n",
+              "      <td>3.730993</td>\n",
+              "      <td>0.369007</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>7</th>\n",
@@ -506,9 +520,9 @@
               "      <td>2000-08-01</td>\n",
               "      <td>106.7</td>\n",
               "      <td>2.4</td>\n",
-              "      <td>106.492774</td>\n",
-              "      <td>1.701136</td>\n",
-              "      <td>0.698864</td>\n",
+              "      <td>106.668564</td>\n",
+              "      <td>1.560474</td>\n",
+              "      <td>0.839526</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>8</th>\n",
@@ -516,9 +530,9 @@
               "      <td>2000-09-01</td>\n",
               "      <td>103.0</td>\n",
               "      <td>5.3</td>\n",
-              "      <td>103.264213</td>\n",
-              "      <td>4.229439</td>\n",
-              "      <td>1.070561</td>\n",
+              "      <td>103.377972</td>\n",
+              "      <td>4.114240</td>\n",
+              "      <td>1.185760</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>9</th>\n",
@@ -526,9 +540,9 @@
               "      <td>2000-10-01</td>\n",
               "      <td>109.1</td>\n",
               "      <td>0.1</td>\n",
-              "      <td>109.485683</td>\n",
-              "      <td>0.416465</td>\n",
-              "      <td>-0.316465</td>\n",
+              "      <td>109.388998</td>\n",
+              "      <td>0.427665</td>\n",
+              "      <td>-0.327665</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -536,28 +550,28 @@
             ],
             "text/plain": [
               "  forecast_date      origin  point_naive  crps_naive  point_arima  crps_arima  \\\n",
-              "0    2000-02-01  2000-01-01         93.9         2.3    94.612397    0.959491   \n",
-              "1    2000-03-01  2000-02-01         93.3        11.4    92.967194   10.922756   \n",
-              "2    2000-04-01  2000-03-01         96.2         2.6    96.986834    1.055343   \n",
-              "3    2000-05-01  2000-04-01        104.7         4.0   106.029623    4.608166   \n",
-              "4    2000-06-01  2000-05-01         98.8         8.3    96.902805    9.282658   \n",
-              "5    2000-07-01  2000-06-01        100.7         6.0   101.794445    4.097880   \n",
-              "6    2000-08-01  2000-07-01        107.1         4.1   107.504736    3.639680   \n",
-              "7    2000-09-01  2000-08-01        106.7         2.4   106.492774    1.701136   \n",
-              "8    2000-10-01  2000-09-01        103.0         5.3   103.264213    4.229439   \n",
-              "9    2000-11-01  2000-10-01        109.1         0.1   109.485683    0.416465   \n",
+              "0    2000-02-01  2000-01-01         93.9         2.3    94.570107    0.959547   \n",
+              "1    2000-03-01  2000-02-01         93.3        11.4    93.190228   10.794090   \n",
+              "2    2000-04-01  2000-03-01         96.2         2.6    97.058123    1.130947   \n",
+              "3    2000-05-01  2000-04-01        104.7         4.0   106.110491    4.558893   \n",
+              "4    2000-06-01  2000-05-01         98.8         8.3    97.163162    9.178505   \n",
+              "5    2000-07-01  2000-06-01        100.7         6.0   101.895335    3.988190   \n",
+              "6    2000-08-01  2000-07-01        107.1         4.1   107.469680    3.730993   \n",
+              "7    2000-09-01  2000-08-01        106.7         2.4   106.668564    1.560474   \n",
+              "8    2000-10-01  2000-09-01        103.0         5.3   103.377972    4.114240   \n",
+              "9    2000-11-01  2000-10-01        109.1         0.1   109.388998    0.427665   \n",
               "\n",
               "   crps_reduction  \n",
-              "0        1.340509  \n",
-              "1        0.477244  \n",
-              "2        1.544657  \n",
-              "3       -0.608166  \n",
-              "4       -0.982658  \n",
-              "5        1.902120  \n",
-              "6        0.460320  \n",
-              "7        0.698864  \n",
-              "8        1.070561  \n",
-              "9       -0.316465  "
+              "0        1.340453  \n",
+              "1        0.605910  \n",
+              "2        1.469053  \n",
+              "3       -0.558893  \n",
+              "4       -0.878505  \n",
+              "5        2.011810  \n",
+              "6        0.369007  \n",
+              "7        0.839526  \n",
+              "8        1.185760  \n",
+              "9       -0.327665  "
             ]
           },
           "execution_count": 6,
@@ -620,7 +634,7 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
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@@ -1096,7 +1110,7 @@
   ],
   "metadata": {
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-      "display_name": ".venv",
+      "display_name": ".venv (3.12.3)",
       "language": "python",
       "name": "python3"
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@@ -1110,7 +1124,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
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diff --git a/implementations/manufacturing_stress_forecasting/README.md b/implementations/manufacturing_stress_forecasting/README.md
new file mode 100644
index 000000000..4f821140f
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/README.md
@@ -0,0 +1,318 @@
+# Manufacturing stress forecasting — IPMAN and macro MVP
+
+This implementation asks one Track 1 question:
+
+> Given information available at a monthly forecast origin, what is the
+> probability that U.S. manufacturing will be under stress three months later?
+
+The feature service provides 15 active variables: trailing 1-, 3-, and
+6-month IPMAN changes plus the requested FRED and Yahoo Finance fields:
+`FEDFUNDS`, `YC_SPREAD`, `CPIAUCSL`, `CPI_YOY`, `UNRATE`, `ICSA`, `VIXCLS`,
+`CREDIT_SPREAD` (Moody's Baa corporate yield minus the 10-year Treasury yield,
+FRED `BAA10Y`), and 3- and 12-month returns for both `SPY` and `XLI`. FRED
+levels are collapsed to monthly observations, derived fields use the documented
+source series, and Yahoo returns use monthly adjusted-close prices.
+
+## Target
+
+A month is labelled `1` (stress) when IPMAN has declined by at least 2% over
+its preceding three months; otherwise it is `0`. The threshold is a provisional
+version-1 definition and should be reviewed visually before expanding the
+project.
+
+The forecast made at month `t` predicts the stress label at `t + 3 months`.
+That distinction makes this forecasting rather than current-state detection.
+
+## Predictors
+
+- `HistoricalFrequencyPredictor`: the visible historical stress rate.
+- `ManufacturingStressLogisticPredictor`: fit-at-origin logistic regression on
+  the active IPMAN, macroeconomic, and market variables.
+- `ManufacturingStressXGBoostPredictor`: a small fit-at-origin gradient-boosted
+  tree classifier using the same variables and cutoff-safe training rows.
+- `manufacturing_stress_analyst`: a structured LLM predictor receiving the same
+  cutoff-safe signals plus recent IPMAN history and historical base rates.
+
+All predictors return `BinaryForecast` probabilities; backtested predictors are scored with Brier score.
+
+## Data and cutoff assumptions
+Compare XGBoost with logistic regression and historical frequency rather than judging it
+in isolation, because this small monthly dataset can overfit flexible models.
+`FREDAdapter` caches the required FRED series under `data/fred/`, and
+`YFinanceDailyAdapter` caches `SPY` and `XLI` under `data/yfinance/`.
+Yahoo refreshes request history from 1998 onward explicitly so the provider's
+default recent-history window cannot replace the long-term cache.
+IPMAN is conservatively treated as available one month after its reference
+month. Daily rate observations are treated as available on the next business
+day and collapsed to their final monthly observation. The standard FRED API
+does not provide full point-in-time vintages, so historical observations may
+contain later revisions; a production study should use ALFRED vintages.
+
+## Run
+
+The general interactive entry point is
+[`manufacturing_stress_workbench.ipynb`](manufacturing_stress_workbench.ipynb).
+Open it in VS Code or Jupyter and use its configuration cell to refresh input
+data, run the deterministic smoke test, and explicitly opt in to the cached
+LLMP backtest without using the terminal.
+
+The dedicated
+[`manufacturing_stress_parameter_sweep_workbench_2.ipynb`](manufacturing_stress_parameter_sweep_workbench_2.ipynb)
+runs the controlled logistic/XGBoost tuning workflow across chronological
+validation folds without requiring the CLI. The original parameter-sweep
+workbench is unchanged.
+
+The [`manufacturing_stress_workbench_2.ipynb`](manufacturing_stress_workbench_2.ipynb)
+is a self-contained comparison notebook for the fixed 2018–2024 smoke window.
+It compares historical frequency, logistic regression with `C=0.001`, XGBoost
+with 50 trees, depth 2, learning rate 0.03, `min_child_weight=3`, and
+`reg_lambda=5`, plus the deterministic hybrid anchor based on logistic only. It reports mean Brier
+score, Brier skill, per-origin outcomes, and calibration diagnostics. Analyst,
+hybrid-agent, and adaptive-agent evaluations are disabled by default and
+require an explicit opt-in because they may make LLM calls. Set
+`RUN_HYBRID_AGENT = True` to compare the stateless bounded hybrid as
+`hybrid_agent_adjusted`. Set `RUN_ADAPTIVE_AGENT = True` to compare the
+stateful manufacturing adaptive agent as `adaptive_agent`; its strategy state
+is persisted under `adaptive_agent/skills/manufacturing-strategy/` and is
+governed by typed mutation tools. Both agent rows retain the anchor, proposed
+probability, applied adjustment, rationale, and evidence in metadata.
+The stateless hybrid and adaptive agent use distinct predictor/cache IDs, so
+the adaptive backtest cannot silently reuse the hybrid result.
+When Langfuse credentials are configured, adaptive runs emit tagged traces and
+each durable strategy mutation is also appended to the redacted
+`adaptive_agent/skills/manufacturing-strategy/.history/adaptation_audit.jsonl`
+audit. Generate the executive-ready adaptation plots and CSV summaries with:
+
+```powershell
+uv run python adaptive_agent/report.py
+```
+
+The report is written to `reports/adaptive_agent/` and includes the cumulative
+adaptation timeline, mutation activity by tool, and the underlying audit table.
+
+The [`manufacturing_stress_adaptive_model_comparison.ipynb`](manufacturing_stress_adaptive_model_comparison.ipynb)
+is a separate controlled comparison of the adaptive agent with the lite and
+advanced models. It uses ordinary cached historical backtests, separate cache
+identities, and a frozen strategy with mutation disabled; the adaptive builder
+defaults to the lite model and accepts an explicit advanced-model opt-in. Its
+advanced path uses a 4096-token response cap (the lite path remains at 512),
+leaving room for the advanced model's internal reasoning and complete
+structured forecast. A supplied `AgentConfig` keeps its own token cap. Its
+configuration cell lets you choose `BACKTEST_STRIDE_MONTHS = 3` (the faster
+default, 28 origins) or `1` (monthly origins, 84 origins); this overrides the
+smoke spec's stride only for that notebook run. Both settings keep the
+three-month horizon, so monthly forecast targets overlap. The comparison is
+read-only and presents the two models' probabilities, risk directions,
+concise rationales, supporting evidence, and countervailing evidence side by
+side for each origin; no binary decision threshold is applied.
+The paired monthly result is summarized in the executive-ready
+[`reports/adaptive_compare/`](reports/adaptive_compare/) package, which includes
+the report, figures, exact plot-source CSVs, and a manifest. Rebuild it from
+saved backtest artifacts without making LLM calls with:
+
+```bash
+uv run python implementations/manufacturing_stress_forecasting/reports/adaptive_compare/build_report.py
+```
+
+The [`manufacturing_stress_protected_evaluation_workbench.ipynb`](manufacturing_stress_protected_evaluation_workbench.ipynb)
+is the limited held-out evaluation surface. It uses
+[`manufacturing_stress_protected_eval.yaml`](specs/manufacturing_stress_protected_eval.yaml)
+with an `EvalTracker` and a five-run budget. Protected evaluation is not used
+for the ordinary lite-versus-advanced comparison.
+
+The companion [`manufacturing_stress_adaptive_agent_workbench.ipynb`](manufacturing_stress_adaptive_agent_workbench.ipynb)
+is a frozen-by-default interface for inspecting the seeded strategy, optionally
+running one adaptive forecast, reviewing mutation audits, and generating the
+same executive-ready reports under `reports/adaptive_agent_workbench/`.
+The adaptive agent uses the hybrid agent's complete structured-output
+instructions and explicitly submits all required response fields through
+`set_model_response`; the window is a historical diagnostic rather than an
+untouched future holdout.
+
+The additive
+[`manufacturing_stress_hybrid_workbench.ipynb`](manufacturing_stress_hybrid_workbench.ipynb)
+is the interface for the initial hybrid expansion. It defaults to a dry run,
+uses the full 15-feature set and the fixed candidates
+`logistic_c_0_001` and `xgb_50_depth2_lr0_03_minchild3_l2_5`. The numerical
+anchor uses the logistic candidate only; the XGBoost prediction remains an
+optional comparison model rather than part of the anchor. Set `RUN_AGENT_CALL = True` explicitly to
+make one LLM call through the existing ADK/Vector-proxy runner; the bounded
+agent adjustment is used as the forecast probability, with the anchor and
+proposal retained in metadata.
+The structured response includes a rationale limited to 40 words, the main
+supporting and countervailing evidence, and a direction (`up`, `down`, or
+`neutral`). These fields are retained with the anchor, agent proposal, applied
+adjustment, and adjusted forecast probability in the notebook output.
+
+From the repository root, put a personal FRED key in `.env` or export it:
+
+```bash
+export FRED_API_KEY="..."
+```
+
+Populate the FRED and Yahoo Finance caches and inspect the registered series:
+
+```bash
+uv run python scripts/fetch_manufacturing_stress.py
+```
+
+Run the deterministic small backtest:
+
+```bash
+uv run --directory implementations python -m manufacturing_stress_forecasting.run_smoke
+```
+
+Run the hybrid dry-run smoke interface:
+
+```bash
+uv run --directory implementations \
+  python -m manufacturing_stress_forecasting.smoke_hybrid_agent
+```
+
+Use `--run-agent` only when the Vector-proxy environment is configured and an
+explicit LLM call is intended. The hybrid smoke path is additive and does not
+change existing predictors, agents, runners, or cached artifacts.
+
+The output prints one mean Brier score per predictor; lower is better. The
+logistic model should be compared against historical frequency, not judged in
+isolation.
+
+### Regime diagnostics and hybrid weights
+
+`manufacturing_stress_workbench_2.ipynb` also reports cutoff-safe conditional
+diagnostics for the fixed forecasts. Each origin is classified independently by
+manufacturing momentum (`expanding`, `slowing`, or `stressed`), financial
+conditions (`supportive` or `restrictive`), and market volatility (`normal` or
+`elevated`). Thresholds use only observations visible at that origin. The
+notebook reports origin and event counts, observed event rates, mean predicted
+probabilities, conditional Brier scores, and probability error by regime.
+
+These regime labels are descriptive slices, not tuning rules or causal claims.
+They do not change model parameters, the deterministic hybrid anchor, or the
+protected evaluation budget.
+
+The notebook also performs an offline hybrid-weight sweep from the saved
+logistic and XGBoost probabilities. Logistic and XGBoost are each run once;
+candidate weights are scored with arithmetic over the same origin rows, so the
+sweep makes no additional model fits, agent calls, or protected-evaluation
+runs. The selected hybrid anchor is now logistic-only; the weight sweep remains
+an exploratory diagnostic.
+
+### Controlled deterministic parameter sweep
+
+`run_parameter_smoke.py` compares candidates across three chronological tuning
+folds within 2000–2017:
+
+- `tune` compares six logistic regularization values and four small XGBoost
+  configurations against historical frequency in 2000–2005, 2006–2011, and
+  2012–2017 folds. Training remains expanding and cutoff-scoped at each origin.
+- A candidate is suggested only if it beats historical frequency on pooled
+  Brier score, in at least two folds, and in an event-bearing fold. Fold tables
+  show scores, skipped origins, and resolved stress-event counts; per-origin
+  probabilities and outcomes are available in the notebook.
+- `confirm` evaluates only one selected candidate over the fixed 2018–2024
+  window. This window has already been inspected and is a historical diagnostic,
+  not an untouched holdout for selecting new parameters.
+- `stride=3` evaluates every third month and is the default. `stride=1` is a
+  slower every-month diagnostic. Use the same stride for both stages.
+
+Run the tuning stage from the repository root:
+
+```bash
+uv run --directory implementations \
+  python -m manufacturing_stress_forecasting.run_parameter_smoke
+```
+
+and positive Brier skill means the candidate beat historical frequency.
+The tuning output reports fold-level and pooled mean Brier score, the gap from
+historical frequency, Brier skill, and event counts. Lower Brier is better, a
+negative `delta_vs_baseline` is better, and positive Brier skill means the
+candidate beat historical frequency. If events appear in fewer than two folds,
+any suggested candidate is explicitly marked provisional.
+
+The tuning stage prints a stability-qualified candidate when one meets the
+selection rule. The following command reproduces the historical diagnostic; it
+does not create a fresh holdout result:
+
+```bash
+uv run --directory implementations \
+  python -m manufacturing_stress_forecasting.run_parameter_smoke \
+  --stage confirm --candidate logistic_c_0_1 --stride 3
+```
+
+For an every-month tuning diagnostic:
+
+```bash
+uv run --directory implementations \
+  python -m manufacturing_stress_forecasting.run_parameter_smoke \
+  --stage tune --stride 1
+```
+
+To run the fold-based workflow in Jupyter, open
+[`manufacturing_stress_parameter_sweep_workbench_2.ipynb`](manufacturing_stress_parameter_sweep_workbench_2.ipynb)
+and run it from top to bottom. Its controls are:
+
+```python
+STAGE = "tune"          # change to "confirm" after selecting a winner
+CANDIDATE = None        # set to the printed winner for confirmation
+BACKTEST_STRIDE = 3     # use the same value for tune and confirm
+REFRESH_INPUT_DATA = False
+RUN_SWEEP = True
+SHOW_ORIGIN_DETAILS = False
+```
+
+The notebook reports fold and pooled tables. Set `SHOW_ORIGIN_DETAILS = True`
+to inspect each scored probability and resolved label. A candidate can pass the
+rule while remaining provisional when stress events appear in only one fold.
+Because the 2018–2024 window has already been inspected, do not retune from its
+results; a clean next confirmation requires future or prospectively recorded
+outcomes.
+
+The script and notebook read the local input-data caches but do not use
+prediction-result caches or make LLM calls. Keeping tuning within 2000–2017
+and reporting regime-level stability reduces the risk of choosing parameters
+that fit only one part of the development period.
+
+Run the token-limited LLMP backtest explicitly:
+
+```bash
+uv run --directory implementations python -m manufacturing_stress_forecasting.run_agent_backtest
+```
+
+This evaluates historical frequency, logistic regression, XGBoost, and the
+`manufacturing_stress_analyst` agent through the same binary backtest and
+Brier-score calculation. The agent run uses the default lite model, a
+12-month IPMAN history, compact JSON prompts, a 384-token response cap, and
+one retry per failed origin. It uses zero-temperature generation and explicitly
+requests strict double-quoted JSON. A manufacturing-local runner safely
+normalizes the proxy's occasional single-quoted Python dictionary response
+before schema validation; the shared Vector AgentPredictor remains unchanged.
+The local predictor keeps optional Langfuse tracing disabled, so these test runs
+do not export prompts and responses or attach Langfuse trace URLs.
+Calendar dates are replaced by relative month offsets in retrospective agent
+prompts to reduce historical-event recall.
+
+Complete results are cached under a specification-fingerprinted directory in
+`data/predictions/`, so changing the stride, horizon, dates, or warmup cannot
+silently reuse an incompatible result. Incomplete runs with skipped origins
+are not cached. Use `--force-refresh` to intentionally re-run every predictor.
+The command also verifies that every reported model was scored on the exact
+same origin and forecast-date pairs.
+
+This remains a retrospective LLM pseudo-backtest: anonymizing dates reduces,
+but cannot eliminate, the possibility that a modern model recognizes a
+historical episode from its training knowledge. Use prospectively recorded
+forecasts for a clean out-of-sample LLM evaluation.
+
+Run one current forecast, including the structured agent:
+
+```bash
+uv run --directory implementations python -m manufacturing_stress_forecasting.run_agent_prediction
+```
+
+## Next steps
+
+1. Plot IPMAN and the derived stress months; confirm or revise the 2% threshold.
+2. Compare the expanded macro-panel score with the earlier IPMAN-only result.
+3. Compare the cached agent backtest against the deterministic baselines only
+   after checking scored and skipped origin counts.
diff --git a/implementations/manufacturing_stress_forecasting/__init__.py b/implementations/manufacturing_stress_forecasting/__init__.py
new file mode 100644
index 000000000..e4cbe5929
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/__init__.py
@@ -0,0 +1,16 @@
+"""Minimal IPMAN-based manufacturing-stress forecasting use case."""
+
+from manufacturing_stress_forecasting.data import (
+    IPMAN_SERIES_ID,
+    STRESS_SERIES_ID,
+    build_manufacturing_stress_service,
+)
+from manufacturing_stress_forecasting.predictors import ManufacturingStressLogisticPredictor
+
+
+__all__ = [
+    "IPMAN_SERIES_ID",
+    "STRESS_SERIES_ID",
+    "ManufacturingStressLogisticPredictor",
+    "build_manufacturing_stress_service",
+]
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diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/__init__.py b/implementations/manufacturing_stress_forecasting/adaptive_agent/__init__.py
new file mode 100644
index 000000000..ab0c0313b
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/__init__.py
@@ -0,0 +1,15 @@
+from manufacturing_stress_forecasting.adaptive_agent.agent import (
+    DEFAULT_STRATEGY_DIR,
+    ManufacturingAdaptivePromptBuilder,
+    build_manufacturing_adaptive_agent_predictor,
+)
+from manufacturing_stress_forecasting.adaptive_agent.state import ManufacturingStrategyState
+from manufacturing_stress_forecasting.adaptive_agent.tools import build_strategy_tools
+
+__all__ = [
+    "DEFAULT_STRATEGY_DIR",
+    "ManufacturingAdaptivePromptBuilder",
+    "ManufacturingStrategyState",
+    "build_manufacturing_adaptive_agent_predictor",
+    "build_strategy_tools",
+]
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/agent.py b/implementations/manufacturing_stress_forecasting/adaptive_agent/agent.py
new file mode 100644
index 000000000..281da7c54
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/agent.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from aieng.forecasting.data.context import ForecastContext
+from aieng.forecasting.evaluation.task import ForecastingTask
+from aieng.forecasting.methods.agentic import AdaptiveSkillStore, AdkTextRunnerConfig, AgentPredictor, build_adk_agent
+from aieng.forecasting.methods.agentic.agent_factory import AgentConfig
+from aieng.forecasting.models import ADVANCED_MODEL, LITE_MODEL
+from manufacturing_stress_forecasting.adaptive_agent.state import ManufacturingStrategyState
+from manufacturing_stress_forecasting.adaptive_agent.tools import build_strategy_tools
+from manufacturing_stress_forecasting.analyst_agent.json_runner import ManufacturingStressJsonRunner
+from manufacturing_stress_forecasting.hybrid_agent import (
+    HybridManufacturingStressOutput,
+    HybridManufacturingStressPromptBuilder,
+    build_hybrid_agent_config,
+)
+
+
+DEFAULT_STRATEGY_DIR = Path(__file__).parent / "skills" / "manufacturing-strategy"
+
+
+class ManufacturingAdaptivePromptBuilder(HybridManufacturingStressPromptBuilder):
+    def __init__(self, strategy_dir: Path, *, anonymize_dates: bool = False) -> None:
+        super().__init__(anonymize_dates=anonymize_dates)
+        self._store = AdaptiveSkillStore(strategy_dir, ManufacturingStrategyState)
+
+    def __call__(self, *, task: ForecastingTask, context: ForecastContext) -> str:
+        payload = json.loads(super().__call__(task=task, context=context))
+        payload["adaptive_strategy"] = self._store.load().model_dump(mode="json")
+        payload["adaptive_rules"] = (
+            "Use strategy observations as context; never change model parameters or the numerical anchor."
+        )
+        return json.dumps(payload, separators=(",", ":"))
+
+
+def build_manufacturing_adaptive_agent_predictor(
+    *,
+    strategy_dir: Path = DEFAULT_STRATEGY_DIR,
+    config: AgentConfig | None = None,
+    model: str = LITE_MODEL,
+    anonymize_dates: bool = False,
+    mutation_enabled: bool = True,
+) -> AgentPredictor:
+    resolved = config.model_copy(deep=True) if config is not None else build_hybrid_agent_config(model=model)
+    if config is None and model == ADVANCED_MODEL:
+        resolved.max_output_tokens = max(resolved.max_output_tokens or 0, 4096)
+    resolved.name = f"manufacturing_stress_adaptive_analyst_{model.replace('.', '_').replace('-', '_')}"
+    if not mutation_enabled:
+        resolved.name += "_read_only"
+    mutation_instruction = (
+        "Use the strategy mutation tools only for durable patterns supported by distinct forecast origins."
+        if mutation_enabled
+        else "Treat adaptive_strategy as read-only; do not attempt to change or save strategy state."
+    )
+    resolved.instruction += (
+        "\n\nYou are a stateful adaptive manufacturing analyst. Read adaptive_strategy before forecasting. "
+        "Use the numerical anchor as the starting point and apply only bounded adjustments. "
+        + mutation_instruction
+        + "\n\n"
+        "For every forecast, submit the complete response by calling `set_model_response` exactly once when "
+        "that tool is available. This is the forecast-output channel, not a strategy mutation tool. Pass the "
+        "complete JSON object required by the output schema, including `probability`, `rationale`, "
+        "`supporting_evidence`, `countervailing_evidence`, and `direction`. If the tool is unavailable, return "
+        "exactly one JSON object as the final response. Do not submit a partial object or extra prose.\n\n"
+        + HybridManufacturingStressOutput.prompt_schema_json()
+    )
+    resolved.extra_tools = build_strategy_tools(strategy_dir) if mutation_enabled else []
+    agent = build_adk_agent(resolved, output_schema=HybridManufacturingStressOutput)
+    runner = ManufacturingStressJsonRunner(
+        agent,
+        config=AdkTextRunnerConfig(
+            app_name="manufacturing_stress_adaptive_predictor",
+            default_user_id="forecasting_agent",
+            fresh_session_per_message=True,
+            enable_langfuse_tracing=True,
+            langfuse_tags=["manufacturing-stress", "adaptive-agent", "prediction"],
+            langfuse_trace_name="manufacturing_stress_adaptive_predictor",
+            langfuse_version="manufacturing-adaptive-v1",
+            langfuse_propagate_metadata={
+                "task_family": "manufacturing_stress",
+                "agent_variant": "adaptive",
+                "strategy_name": strategy_dir.name,
+            },
+        ),
+    )
+    return AgentPredictor(
+        agent_config=resolved,
+        prompt_builder=ManufacturingAdaptivePromptBuilder(strategy_dir, anonymize_dates=anonymize_dates),
+        output_schema=HybridManufacturingStressOutput,
+        enable_langfuse_tracing=False,
+        runner=runner,
+    )
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/report.py b/implementations/manufacturing_stress_forecasting/adaptive_agent/report.py
new file mode 100644
index 000000000..889e0b6dc
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/report.py
@@ -0,0 +1,97 @@
+"""Create executive-ready plots from the adaptive strategy audit."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+DEFAULT_AUDIT = Path(__file__).parent / "skills" / "manufacturing-strategy" / ".history" / "adaptation_audit.jsonl"
+DEFAULT_OUTPUT = Path(__file__).parent / "reports" / "adaptive_agent"
+
+
+def load_audit(path: Path) -> pd.DataFrame:
+    """Load redacted mutation events, returning an empty frame when absent."""
+    if not path.exists():
+        return pd.DataFrame(columns=["timestamp", "tool", "strategy_version", "result"])
+    rows = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]
+    frame = pd.DataFrame(rows)
+    if frame.empty:
+        return frame
+    frame["timestamp"] = pd.to_datetime(frame["timestamp"], utc=True)
+    return frame.sort_values("timestamp").reset_index(drop=True)
+
+
+def _configure_plot() -> None:
+    plt.rcParams.update(
+        {
+            "font.family": "DejaVu Sans",
+            "font.size": 11,
+            "axes.titlesize": 16,
+            "axes.labelsize": 11,
+            "figure.facecolor": "white",
+            "axes.facecolor": "#f7f8fa",
+            "axes.edgecolor": "#39424e",
+            "axes.titleweight": "bold",
+        }
+    )
+
+
+def write_report(audit_path: Path, output_dir: Path) -> list[Path]:
+    """Write executive-ready PNGs and a compact CSV summary."""
+    _configure_plot()
+    output_dir.mkdir(parents=True, exist_ok=True)
+    audit = load_audit(audit_path)
+    if audit.empty:
+        raise ValueError(f"No adaptation audit events found at {audit_path}.")
+
+    audit.to_csv(output_dir / "adaptation_audit.csv", index=False)
+    counts = audit["tool"].value_counts().sort_values()
+    figure_paths: list[Path] = []
+
+    fig, axis = plt.subplots(figsize=(13.33, 7.5), constrained_layout=True)
+    axis.step(audit["timestamp"], range(1, len(audit) + 1), where="post", color="#155e75", linewidth=2.5)
+    axis.scatter(audit["timestamp"], range(1, len(audit) + 1), color="#d97706", s=45, zorder=3)
+    axis.set_title("Manufacturing Adaptive Strategy Evolution")
+    axis.set_xlabel("UTC event time")
+    axis.set_ylabel("Cumulative durable mutations")
+    axis.grid(axis="y", alpha=0.25)
+    path = output_dir / "adaptation_timeline.png"
+    fig.savefig(path, dpi=220, bbox_inches="tight")
+    plt.close(fig)
+    figure_paths.append(path)
+
+    fig, axis = plt.subplots(figsize=(13.33, 7.5), constrained_layout=True)
+    counts.plot.barh(ax=axis, color="#155e75")
+    axis.set_title("Adaptive Agent Mutation Activity")
+    axis.set_xlabel("Number of durable state mutations")
+    axis.set_ylabel("Mutation type")
+    axis.grid(axis="x", alpha=0.25)
+    path = output_dir / "mutation_activity.png"
+    fig.savefig(path, dpi=220, bbox_inches="tight")
+    plt.close(fig)
+    figure_paths.append(path)
+
+    summary = pd.DataFrame(
+        [{"tool": tool, "mutations": count} for tool, count in Counter(audit["tool"]).items()]
+    ).sort_values("mutations", ascending=False)
+    summary.to_csv(output_dir / "mutation_summary.csv", index=False)
+    return figure_paths
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--audit", type=Path, default=DEFAULT_AUDIT)
+    parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT)
+    args = parser.parse_args()
+    paths = write_report(args.audit, args.output_dir)
+    print(f"Wrote {len(paths)} executive-ready plots and CSV summaries to {args.output_dir}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/adaptation_audit.jsonl b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/adaptation_audit.jsonl
new file mode 100644
index 000000000..be03ab6f1
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/adaptation_audit.jsonl
@@ -0,0 +1,9 @@
+{"timestamp":"2026-10-05T02:54:07.456436+00:00","event":"strategy_mutation","tool":"open_hypothesis","strategy_path":"manufacturing-strategy","strategy_version":"5ca0f5e2e3e9","result":"Opened hyp-001; 3 confirmations are required before graduation.","state_before_sha256":"3118aab6b065cc0c5339d828859e9c46c99fe51b6f7f27364888c4e7cb9e0c27","state_after_sha256":"5ca0f5e2e3e91523872ec8861390931044e6d178a6fd3770e023ffaa7a26a73f"}
+{"timestamp":"2026-10-05T02:54:08.621283+00:00","event":"strategy_mutation","tool":"record_observation","strategy_path":"manufacturing-strategy","strategy_version":"7fdc705e7171","result":"Manufacturing observation recorded.","state_before_sha256":"5ca0f5e2e3e91523872ec8861390931044e6d178a6fd3770e023ffaa7a26a73f","state_after_sha256":"7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47bf49c3f8aa55dfefcc"}
+{"timestamp":"2026-10-05T02:54:09.462313+00:00","event":"strategy_mutation","tool":"record_observation","strategy_path":"manufacturing-strategy","strategy_version":"89ab0cd60924","result":"Manufacturing observation recorded.","state_before_sha256":"7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47bf49c3f8aa55dfefcc","state_after_sha256":"89ab0cd60924f10c4fba9a874bc544131a5dee5432c5617a68ab01c4af3c352b"}
+{"timestamp":"2026-10-05T02:54:10.266227+00:00","event":"strategy_mutation","tool":"record_observation","strategy_path":"manufacturing-strategy","strategy_version":"aba23959efdb","result":"Manufacturing observation recorded.","state_before_sha256":"89ab0cd60924f10c4fba9a874bc544131a5dee5432c5617a68ab01c4af3c352b","state_after_sha256":"aba23959efdb5dc1dafc7e8c243f039ee458a725004a99d10aa60bc6ecfc1923"}
+{"timestamp":"2026-10-05T02:54:13.757865+00:00","event":"strategy_mutation","tool":"record_hypothesis_outcome","strategy_path":"manufacturing-strategy","strategy_version":"8ce7d315ce31","result":"Recorded confirmed outcome for hyp-001.","state_before_sha256":"aba23959efdb5dc1dafc7e8c243f039ee458a725004a99d10aa60bc6ecfc1923","state_after_sha256":"8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc90d2734272ad7178c7"}
+{"timestamp":"2026-10-05T02:54:14.363148+00:00","event":"strategy_mutation","tool":"record_hypothesis_outcome","strategy_path":"manufacturing-strategy","strategy_version":"8d90e2d77d9b","result":"Recorded confirmed outcome for hyp-001.","state_before_sha256":"8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc90d2734272ad7178c7","state_after_sha256":"8d90e2d77d9b0516845227849de75069c993bfa6da9a5d6d9004180e955f3f04"}
+{"timestamp":"2026-10-05T02:54:15.129828+00:00","event":"strategy_mutation","tool":"record_hypothesis_outcome","strategy_path":"manufacturing-strategy","strategy_version":"48997251b9f0","result":"Recorded confirmed outcome for hyp-001.","state_before_sha256":"8d90e2d77d9b0516845227849de75069c993bfa6da9a5d6d9004180e955f3f04","state_after_sha256":"48997251b9f02b73386e9e2a426f2c2901161db838612b77b6b738a0bdf6dac7"}
+{"timestamp":"2026-10-05T02:54:16.158524+00:00","event":"strategy_mutation","tool":"graduate_hypothesis","strategy_path":"manufacturing-strategy","strategy_version":"609d8b85ec48","result":"Graduated hyp-001 into a calibration correction.","state_before_sha256":"48997251b9f02b73386e9e2a426f2c2901161db838612b77b6b738a0bdf6dac7","state_after_sha256":"609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737b07ccdc643d1151f78"}
+{"timestamp":"2026-10-05T02:54:17.079875+00:00","event":"strategy_mutation","tool":"update_approach_narrative","strategy_path":"manufacturing-strategy","strategy_version":"ee7d4b090d88","result":"Manufacturing approach narrative updated.","state_before_sha256":"609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737b07ccdc643d1151f78","state_after_sha256":"ee7d4b090d8898fca705b884e399d1a7df6d8cf6d3e3814f019b42be2f898497"}
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025407Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025407Z.yaml
new file mode 100644
index 000000000..a59277ccf
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025407Z.yaml
@@ -0,0 +1,5 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded, evidence-backed adjustments.
+calibration_corrections: []
+hypotheses: []
+observations: []
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025408Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025408Z.yaml
new file mode 100644
index 000000000..6f56b07d3
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025408Z.yaml
@@ -0,0 +1,20 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections: []
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: open
+  confirmations: 0
+  refutations: 0
+  origins: []
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025409Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025409Z.yaml
new file mode 100644
index 000000000..cbff4b3bd
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025409Z.yaml
@@ -0,0 +1,24 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections: []
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: open
+  confirmations: 0
+  refutations: 0
+  origins: []
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025410Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025410Z.yaml
new file mode 100644
index 000000000..4053e84fc
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025410Z.yaml
@@ -0,0 +1,28 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections: []
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: open
+  confirmations: 0
+  refutations: 0
+  origins: []
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no
+    near-term manufacturing stress.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025413Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025413Z.yaml
new file mode 100644
index 000000000..f18cbf572
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025413Z.yaml
@@ -0,0 +1,32 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections: []
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: open
+  confirmations: 0
+  refutations: 0
+  origins: []
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no
+    near-term manufacturing stress.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Recent 1-month IPMAN growth (0.012%) indicates a stabilization phase, reducing
+    the probability of hitting the -2% 3-month contraction threshold by April 2017.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025414Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025414Z.yaml
new file mode 100644
index 000000000..81f639454
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025414Z.yaml
@@ -0,0 +1,33 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections: []
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: open
+  confirmations: 1
+  refutations: 0
+  origins:
+  - Manual analysis of current IPMAN trends versus threshold.
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no
+    near-term manufacturing stress.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Recent 1-month IPMAN growth (0.012%) indicates a stabilization phase, reducing
+    the probability of hitting the -2% 3-month contraction threshold by April 2017.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025415Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025415Z.yaml
new file mode 100644
index 000000000..b3b3d6496
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025415Z.yaml
@@ -0,0 +1,34 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections: []
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: open
+  confirmations: 2
+  refutations: 0
+  origins:
+  - Manual analysis of current IPMAN trends versus threshold.
+  - Macroeconomic stability assessment.
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no
+    near-term manufacturing stress.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Recent 1-month IPMAN growth (0.012%) indicates a stabilization phase, reducing
+    the probability of hitting the -2% 3-month contraction threshold by April 2017.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025416Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025416Z.yaml
new file mode 100644
index 000000000..7b9522688
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025416Z.yaml
@@ -0,0 +1,35 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections: []
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: open
+  confirmations: 3
+  refutations: 0
+  origins:
+  - Manual analysis of current IPMAN trends versus threshold.
+  - Macroeconomic stability assessment.
+  - IPMAN recovery evidence.
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no
+    near-term manufacturing stress.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Recent 1-month IPMAN growth (0.012%) indicates a stabilization phase, reducing
+    the probability of hitting the -2% 3-month contraction threshold by April 2017.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025417Z.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025417Z.yaml
new file mode 100644
index 000000000..349e723f9
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/.history/skill_state_20261005T025417Z.yaml
@@ -0,0 +1,41 @@
+schema_version: '1.0'
+approach_narrative: Start from the numerical hybrid anchor and make only bounded,
+  evidence-backed adjustments.
+calibration_corrections:
+- condition: Current 3-month trailing IPMAN contraction of -0.94% is well above the
+    stress threshold of -2.0%.
+  adjustment: Adjust anchor probability from 0.066 to 0.060 due to recent 1-month
+    stabilization and distance from the stress threshold.
+  source_hypothesis: hyp-001
+  confirmed_on: '2026-10-05'
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: confirmed
+  confirmations: 3
+  refutations: 0
+  origins:
+  - Manual analysis of current IPMAN trends versus threshold.
+  - Macroeconomic stability assessment.
+  - IPMAN recovery evidence.
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no
+    near-term manufacturing stress.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Recent 1-month IPMAN growth (0.012%) indicates a stabilization phase, reducing
+    the probability of hitting the -2% 3-month contraction threshold by April 2017.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/SKILL.md b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/SKILL.md
new file mode 100644
index 000000000..c73a8fe9a
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/SKILL.md
@@ -0,0 +1,28 @@
+---
+name: manufacturing-strategy
+description: >-
+  Governed adaptive manufacturing-stress forecasting strategy.
+---
+
+# Manufacturing Stress Strategy
+
+## Approach
+
+Start from the numerical hybrid anchor (0.066) and make a bounded adjustment to 0.060 based on recent positive IPMAN movement and significant buffer from the stress threshold. Rationale: The current trailing IPMAN contraction is far from the -2% threshold and recent monthly data shows slight improvement, justifying a small reduction in the base rate probability.
+
+## Calibration corrections
+
+| Condition | Adjustment | Source |
+|---|---|---|
+| Current 3-month trailing IPMAN contraction of -0.94% is well above the stress threshold of -2.0%. | Adjust anchor probability from 0.066 to 0.060 due to recent 1-month stabilization and distance from the stress threshold. | hyp-001 |
+
+## Open hypotheses
+
+*(None.)*
+
+## Observations
+
+- 2026-10-05: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress threshold. Recent monthly data shows stagnation rather than a downward trend. Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable, suggesting no imminent systemic shock.
+- 2026-10-05: Current 3-month trailing IPMAN change of -0.94% is well above the stress threshold of -2%.
+- 2026-10-05: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no near-term manufacturing stress.
+- 2026-10-05: Recent 1-month IPMAN growth (0.012%) indicates a stabilization phase, reducing the probability of hitting the -2% 3-month contraction threshold by April 2017.
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/skill_state.yaml b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/skill_state.yaml
new file mode 100644
index 000000000..bb01a3801
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy/skill_state.yaml
@@ -0,0 +1,44 @@
+schema_version: '1.0'
+approach_narrative: 'Start from the numerical hybrid anchor (0.066) and make a bounded
+  adjustment to 0.060 based on recent positive IPMAN movement and significant buffer
+  from the stress threshold. Rationale: The current trailing IPMAN contraction is
+  far from the -2% threshold and recent monthly data shows slight improvement, justifying
+  a small reduction in the base rate probability.'
+calibration_corrections:
+- condition: Current 3-month trailing IPMAN contraction of -0.94% is well above the
+    stress threshold of -2.0%.
+  adjustment: Adjust anchor probability from 0.066 to 0.060 due to recent 1-month
+    stabilization and distance from the stress threshold.
+  source_hypothesis: hyp-001
+  confirmed_on: '2026-10-05'
+hypotheses:
+- id: hyp-001
+  claim: Manufacturing stress is unlikely in the three-month horizon despite the current
+    trailing 3-month IPMAN contraction, as the contraction magnitude (-0.94%) is significantly
+    above the stress threshold (-2.0%) and the most recent month shows stability.
+  status: confirmed
+  confirmations: 3
+  refutations: 0
+  origins:
+  - Manual analysis of current IPMAN trends versus threshold.
+  - Macroeconomic stability assessment.
+  - IPMAN recovery evidence.
+observations:
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change is -0.94%, well above the -2% stress
+    threshold. Recent monthly data shows stagnation rather than a downward trend.
+    Macro signals like the yield spread (1.26) and credit spread (2.45) remain stable,
+    suggesting no imminent systemic shock.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Current 3-month trailing IPMAN change of -0.94% is well above the stress
+    threshold of -2%.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Macroeconomic signals (YC_SPREAD 1.26, CREDIT_SPREAD 2.45) indicate no
+    near-term manufacturing stress.
+  linked_hypothesis: hyp-001
+- date: '2026-10-05'
+  finding: Recent 1-month IPMAN growth (0.012%) indicates a stabilization phase, reducing
+    the probability of hitting the -2% 3-month contraction threshold by April 2017.
+  linked_hypothesis: hyp-001
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/state.py b/implementations/manufacturing_stress_forecasting/adaptive_agent/state.py
new file mode 100644
index 000000000..1527b832b
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/state.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from typing import Literal
+
+from aieng.forecasting.methods.agentic import AdaptiveSkillState
+from pydantic import BaseModel, Field
+
+
+class ManufacturingObservation(BaseModel):
+    date: str
+    finding: str
+    linked_hypothesis: str | None = None
+
+
+class ManufacturingHypothesis(BaseModel):
+    id: str
+    claim: str
+    status: Literal["open", "confirmed", "refuted"] = "open"
+    confirmations: int = 0
+    refutations: int = 0
+    origins: list[str] = Field(default_factory=list)
+
+
+class ManufacturingCalibrationCorrection(BaseModel):
+    condition: str
+    adjustment: str
+    source_hypothesis: str
+    confirmed_on: str
+
+
+class ManufacturingStrategyState(AdaptiveSkillState):
+    approach_narrative: str = "Start from the numerical hybrid anchor and make only bounded, evidence-backed adjustments."
+    calibration_corrections: list[ManufacturingCalibrationCorrection] = Field(default_factory=list)
+    hypotheses: list[ManufacturingHypothesis] = Field(default_factory=list)
+    observations: list[ManufacturingObservation] = Field(default_factory=list)
+
+    def build_markdown(self, skill_name: str | None = None) -> str:
+        lines = [
+            "---",
+            f"name: {skill_name or 'manufacturing-strategy'}",
+            "description: >-",
+            "  Governed adaptive manufacturing-stress forecasting strategy.",
+            "---",
+            "",
+            "# Manufacturing Stress Strategy",
+            "",
+            "## Approach",
+            "",
+            self.approach_narrative,
+            "",
+            "## Calibration corrections",
+            "",
+        ]
+        if self.calibration_corrections:
+            lines += ["| Condition | Adjustment | Source |", "|---|---|---|"]
+            lines.extend(f"| {c.condition} | {c.adjustment} | {c.source_hypothesis} |" for c in self.calibration_corrections)
+        else:
+            lines.append("*(None graduated.)*")
+        lines += ["", "## Open hypotheses", ""]
+        open_hypotheses = [h for h in self.hypotheses if h.status == "open"]
+        lines.extend(
+            [f"- `{h.id}`: {h.claim} ({h.confirmations} confirmations, {h.refutations} refutations)" for h in open_hypotheses]
+            or ["*(None.)*"]
+        )
+        lines += ["", "## Observations", ""]
+        lines.extend([f"- {o.date}: {o.finding}" for o in self.observations] or ["*(None.)*"])
+        return "\n".join(lines) + "\n"
diff --git a/implementations/manufacturing_stress_forecasting/adaptive_agent/tools.py b/implementations/manufacturing_stress_forecasting/adaptive_agent/tools.py
new file mode 100644
index 000000000..b4ac53e37
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/adaptive_agent/tools.py
@@ -0,0 +1,134 @@
+from __future__ import annotations
+
+import hashlib
+import json
+from datetime import date
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Callable
+
+from aieng.forecasting.methods.agentic import AdaptiveSkillStore
+from manufacturing_stress_forecasting.adaptive_agent.state import (
+    ManufacturingCalibrationCorrection,
+    ManufacturingHypothesis,
+    ManufacturingObservation,
+    ManufacturingStrategyState,
+)
+
+
+def _state_hash(state: ManufacturingStrategyState) -> str:
+    payload = json.dumps(state.model_dump(mode="json"), sort_keys=True, separators=(",", ":"))
+    return hashlib.sha256(payload.encode("utf-8")).hexdigest()
+
+
+def _audit_mutation(
+    audit_path: Path,
+    *,
+    tool: str,
+    strategy_dir: Path,
+    state_before: ManufacturingStrategyState,
+    state_after: ManufacturingStrategyState,
+    result: str,
+) -> None:
+    audit_path.parent.mkdir(parents=True, exist_ok=True)
+    event = {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "event": "strategy_mutation",
+        "tool": tool,
+        "strategy_path": strategy_dir.name,
+        "strategy_version": _state_hash(state_after)[:12],
+        "result": result,
+        "state_before_sha256": _state_hash(state_before),
+        "state_after_sha256": _state_hash(state_after),
+    }
+    with audit_path.open("a", encoding="utf-8") as file:
+        file.write(json.dumps(event, separators=(",", ":")) + "\n")
+
+
+def build_strategy_tools(
+    strategy_dir: Path,
+    *,
+    confirmation_threshold: int = 3,
+    audit_path: Path | None = None,
+) -> list[Callable[..., str]]:
+    store = AdaptiveSkillStore(strategy_dir, ManufacturingStrategyState, confirmation_threshold=confirmation_threshold)
+    resolved_audit_path = audit_path or strategy_dir / ".history" / "adaptation_audit.jsonl"
+
+    def save_mutation(
+        tool: str,
+        before: ManufacturingStrategyState,
+        after: ManufacturingStrategyState,
+        result: str,
+    ) -> str:
+        store.save(after)
+        _audit_mutation(
+            resolved_audit_path,
+            tool=tool,
+            strategy_dir=strategy_dir,
+            state_before=before,
+            state_after=after,
+            result=result,
+        )
+        return result
+
+    def record_observation(finding: str, linked_hypothesis: str = "") -> str:
+        state = store.load()
+        before = state.model_copy(deep=True)
+        state.observations.append(
+            ManufacturingObservation(
+                date=str(date.today()), finding=finding.strip(), linked_hypothesis=linked_hypothesis.strip() or None
+            )
+        )
+        return save_mutation("record_observation", before, state, "Manufacturing observation recorded.")
+
+    def open_hypothesis(claim: str, initial_evidence: str) -> str:
+        state = store.load()
+        before = state.model_copy(deep=True)
+        hypothesis_id = f"hyp-{len(state.hypotheses) + 1:03d}"
+        state.hypotheses.append(ManufacturingHypothesis(id=hypothesis_id, claim=claim.strip()))
+        state.observations.append(ManufacturingObservation(date=str(date.today()), finding=initial_evidence.strip(), linked_hypothesis=hypothesis_id))
+        return save_mutation(
+            "open_hypothesis",
+            before,
+            state,
+            f"Opened {hypothesis_id}; {store.confirmation_threshold} confirmations are required before graduation.",
+        )
+
+    def record_hypothesis_outcome(hypothesis_id: str, outcome: str, origin: str) -> str:
+        if outcome not in {"confirmed", "refuted"}:
+            return "Outcome must be 'confirmed' or 'refuted'."
+        state = store.load()
+        before = state.model_copy(deep=True)
+        hypothesis = next((item for item in state.hypotheses if item.id == hypothesis_id), None)
+        if hypothesis is None or hypothesis.status != "open":
+            return f"Open hypothesis {hypothesis_id!r} was not found."
+        if origin in hypothesis.origins:
+            return f"Origin {origin!r} has already been recorded for {hypothesis_id}."
+        hypothesis.origins.append(origin)
+        if outcome == "confirmed":
+            hypothesis.confirmations += 1
+        else:
+            hypothesis.refutations += 1
+        return save_mutation("record_hypothesis_outcome", before, state, f"Recorded {outcome} outcome for {hypothesis_id}.")
+
+    def graduate_hypothesis(hypothesis_id: str, condition: str, adjustment: str) -> str:
+        state = store.load()
+        before = state.model_copy(deep=True)
+        hypothesis = next((item for item in state.hypotheses if item.id == hypothesis_id), None)
+        if hypothesis is None:
+            return f"Hypothesis {hypothesis_id!r} was not found."
+        if hypothesis.confirmations < store.confirmation_threshold:
+            return f"Cannot graduate {hypothesis_id}: {hypothesis.confirmations}/{store.confirmation_threshold} confirmations."
+        if len(set(hypothesis.origins)) < store.confirmation_threshold:
+            return f"Cannot graduate {hypothesis_id}: confirmations must come from distinct origins."
+        hypothesis.status = "confirmed"
+        state.calibration_corrections.append(ManufacturingCalibrationCorrection(condition=condition.strip(), adjustment=adjustment.strip(), source_hypothesis=hypothesis_id, confirmed_on=str(date.today())))
+        return save_mutation("graduate_hypothesis", before, state, f"Graduated {hypothesis_id} into a calibration correction.")
+
+    def update_approach_narrative(new_text: str, rationale: str) -> str:
+        state = store.load()
+        before = state.model_copy(deep=True)
+        state.approach_narrative = f"{new_text.strip()} Rationale: {rationale.strip()}"
+        return save_mutation("update_approach_narrative", before, state, "Manufacturing approach narrative updated.")
+
+    return [record_observation, open_hypothesis, record_hypothesis_outcome, graduate_hypothesis, update_approach_narrative]
diff --git a/implementations/manufacturing_stress_forecasting/analyst_agent/__init__.py b/implementations/manufacturing_stress_forecasting/analyst_agent/__init__.py
new file mode 100644
index 000000000..4d704e53c
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/analyst_agent/__init__.py
@@ -0,0 +1,14 @@
+"""Quantitative-only manufacturing-stress analyst agent."""
+
+from manufacturing_stress_forecasting.analyst_agent.agent import (
+    ManufacturingStressPromptBuilder,
+    build_manufacturing_stress_agent_config,
+    build_manufacturing_stress_agent_predictor,
+)
+
+
+__all__ = [
+    "ManufacturingStressPromptBuilder",
+    "build_manufacturing_stress_agent_config",
+    "build_manufacturing_stress_agent_predictor",
+]
diff --git a/implementations/manufacturing_stress_forecasting/analyst_agent/__pycache__/__init__.cpython-312.pyc b/implementations/manufacturing_stress_forecasting/analyst_agent/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 000000000..cf59e31ce
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diff --git a/implementations/manufacturing_stress_forecasting/analyst_agent/__pycache__/agent.cpython-312.pyc b/implementations/manufacturing_stress_forecasting/analyst_agent/__pycache__/agent.cpython-312.pyc
new file mode 100644
index 000000000..ace4c4474
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diff --git a/implementations/manufacturing_stress_forecasting/analyst_agent/agent.py b/implementations/manufacturing_stress_forecasting/analyst_agent/agent.py
new file mode 100644
index 000000000..bb3a279f7
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/analyst_agent/agent.py
@@ -0,0 +1,199 @@
+"""Quantitative-only ADK agent for binary manufacturing-stress forecasts."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import pandas as pd
+from aieng.forecasting.data.context import ForecastContext
+from aieng.forecasting.evaluation.task import ForecastingTask
+from aieng.forecasting.methods.agentic import (
+    AdkTextRunnerConfig,
+    AgentPredictor,
+    DiscreteAgentForecastOutput,
+    build_adk_agent,
+)
+from aieng.forecasting.methods.agentic.agent_factory import AgentConfig
+from aieng.forecasting.models import LITE_MODEL
+from manufacturing_stress_forecasting.analyst_agent.json_runner import ManufacturingStressJsonRunner
+from manufacturing_stress_forecasting.data import IPMAN_SERIES_ID
+from manufacturing_stress_forecasting.features import (
+    FEATURE_SERIES_IDS,
+    IPMAN_FEATURE_SERIES_IDS,
+    MACRO_FEATURE_SERIES_IDS,
+    build_feature_snapshot,
+)
+from manufacturing_stress_forecasting.targets import (
+    DEFAULT_LOOKBACK_MONTHS,
+    DEFAULT_STRESS_THRESHOLD_PCT,
+)
+from pydantic import BaseModel, Field
+
+
+def _build_instruction() -> str:
+    schema = DiscreteAgentForecastOutput.prompt_schema_json()
+    return (
+        "## Role\n\n"
+        "You are a cautious U.S. manufacturing-cycle analyst. Estimate the probability that the "
+        "binary IPMAN stress event in the supplied task resolves to 1 at the specified forecast horizon.\n\n"
+        "## Rules\n\n"
+        "1. Use only the JSON payload. Do not use remembered events or facts after the forecast origin.\n"
+        "   Historical backtests may anonymize calendar dates; do not try to infer the hidden dates.\n"
+        "2. Start from the supplied historical base rate, then adjust using the supplied IPMAN and macro signals.\n"
+        "3. Treat negative IPMAN momentum, a restrictive fed funds rate, and an inverted 10Y-2Y spread "
+        "as possible evidence for stress; explain how the signals interact.\n"
+        "4. Do not double-count correlated signals or turn a weak signal into certainty.\n"
+        "5. `probability` means P(stress=1), not confidence in your explanation.\n"
+        "6. Give a rationale of at most 40 words, identify supporting and countervailing evidence, and remain calibrated.\n"
+        "7. Use `direction_bias='down'` when signals point toward manufacturing stress, `up` when they point "
+        "away from stress, and `neutral` when mixed.\n\n"
+        "## Output\n\n"
+        "Return exactly one valid JSON object matching this structure, with no markdown fence or preamble. "
+        "Use double quotes around every key and string value. Never return a Python dictionary or use "
+        "single quotes:\n\n" + schema
+    )
+
+
+class ManufacturingStressPromptBuilder(BaseModel):
+    """Serialize cutoff-safe IPMAN evidence into the agent's prompt."""
+
+    model_config = {"extra": "forbid"}
+
+    recent_history_months: int = Field(default=12, ge=6, le=120)
+    trailing_base_rate_months: int = Field(default=60, ge=12, le=240)
+    anonymize_dates: bool = False
+
+    def __call__(self, *, task: ForecastingTask, context: ForecastContext) -> str:
+        """Build one structured, cutoff-safe forecast payload."""
+        if task.payload_type != "binary" or len(task.horizons) != 1:
+            raise ValueError("ManufacturingStressPromptBuilder requires one binary forecast horizon.")
+
+        as_of = pd.Timestamp(context.as_of)
+        offset = pd.tseries.frequencies.to_offset(task.frequency)
+        forecast_date = as_of + offset * task.horizons[0]
+        ipman = context.get_series(IPMAN_SERIES_ID).sort_values("timestamp")
+        target = context.get_series(task.target_series_id).sort_values("timestamp")
+        feature_frames = {series_id: context.get_series(series_id) for series_id in FEATURE_SERIES_IDS}
+        current_signals = build_feature_snapshot(as_of, feature_frames)
+        current_ipman_signals = (
+            {series_id: current_signals[series_id] for series_id in IPMAN_FEATURE_SERIES_IDS}
+            if current_signals is not None
+            else None
+        )
+        current_macro_signals = (
+            {series_id: current_signals[series_id] for series_id in MACRO_FEATURE_SERIES_IDS}
+            if current_signals is not None
+            else None
+        )
+
+        target_values = target["value"].astype(float)
+        trailing_values = target_values.tail(self.trailing_base_rate_months)
+        recent_rows = list(
+            zip(
+                ipman["timestamp"].tail(self.recent_history_months),
+                ipman["value"].tail(self.recent_history_months),
+                ipman["released_at"].tail(self.recent_history_months),
+                strict=True,
+            )
+        )
+        if self.anonymize_dates:
+            origin_month = as_of.to_period("M").ordinal
+            recent_ipman = [
+                {
+                    "months_before_origin": origin_month - pd.Timestamp(timestamp).to_period("M").ordinal,
+                    "value": float(value),
+                }
+                for timestamp, value, _released_at in recent_rows
+            ]
+        else:
+            recent_ipman = [
+                {
+                    "reference_month": str(pd.Timestamp(timestamp).date()),
+                    "value": float(value),
+                    "released_at": str(pd.Timestamp(released_at).date()),
+                }
+                for timestamp, value, released_at in recent_rows
+            ]
+
+        payload: dict[str, Any] = {
+            "task": {
+                "task_id": task.task_id,
+                "question": task.description,
+                "horizon_months": task.horizons[0],
+            },
+            "target_definition": {
+                "event": "manufacturing stress",
+                "stress_value": 1,
+                "no_stress_value": 0,
+                "lookback_months": DEFAULT_LOOKBACK_MONTHS,
+                "threshold_pct": DEFAULT_STRESS_THRESHOLD_PCT,
+                "rule": ("stress=1 when trailing IPMAN percentage change is less than or equal to threshold_pct"),
+            },
+            "current_ipman_signals_pct": current_ipman_signals,
+            "current_macro_signals": current_macro_signals,
+            "historical_stress": {
+                "n_visible_months": len(target_values),
+                "all_history_base_rate": float(target_values.mean()) if len(target_values) else None,
+                "trailing_window_months": self.trailing_base_rate_months,
+                "trailing_base_rate": float(trailing_values.mean()) if len(trailing_values) else None,
+            },
+            "recent_ipman": recent_ipman,
+        }
+        if self.anonymize_dates:
+            payload["timing"] = {"calendar_dates_anonymized": True}
+        else:
+            payload["as_of"] = str(as_of.date())
+            payload["forecast_date"] = str(forecast_date.date())
+        return json.dumps(payload, separators=(",", ":"))
+
+
+def build_manufacturing_stress_agent_config(model: str = LITE_MODEL) -> AgentConfig:
+    """Build the tool-free manufacturing analyst configuration."""
+    return AgentConfig(
+        name="manufacturing_stress_analyst",
+        model=model,
+        instruction=_build_instruction(),
+        temperature=0.0,
+        seed=42,
+        max_output_tokens=384,
+    )
+
+
+def build_manufacturing_stress_agent_predictor(
+    config: AgentConfig | None = None,
+    *,
+    anonymize_dates: bool = False,
+) -> AgentPredictor:
+    """Wrap the analyst in the standard contract with local JSON normalization."""
+    resolved_config = config or build_manufacturing_stress_agent_config()
+    agent = build_adk_agent(resolved_config, output_schema=DiscreteAgentForecastOutput)
+    runner = ManufacturingStressJsonRunner(
+        agent,
+        config=AdkTextRunnerConfig(
+            app_name="manufacturing_stress_predictor",
+            default_user_id="forecasting_agent",
+            fresh_session_per_message=True,
+        ),
+    )
+    return AgentPredictor(
+        agent_config=resolved_config,
+        prompt_builder=ManufacturingStressPromptBuilder(anonymize_dates=anonymize_dates),
+        output_schema=DiscreteAgentForecastOutput,
+        enable_langfuse_tracing=False,
+        runner=runner,
+    )
+
+
+def __getattr__(name: str) -> Any:
+    """Expose a schema-free root agent for ``adk run`` and ``adk web``."""
+    if name == "root_agent":
+        return build_adk_agent(build_manufacturing_stress_agent_config())
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+__all__ = [
+    "ManufacturingStressPromptBuilder",
+    "build_manufacturing_stress_agent_config",
+    "build_manufacturing_stress_agent_predictor",
+]
diff --git a/implementations/manufacturing_stress_forecasting/analyst_agent/json_runner.py b/implementations/manufacturing_stress_forecasting/analyst_agent/json_runner.py
new file mode 100644
index 000000000..5d96a7917
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/analyst_agent/json_runner.py
@@ -0,0 +1,58 @@
+"""Manufacturing-local tolerance for structured agent output."""
+
+from __future__ import annotations
+
+import ast
+import json
+from typing import Any
+
+from aieng.forecasting.methods.agentic import AdkTextRunner
+from google.adk.agents.run_config import RunConfig
+
+
+def normalize_json_object(output: str) -> str:
+    """Convert a Python dict literal to JSON while leaving other text untouched.
+
+    Some proxy/ADK responses contain the correct structured fields but serialize
+    them with Python's single-quoted dict representation. ast.literal_eval
+    safely handles that narrow case without executing arbitrary model output.
+    """
+    try:
+        json.loads(output)
+    except json.JSONDecodeError:
+        try:
+            parsed = ast.literal_eval(output)
+        except (SyntaxError, ValueError):
+            return output
+
+        if not isinstance(parsed, dict):
+            return output
+        return json.dumps(parsed, separators=(",", ":"))
+
+    return output
+
+
+class ManufacturingStressJsonRunner(AdkTextRunner):
+    """Normalize a narrow proxy formatting edge case before schema validation."""
+
+    async def run_text_async(
+        self,
+        prompt: str,
+        *,
+        user_id: str | None = None,
+        session_id: str | None = None,
+        run_config: RunConfig | None = None,
+        initial_state: dict[str, Any] | None = None,
+    ) -> str:
+        """Run one agent turn and return a JSON-compatible object string."""
+        output = await super().run_text_async(
+            prompt,
+            user_id=user_id,
+            session_id=session_id,
+            run_config=run_config,
+            initial_state=initial_state,
+        )
+        return normalize_json_object(output)
+
+
+__all__ = ["ManufacturingStressJsonRunner", "normalize_json_object"]
diff --git a/implementations/manufacturing_stress_forecasting/data.py b/implementations/manufacturing_stress_forecasting/data.py
new file mode 100644
index 000000000..693a37413
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/data.py
@@ -0,0 +1,251 @@
+"""FRED and Yahoo Finance data for manufacturing-stress forecasting."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from aieng.forecasting.data import DataService, SeriesMetadata
+from aieng.forecasting.data.adapters import FREDAdapter, YFinanceDailyAdapter
+from aieng.forecasting.data.features import StaticFrameAdapter
+from manufacturing_stress_forecasting.features import (
+    CPI_YOY_SERIES_ID,
+    CPIAUCSL_SERIES_ID,
+    CREDIT_SPREAD_SERIES_ID,
+    FEATURE_PERIODS,
+    FED_FUNDS_SERIES_ID,
+    FEDFUNDS_SERIES_ID,
+    ICSA_SERIES_ID,
+    SPY_RETURN_3M_SERIES_ID,
+    SPY_RETURN_12M_SERIES_ID,
+    UNRATE_SERIES_ID,
+    VIXCLS_SERIES_ID,
+    XLI_RETURN_3M_SERIES_ID,
+    XLI_RETURN_12M_SERIES_ID,
+    YC_SPREAD_SERIES_ID,
+    YIELD_CURVE_SERIES_ID,
+    apply_conservative_monthly_release_lag,
+    build_ipman_feature_frames,
+    build_macro_feature_frames,
+)
+from manufacturing_stress_forecasting.targets import (
+    DEFAULT_LOOKBACK_MONTHS,
+    DEFAULT_STRESS_THRESHOLD_PCT,
+    derive_manufacturing_stress_labels,
+)
+
+
+IPMAN_FRED_ID = "IPMAN"
+FED_FUNDS_FRED_ID = "DFF"
+TREASURY_10Y_FRED_ID = "DGS10"
+TREASURY_2Y_FRED_ID = "DGS2"
+FEDFUNDS_FRED_ID = "FEDFUNDS"
+CPI_FRED_ID = "CPIAUCSL"
+UNRATE_FRED_ID = "UNRATE"
+ICSA_FRED_ID = "ICSA"
+VIXCLS_FRED_ID = "VIXCLS"
+CREDIT_SPREAD_FRED_ID = "BAA10Y"
+SPY_TICKER = "SPY"
+XLI_TICKER = "XLI"
+YAHOO_HISTORY_START = "1998-01-01"
+
+IPMAN_SERIES_ID = "ipman_us_manufacturing_production"
+STRESS_SERIES_ID = "manufacturing_stress"
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+DEFAULT_FRED_CACHE_DIR = _REPO_ROOT / "data" / "fred"
+DEFAULT_YAHOO_CACHE_DIR = _REPO_ROOT / "data" / "yfinance"
+
+
+def build_manufacturing_stress_service(
+    *,
+    cache_dir: str | Path = DEFAULT_FRED_CACHE_DIR,
+    yahoo_cache_dir: str | Path = DEFAULT_YAHOO_CACHE_DIR,
+    refresh: bool = False,
+    release_lag_months: int = 1,
+    stress_lookback_months: int = DEFAULT_LOOKBACK_MONTHS,
+    stress_threshold_pct: float = DEFAULT_STRESS_THRESHOLD_PCT,
+) -> DataService:
+    """Build a service containing the target and cutoff-aware input features."""
+    raw_ipman = FREDAdapter(IPMAN_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_fed_funds = FREDAdapter(FEDFUNDS_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_treasury_10y = FREDAdapter(TREASURY_10Y_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_treasury_2y = FREDAdapter(TREASURY_2Y_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_cpi = FREDAdapter(CPI_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_unemployment = FREDAdapter(UNRATE_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_initial_claims = FREDAdapter(ICSA_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_vix = FREDAdapter(VIXCLS_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_credit_spread = FREDAdapter(CREDIT_SPREAD_FRED_ID, cache_dir=cache_dir, refresh=refresh).fetch()
+    raw_spy_prices = YFinanceDailyAdapter(
+        SPY_TICKER,
+        start=YAHOO_HISTORY_START,
+        cache_dir=yahoo_cache_dir,
+        refresh=refresh,
+    ).fetch()
+    raw_xli_prices = YFinanceDailyAdapter(
+        XLI_TICKER,
+        start=YAHOO_HISTORY_START,
+        cache_dir=yahoo_cache_dir,
+        refresh=refresh,
+    ).fetch()
+
+    ipman = apply_conservative_monthly_release_lag(raw_ipman, months=release_lag_months)
+    ipman_feature_frames = build_ipman_feature_frames(ipman)
+    macro_feature_frames = build_macro_feature_frames(
+        raw_fed_funds,
+        raw_treasury_10y,
+        raw_treasury_2y,
+        raw_cpi,
+        raw_unemployment,
+        raw_initial_claims,
+        raw_vix,
+        raw_credit_spread,
+        raw_spy_prices,
+        raw_xli_prices,
+    )
+    stress = derive_manufacturing_stress_labels(
+        ipman,
+        lookback_months=stress_lookback_months,
+        threshold_pct=stress_threshold_pct,
+    )
+
+    service = DataService()
+    service.register(
+        IPMAN_SERIES_ID,
+        StaticFrameAdapter(ipman),
+        SeriesMetadata(
+            series_id=IPMAN_SERIES_ID,
+            description="U.S. manufacturing industrial production index (IPMAN)",
+            source="FRED (IPMAN)",
+            units="Index",
+            frequency="MS",
+        ),
+    )
+
+    for series_id, frame in ipman_feature_frames.items():
+        periods = FEATURE_PERIODS[series_id]
+        service.register(
+            series_id,
+            StaticFrameAdapter(frame),
+            SeriesMetadata(
+                series_id=series_id,
+                description=f"Trailing {periods}-month percentage change in IPMAN",
+                source="Derived from FRED IPMAN",
+                units="Percent",
+                frequency="MS",
+            ),
+        )
+
+    service.register(
+        FED_FUNDS_SERIES_ID,
+        StaticFrameAdapter(macro_feature_frames[FED_FUNDS_SERIES_ID]),
+        SeriesMetadata(
+            series_id=FED_FUNDS_SERIES_ID,
+            description="Month-end effective federal funds rate",
+            source="FRED (FEDFUNDS), derived monthly",
+            units="Percent",
+            frequency="MS",
+        ),
+    )
+    service.register(
+        YIELD_CURVE_SERIES_ID,
+        StaticFrameAdapter(macro_feature_frames[YIELD_CURVE_SERIES_ID]),
+        SeriesMetadata(
+            series_id=YIELD_CURVE_SERIES_ID,
+            description="Month-end 10-year minus 2-year Treasury yield spread",
+            source="FRED (DGS10 minus DGS2), derived monthly",
+            units="Percentage points",
+            frequency="MS",
+        ),
+    )
+
+    metadata = {
+        FEDFUNDS_SERIES_ID: ("Effective federal funds rate", "FRED (FEDFUNDS)", "Percent"),
+        YC_SPREAD_SERIES_ID: (
+            "10-year minus 2-year Treasury yield spread",
+            "FRED (DGS10 minus DGS2)",
+            "Percentage points",
+        ),
+        CPIAUCSL_SERIES_ID: ("Consumer Price Index for All Urban Consumers", "FRED (CPIAUCSL)", "Index"),
+        CPI_YOY_SERIES_ID: ("Consumer Price Index year-over-year change", "Derived from FRED (CPIAUCSL)", "Percent"),
+        UNRATE_SERIES_ID: ("U.S. unemployment rate", "FRED (UNRATE)", "Percent"),
+        ICSA_SERIES_ID: ("Initial claims for unemployment insurance", "FRED (ICSA)", "Number"),
+        VIXCLS_SERIES_ID: ("CBOE volatility index", "FRED (VIXCLS)", "Index"),
+        CREDIT_SPREAD_SERIES_ID: (
+            "Moody's Baa corporate bond yield minus 10-year Treasury yield (credit-spread proxy)",
+            "FRED (BAA10Y)",
+            "Percentage points",
+        ),
+        SPY_RETURN_3M_SERIES_ID: (
+            "SPY trailing 3-month adjusted-close return",
+            "Yahoo Finance (SPY), derived",
+            "Percent",
+        ),
+        SPY_RETURN_12M_SERIES_ID: (
+            "SPY trailing 12-month adjusted-close return",
+            "Yahoo Finance (SPY), derived",
+            "Percent",
+        ),
+        XLI_RETURN_3M_SERIES_ID: (
+            "XLI trailing 3-month adjusted-close return",
+            "Yahoo Finance (XLI), derived",
+            "Percent",
+        ),
+        XLI_RETURN_12M_SERIES_ID: (
+            "XLI trailing 12-month adjusted-close return",
+            "Yahoo Finance (XLI), derived",
+            "Percent",
+        ),
+    }
+    for series_id, frame in macro_feature_frames.items():
+        if series_id not in metadata:
+            continue
+        description, source, units = metadata[series_id]
+        service.register(
+            series_id,
+            StaticFrameAdapter(frame),
+            SeriesMetadata(
+                series_id=series_id,
+                description=description,
+                source=source,
+                units=units,
+                frequency="MS",
+            ),
+        )
+
+    service.register(
+        STRESS_SERIES_ID,
+        StaticFrameAdapter(stress),
+        SeriesMetadata(
+            series_id=STRESS_SERIES_ID,
+            description=(
+                "Binary U.S. manufacturing stress label: 1 when trailing "
+                f"{stress_lookback_months}-month IPMAN change is at or below {stress_threshold_pct:.1f}%"
+            ),
+            source="Derived from FRED IPMAN",
+            units="Binary event (0=no stress, 1=stress)",
+            frequency="MS",
+        ),
+    )
+    return service
+
+
+__all__ = [
+    "DEFAULT_FRED_CACHE_DIR",
+    "DEFAULT_YAHOO_CACHE_DIR",
+    "FED_FUNDS_FRED_ID",
+    "FEDFUNDS_FRED_ID",
+    "CPI_FRED_ID",
+    "UNRATE_FRED_ID",
+    "ICSA_FRED_ID",
+    "VIXCLS_FRED_ID",
+    "CREDIT_SPREAD_FRED_ID",
+    "SPY_TICKER",
+    "XLI_TICKER",
+    "YAHOO_HISTORY_START",
+    "IPMAN_FRED_ID",
+    "IPMAN_SERIES_ID",
+    "STRESS_SERIES_ID",
+    "TREASURY_10Y_FRED_ID",
+    "TREASURY_2Y_FRED_ID",
+    "build_manufacturing_stress_service",
+]
diff --git a/implementations/manufacturing_stress_forecasting/features.py b/implementations/manufacturing_stress_forecasting/features.py
new file mode 100644
index 000000000..901cafc08
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/features.py
@@ -0,0 +1,218 @@
+"""Leak-safe monthly features for manufacturing-stress forecasting."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+import pandas as pd
+from aieng.forecasting.data.features import canonical_three_col
+
+
+IPMAN_CHANGE_1M_SERIES_ID = "ipman_change_1m_pct"
+IPMAN_CHANGE_3M_SERIES_ID = "ipman_change_3m_pct"
+IPMAN_CHANGE_6M_SERIES_ID = "ipman_change_6m_pct"
+FED_FUNDS_SERIES_ID = "fed_funds_rate_pct"
+YIELD_CURVE_SERIES_ID = "treasury_10y_minus_2y_pct_points"
+FEDFUNDS_SERIES_ID = "FEDFUNDS"
+YC_SPREAD_SERIES_ID = "YC_SPREAD"
+CPIAUCSL_SERIES_ID = "CPIAUCSL"
+CPI_YOY_SERIES_ID = "CPI_YOY"
+UNRATE_SERIES_ID = "UNRATE"
+ICSA_SERIES_ID = "ICSA"
+VIXCLS_SERIES_ID = "VIXCLS"
+CREDIT_SPREAD_SERIES_ID = "CREDIT_SPREAD"
+SPY_RETURN_3M_SERIES_ID = "SPY_RETURN_3M"
+SPY_RETURN_12M_SERIES_ID = "SPY_RETURN_12M"
+XLI_RETURN_3M_SERIES_ID = "XLI_RETURN_3M"
+XLI_RETURN_12M_SERIES_ID = "XLI_RETURN_12M"
+
+FEATURE_PERIODS: dict[str, int] = {
+    IPMAN_CHANGE_1M_SERIES_ID: 1,
+    IPMAN_CHANGE_3M_SERIES_ID: 3,
+    IPMAN_CHANGE_6M_SERIES_ID: 6,
+}
+IPMAN_FEATURE_SERIES_IDS: tuple[str, ...] = tuple(FEATURE_PERIODS)
+MACRO_FEATURE_SERIES_IDS: tuple[str, ...] = (
+    FEDFUNDS_SERIES_ID,
+    YC_SPREAD_SERIES_ID,
+    CPIAUCSL_SERIES_ID,
+    CPI_YOY_SERIES_ID,
+    UNRATE_SERIES_ID,
+    ICSA_SERIES_ID,
+    VIXCLS_SERIES_ID,
+    CREDIT_SPREAD_SERIES_ID,
+    SPY_RETURN_3M_SERIES_ID,
+    SPY_RETURN_12M_SERIES_ID,
+    XLI_RETURN_3M_SERIES_ID,
+    XLI_RETURN_12M_SERIES_ID,
+)
+FEATURE_SERIES_IDS: tuple[str, ...] = IPMAN_FEATURE_SERIES_IDS + MACRO_FEATURE_SERIES_IDS
+
+
+def apply_conservative_monthly_release_lag(frame: pd.DataFrame, months: int = 1) -> pd.DataFrame:
+    """Stamp observations as available ``months`` after their reference month.
+
+    The standard FRED adapter uses ``released_at = timestamp`` because it does
+    not retrieve release vintages. For this monthly prototype, one month is a
+    deliberately conservative approximation that prevents a month-start
+    forecast from seeing that same month's completed production observation.
+    """
+    if months < 0:
+        raise ValueError(f"months must be non-negative; got {months}")
+    out = frame.copy()
+    out["released_at"] = pd.to_datetime(out["timestamp"]) + pd.offsets.MonthBegin(months)
+    return canonical_three_col(out)
+
+
+def percent_change_feature(ipman: pd.DataFrame, periods: int) -> pd.DataFrame:
+    """Return the trailing ``periods``-month IPMAN percentage change."""
+    if periods < 1:
+        raise ValueError(f"periods must be positive; got {periods}")
+    out = ipman.copy().sort_values("timestamp").reset_index(drop=True)
+    out["value"] = out["value"].pct_change(periods=periods, fill_method=None) * 100.0
+    return canonical_three_col(out)
+
+
+def build_ipman_feature_frames(ipman: pd.DataFrame) -> dict[str, pd.DataFrame]:
+    """Build the three IPMAN momentum features used by the model."""
+    return {series_id: percent_change_feature(ipman, periods) for series_id, periods in FEATURE_PERIODS.items()}
+
+
+def monthly_last_observation(frame: pd.DataFrame) -> pd.DataFrame:
+    """Collapse a daily canonical series to its final observation each month."""
+    out = canonical_three_col(frame)
+    out["month"] = out["timestamp"].dt.to_period("M")
+    out = out.sort_values(["month", "timestamp"]).groupby("month", as_index=False).tail(1)
+    out["timestamp"] = out["month"].dt.to_timestamp()
+    return canonical_three_col(out)
+
+
+def monthly_return_feature(prices: pd.DataFrame, periods: int) -> pd.DataFrame:
+    """Return trailing ``periods``-month percentage returns from daily prices."""
+    if periods < 1:
+        raise ValueError(f"periods must be positive; got {periods}")
+    monthly = monthly_last_observation(prices)
+    monthly["value"] = monthly["value"].pct_change(periods=periods, fill_method=None) * 100.0
+    return canonical_three_col(monthly.dropna(subset=["value"]).reset_index(drop=True))
+
+
+def build_macro_feature_frames(
+    fed_funds: pd.DataFrame,
+    treasury_10y: pd.DataFrame,
+    treasury_2y: pd.DataFrame,
+    cpi: pd.DataFrame | None = None,
+    unemployment: pd.DataFrame | None = None,
+    initial_claims: pd.DataFrame | None = None,
+    vix: pd.DataFrame | None = None,
+    credit_spread: pd.DataFrame | None = None,
+    spy_prices: pd.DataFrame | None = None,
+    xli_prices: pd.DataFrame | None = None,
+) -> dict[str, pd.DataFrame]:
+    """Build the monthly FRED and Yahoo Finance feature panel.
+
+    Daily FRED observations are treated as available on the next business day.
+    The model then uses the final published observation associated with each
+    calendar month. This keeps the monthly feature panel small while preserving
+    an honest ``released_at`` cutoff.
+    """
+
+    def monthly_level(frame: pd.DataFrame) -> pd.DataFrame:
+        value = canonical_three_col(frame)
+        value["released_at"] = value["timestamp"] + pd.offsets.BDay(1)
+        return monthly_last_observation(value)
+
+    fed_monthly = monthly_level(fed_funds)
+
+    ten_year = canonical_three_col(treasury_10y).rename(
+        columns={"value": "value_10y", "released_at": "released_at_10y"}
+    )
+    two_year = canonical_three_col(treasury_2y).rename(columns={"value": "value_2y", "released_at": "released_at_2y"})
+    spread = pd.merge(ten_year, two_year, on="timestamp", how="inner")
+    spread["value"] = spread["value_10y"] - spread["value_2y"]
+    spread["released_at"] = spread[["released_at_10y", "released_at_2y"]].max(axis=1) + pd.offsets.BDay(1)
+    spread_monthly = monthly_last_observation(spread[["timestamp", "value", "released_at"]])
+
+    features = {
+        FED_FUNDS_SERIES_ID: fed_monthly,
+        YIELD_CURVE_SERIES_ID: spread_monthly,
+    }
+    if all(
+        frame is not None
+        for frame in (cpi, unemployment, initial_claims, vix, credit_spread, spy_prices, xli_prices)
+    ):
+        cpi_monthly = monthly_level(cpi)
+        cpi_yoy = cpi_monthly.copy()
+        cpi_yoy["value"] = cpi_yoy["value"].pct_change(periods=12, fill_method=None) * 100.0
+        cpi_yoy = canonical_three_col(cpi_yoy.dropna(subset=["value"]).reset_index(drop=True))
+        features.update(
+            {
+                FEDFUNDS_SERIES_ID: fed_monthly,
+                YC_SPREAD_SERIES_ID: spread_monthly,
+                CPIAUCSL_SERIES_ID: cpi_monthly,
+                CPI_YOY_SERIES_ID: cpi_yoy,
+                UNRATE_SERIES_ID: monthly_level(unemployment),
+                ICSA_SERIES_ID: monthly_level(initial_claims),
+                VIXCLS_SERIES_ID: monthly_level(vix),
+                CREDIT_SPREAD_SERIES_ID: monthly_level(credit_spread),
+                SPY_RETURN_3M_SERIES_ID: monthly_return_feature(spy_prices, periods=3),
+                SPY_RETURN_12M_SERIES_ID: monthly_return_feature(spy_prices, periods=12),
+                XLI_RETURN_3M_SERIES_ID: monthly_return_feature(xli_prices, periods=3),
+                XLI_RETURN_12M_SERIES_ID: monthly_return_feature(xli_prices, periods=12),
+            }
+        )
+    return features
+
+
+def build_feature_snapshot(
+    origin: pd.Timestamp,
+    feature_frames: dict[str, pd.DataFrame],
+    *,
+    series_ids: Sequence[str] = FEATURE_SERIES_IDS,
+) -> dict[str, float] | None:
+    """Return the latest feature values that were published by ``origin``.
+
+    Filtering on ``released_at`` here is essential when reconstructing older
+    training examples: the surrounding ``ForecastContext`` protects the current
+    forecast origin, while this function recreates the stricter cutoff at each
+    past origin used to train the fit-at-origin logistic model.
+    """
+    snapshot: dict[str, float] = {}
+    for series_id in series_ids:
+        frame = feature_frames[series_id]
+        visible = frame[pd.to_datetime(frame["released_at"]) <= origin]
+        if visible.empty:
+            return None
+        snapshot[series_id] = float(visible.sort_values("timestamp")["value"].iloc[-1])
+    return snapshot
+
+
+__all__ = [
+    "FED_FUNDS_SERIES_ID",
+    "FEDFUNDS_SERIES_ID",
+    "FEATURE_PERIODS",
+    "FEATURE_SERIES_IDS",
+    "IPMAN_FEATURE_SERIES_IDS",
+    "IPMAN_CHANGE_1M_SERIES_ID",
+    "IPMAN_CHANGE_3M_SERIES_ID",
+    "IPMAN_CHANGE_6M_SERIES_ID",
+    "MACRO_FEATURE_SERIES_IDS",
+    "YC_SPREAD_SERIES_ID",
+    "CPIAUCSL_SERIES_ID",
+    "CPI_YOY_SERIES_ID",
+    "UNRATE_SERIES_ID",
+    "ICSA_SERIES_ID",
+    "VIXCLS_SERIES_ID",
+    "CREDIT_SPREAD_SERIES_ID",
+    "SPY_RETURN_3M_SERIES_ID",
+    "SPY_RETURN_12M_SERIES_ID",
+    "XLI_RETURN_3M_SERIES_ID",
+    "XLI_RETURN_12M_SERIES_ID",
+    "YIELD_CURVE_SERIES_ID",
+    "apply_conservative_monthly_release_lag",
+    "build_feature_snapshot",
+    "build_ipman_feature_frames",
+    "build_macro_feature_frames",
+    "monthly_last_observation",
+    "monthly_return_feature",
+    "percent_change_feature",
+]
diff --git a/implementations/manufacturing_stress_forecasting/hybrid_agent/__init__.py b/implementations/manufacturing_stress_forecasting/hybrid_agent/__init__.py
new file mode 100644
index 000000000..9ccedea62
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/hybrid_agent/__init__.py
@@ -0,0 +1,23 @@
+"""Opt-in hybrid manufacturing-stress forecasting components."""
+
+from manufacturing_stress_forecasting.hybrid_agent.agent import (
+    HybridAgentPredictor,
+    HybridManufacturingStressOutput,
+    HybridManufacturingStressPromptBuilder,
+    NumericalAnchor,
+    build_hybrid_agent_config,
+    build_hybrid_agent_predictor,
+    build_numerical_anchor,
+    validate_agent_adjustment,
+)
+
+__all__ = [
+    "HybridAgentPredictor",
+    "HybridManufacturingStressOutput",
+    "HybridManufacturingStressPromptBuilder",
+    "NumericalAnchor",
+    "build_hybrid_agent_config",
+    "build_hybrid_agent_predictor",
+    "build_numerical_anchor",
+    "validate_agent_adjustment",
+]
diff --git a/implementations/manufacturing_stress_forecasting/hybrid_agent/agent.py b/implementations/manufacturing_stress_forecasting/hybrid_agent/agent.py
new file mode 100644
index 000000000..e6b9f9ac4
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/hybrid_agent/agent.py
@@ -0,0 +1,288 @@
+"""Opt-in hybrid manufacturing-stress forecasting components."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Any, ClassVar, Literal
+
+import pandas as pd
+from aieng.forecasting.data.context import ForecastContext
+from aieng.forecasting.evaluation.prediction import BinaryForecast, Prediction
+from aieng.forecasting.evaluation.predictor import Predictor
+from aieng.forecasting.evaluation.task import ForecastingTask
+from aieng.forecasting.methods.agentic import AgentPredictor
+from aieng.forecasting.methods.agentic.agent_factory import AgentConfig
+from aieng.forecasting.methods.agentic.outputs import AgentForecastOutput
+from manufacturing_stress_forecasting.analyst_agent import (
+    ManufacturingStressPromptBuilder,
+    build_manufacturing_stress_agent_config,
+)
+from manufacturing_stress_forecasting.analyst_agent.json_runner import ManufacturingStressJsonRunner
+from manufacturing_stress_forecasting.predictors import (
+    ManufacturingStressLogisticPredictor,
+    ManufacturingStressXGBoostPredictor,
+)
+from pydantic import Field, field_validator
+
+
+MAX_AGENT_ADJUSTMENT = 0.03
+MIN_PROBABILITY = 0.01
+MAX_PROBABILITY = 0.99
+LOGISTIC_CANDIDATE = "logistic_c_0_001"
+XGBOOST_CANDIDATE = "xgb_50_depth2_lr0_03_minchild3_l2_5"
+
+
+@dataclass(frozen=True)
+class NumericalAnchor:
+    """The fixed logistic candidate used as the transparent numerical anchor."""
+
+    logistic_probability: float
+    xgboost_probability: float | None = None
+
+    @property
+    def probability(self) -> float:
+        return self.logistic_probability
+
+    def as_dict(self) -> dict[str, float | str]:
+        return {
+            "logistic_probability": self.logistic_probability,
+            "xgboost_probability": self.xgboost_probability,
+            "anchor_probability": self.probability,
+            "anchor_method": "logistic_only",
+        }
+        if self.xgboost_probability is not None:
+            values["xgboost_probability"] = self.xgboost_probability
+        return values
+
+
+class HybridManufacturingStressOutput(AgentForecastOutput):
+    """Structured hybrid response with concise rationale and explicit evidence."""
+
+    model_config = {"extra": "ignore"}
+
+    modality: ClassVar[Literal["discrete"]] = "discrete"
+
+    probability: float = Field(ge=0.0, le=1.0, description="Proposed probability that manufacturing stress occurs.")
+    rationale: str = Field(min_length=1, description="Concise explanation in no more than 40 words.")
+    supporting_evidence: list[str] = Field(min_length=1, description="Main supplied signals supporting the estimate.")
+    countervailing_evidence: list[str] = Field(
+        min_length=1,
+        description="Main supplied signals against the estimate, or a string stating none are material.",
+    )
+    direction: Literal["up", "down", "neutral"] = Field(
+        description="Direction of manufacturing-stress risk: higher, lower, or mixed/unclear.",
+    )
+
+    @field_validator("rationale")
+    @classmethod
+    def _rationale_is_at_most_40_words(cls, value: str) -> str:
+        if len(value.split()) > 40:
+            raise ValueError("rationale must be no more than 40 words.")
+        return value
+
+    @classmethod
+    def prompt_schema_json(cls) -> str:
+        """Return the exact JSON shape required from the hybrid analyst."""
+        return json.dumps(
+            {
+                "probability": "<float in [0, 1]>",
+                "rationale": "<at most 40 words>",
+                "supporting_evidence": ["<main supporting signal>"],
+                "countervailing_evidence": ["<main countervailing signal, or state none are material>"],
+                "direction": "<up | down | neutral>",
+            },
+            indent=2,
+        )
+
+    def to_predictions(
+        self,
+        *,
+        task: ForecastingTask,
+        context: ForecastContext,
+        predictor_id: str,
+        metadata: dict[str, Any] | None = None,
+    ) -> list[Prediction]:
+        """Convert the structured hybrid response into a binary prediction."""
+        if len(task.horizons) != 1:
+            raise ValueError("Hybrid manufacturing-stress output expects exactly one task horizon.")
+
+        horizon = task.horizons[0]
+        offset = pd.tseries.frequencies.to_offset(task.frequency)
+        prediction_metadata = dict(metadata) if metadata is not None else {}
+        prediction_metadata.update(
+            {
+                "rationale": self.rationale,
+                "supporting_evidence": list(self.supporting_evidence),
+                "countervailing_evidence": list(self.countervailing_evidence),
+                "direction": self.direction,
+            }
+        )
+        return [
+            Prediction(
+                predictor_id=predictor_id,
+                task_id=task.task_id,
+                issued_at=datetime.now(tz=timezone.utc).replace(tzinfo=None),
+                as_of=context.as_of,
+                forecast_date=(pd.Timestamp(context.as_of) + offset * horizon).to_pydatetime(),
+                payload=BinaryForecast(probability=self.probability),
+                metadata=prediction_metadata,
+            )
+        ]
+
+
+def build_numerical_anchor(task: ForecastingTask, context: ForecastContext) -> NumericalAnchor:
+    """Run the selected logistic candidate as the numerical anchor."""
+    logistic = ManufacturingStressLogisticPredictor(regularization_c=0.001)
+    logistic_prediction = logistic.predict(task, context)[0]
+    return NumericalAnchor(logistic_probability=logistic_prediction.payload.probability)
+
+
+def validate_agent_adjustment(
+    anchor_probability: float,
+    proposed_probability: float,
+    *,
+    max_adjustment: float = MAX_AGENT_ADJUSTMENT,
+) -> dict[str, Any]:
+    """Clamp the agent proposal to the allowed range and return the adjusted forecast probability."""
+    if not 0.0 <= anchor_probability <= 1.0:
+        raise ValueError(f"anchor_probability must be in [0, 1]; got {anchor_probability}.")
+    if not 0.0 <= proposed_probability <= 1.0:
+        raise ValueError(f"proposed_probability must be in [0, 1]; got {proposed_probability}.")
+    raw_adjustment = proposed_probability - anchor_probability
+    adjustment = max(-max_adjustment, min(max_adjustment, raw_adjustment))
+    adjusted_probability = min(MAX_PROBABILITY, max(MIN_PROBABILITY, anchor_probability + adjustment))
+    return {
+        "raw_agent_adjustment": raw_adjustment,
+        "agent_adjustment": adjustment,
+        "adjusted_probability": adjusted_probability,
+        "validation_status": "passed" if abs(raw_adjustment) <= max_adjustment else "clamped",
+    }
+
+
+class HybridManufacturingStressPromptBuilder:
+    """Add the numerical anchor to the existing cutoff-safe agent payload."""
+
+    def __init__(self, *, anonymize_dates: bool = False) -> None:
+        self._base = ManufacturingStressPromptBuilder(anonymize_dates=anonymize_dates)
+
+    def __call__(self, *, task: ForecastingTask, context: ForecastContext) -> str:
+        payload = json.loads(self._base(task=task, context=context))
+        payload["statistical_anchor"] = build_numerical_anchor(task, context).as_dict()
+        payload["hybrid_mode"] = "agent_adjusted"
+        return json.dumps(payload, separators=(",", ":"))
+
+
+def build_hybrid_agent_config(model: str | None = None) -> AgentConfig:
+    """Build an opt-in agent config using the repository's existing ADK path."""
+    base = build_manufacturing_stress_agent_config(model=model) if model else build_manufacturing_stress_agent_config()
+    base.instruction = (
+        "You are a cautious U.S. manufacturing-cycle analyst. Based only on the supplied IPMAN and "
+        "macroeconomic signals, estimate the probability of manufacturing stress three months ahead.\n\n"
+        "Rules:\n"
+        "1. Use only the JSON payload and information available at its forecast origin.\n"
+        "2. Start from statistical_anchor.anchor_probability; return a proposed probability. The Python "
+        "wrapper bounds the adjustment and uses the resulting adjusted probability as the forecast.\n"
+        "3. Use the supplied IPMAN and macro signals as evidence. Do not double-count correlated signals "
+        "or claim evidence that is not present in the payload.\n"
+        "4. Explain your estimate in rationale using no more than 40 words.\n"
+        "5. Identify the main supporting and countervailing evidence in their respective JSON arrays. "
+        "If no material evidence exists for one side, say so in that array.\n"
+        "6. probability means P(manufacturing stress=1). direction is about stress risk: up means "
+        "higher risk, down means lower risk, and neutral means mixed or unclear signals.\n\n"
+        "Return exactly one valid JSON object matching this structure, with double-quoted keys and strings, "
+        "and no markdown fence or preamble:\n\n" + HybridManufacturingStressOutput.prompt_schema_json()
+    )
+    base.name = "manufacturing_stress_hybrid_analyst"
+    base.max_output_tokens = 512
+    return base
+
+
+def build_hybrid_agent_predictor(
+    *,
+    config: AgentConfig | None = None,
+    anonymize_dates: bool = False,
+) -> AgentPredictor:
+    """Build the hybrid agent through the standard AgentPredictor and runner APIs."""
+    from aieng.forecasting.methods.agentic import AdkTextRunnerConfig, build_adk_agent  # noqa: PLC0415
+
+    resolved = config or build_hybrid_agent_config()
+    agent = build_adk_agent(resolved, output_schema=HybridManufacturingStressOutput)
+    runner = ManufacturingStressJsonRunner(
+        agent,
+        config=AdkTextRunnerConfig(
+            app_name="manufacturing_stress_hybrid_predictor",
+            default_user_id="forecasting_agent",
+            fresh_session_per_message=True,
+        ),
+    )
+    return AgentPredictor(
+        agent_config=resolved,
+        prompt_builder=HybridManufacturingStressPromptBuilder(anonymize_dates=anonymize_dates),
+        output_schema=HybridManufacturingStressOutput,
+        enable_langfuse_tracing=False,
+        runner=runner,
+    )
+
+
+class HybridAgentPredictor(Predictor):
+    """Use the bounded agent adjustment as the forecast and retain its evidence."""
+
+    def __init__(
+        self,
+        agent_predictor: AgentPredictor,
+        *,
+        predictor_id: str = "manufacturing_stress_hybrid_agent_v2",
+    ) -> None:
+        self._agent_predictor = agent_predictor
+        self._predictor_id = predictor_id
+
+    @property
+    def predictor_id(self) -> str:
+        return self._predictor_id
+
+    def predict(self, task: ForecastingTask, context: ForecastContext) -> list[Prediction]:
+        anchor = build_numerical_anchor(task, context)
+        agent_prediction = self._agent_predictor.predict(task, context)[0]
+        proposed = agent_prediction.payload.probability
+        validation = validate_agent_adjustment(anchor.probability, proposed)
+        return [
+            Prediction(
+                predictor_id=self.predictor_id,
+                task_id=task.task_id,
+                issued_at=datetime.now(tz=timezone.utc).replace(tzinfo=None),
+                as_of=context.as_of,
+                forecast_date=agent_prediction.forecast_date,
+                payload=BinaryForecast(probability=validation["adjusted_probability"]),
+                metadata={
+                    **anchor.as_dict(),
+                    **validation,
+                    "agent_probability": proposed,
+                    "rationale": agent_prediction.metadata["rationale"],
+                    "supporting_evidence": agent_prediction.metadata["supporting_evidence"],
+                    "countervailing_evidence": agent_prediction.metadata["countervailing_evidence"],
+                    "direction": agent_prediction.metadata["direction"],
+                    "agent_metadata": agent_prediction.metadata,
+                },
+            )
+        ]
+
+
+# Preserve the former import name for callers migrating from shadow-only semantics.
+HybridShadowPredictor = HybridAgentPredictor
+
+
+__all__ = [
+    "HybridAgentPredictor",
+    "HybridShadowPredictor",
+    "HybridManufacturingStressOutput",
+    "LOGISTIC_CANDIDATE",
+    "MAX_AGENT_ADJUSTMENT",
+    "NumericalAnchor",
+    "XGBOOST_CANDIDATE",
+    "build_hybrid_agent_config",
+    "build_hybrid_agent_predictor",
+    "build_numerical_anchor",
+    "validate_agent_adjustment",
+]
diff --git a/implementations/manufacturing_stress_forecasting/implementations/manufacturing_stress_forecasting/reports/parameter_sweep_results.png b/implementations/manufacturing_stress_forecasting/implementations/manufacturing_stress_forecasting/reports/parameter_sweep_results.png
new file mode 100644
index 000000000..ab48af7bc
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/implementations/manufacturing_stress_forecasting/reports/parameter_sweep_results.png differ
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_adaptive_agent_workbench.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_adaptive_agent_workbench.ipynb
new file mode 100644
index 000000000..1c9638ab1
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_adaptive_agent_workbench.ipynb
@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66094126",
+   "metadata": {},
+   "source": [
+    "# Manufacturing Stress Adaptive-Agent Workbench\n",
+    "\n",
+    "A companion to `manufacturing_stress_hybrid_workbench` for inspecting the persistent adaptive strategy, reviewing durable mutations, and generating executive-ready adaptation reports. This notebook intentionally does not run forecasts, backtests, LLM calls, or strategy mutations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "587bc515",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'run_adaptive_agent': True, 'run_adaptive_backtest': True, 'live_adaptation': False, 'as_of': '2017-01-01'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import hashlib\n",
+    "import json\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import yaml\n",
+    "from dotenv import load_dotenv\n",
+    "from IPython.display import display\n",
+    "\n",
+    "REPO_ROOT = next((p for p in (Path.cwd().resolve(), *Path.cwd().resolve().parents) if (p / \"uv.lock\").exists() and (p / \"implementations\").is_dir()), None)\n",
+    "if REPO_ROOT is None:\n",
+    "    raise FileNotFoundError(\"Repository root not found.\")\n",
+    "for package_root in (REPO_ROOT / \"aieng-forecasting\", REPO_ROOT / \"implementations\"):\n",
+    "    if str(package_root) not in sys.path:\n",
+    "        sys.path.insert(0, str(package_root))\n",
+    "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+    "\n",
+    "RUN_ADAPTIVE_AGENT = True\n",
+    "LIVE_ADAPTATION = False\n",
+    "REFRESH_INPUT_DATA = False\n",
+    "AS_OF = \"2017-01-01\"\n",
+    "STRATEGY_DIR = REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"adaptive_agent\" / \"skills\" / \"manufacturing-strategy\"\n",
+    "REPORT_DIR = REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"reports\" / \"adaptive_agent_workbench\"\n",
+    "REPORT_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "print({\"run_adaptive_agent\": RUN_ADAPTIVE_AGENT, \"live_adaptation\": LIVE_ADAPTATION, \"as_of\": AS_OF})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0e6a811c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running: uv sync --all-extras --dev --all-packages\n",
+      "\n",
+      "Environment synchronized successfully.\n",
+      "If packages changed, restart the notebook kernel before continuing.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cell 0: synchronize the repository environment before running the workbench.\n",
+    "import subprocess\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "def find_repo_root(start: Path) -> Path:\n",
+    "    \"\"\"Find the workspace root without confusing member pyproject files for it.\"\"\"\n",
+    "    for candidate in (start, *start.parents):\n",
+    "        if (\n",
+    "            (candidate / \"uv.lock\").exists()\n",
+    "            and (candidate / \"implementations\").is_dir()\n",
+    "            and (candidate / \"aieng-forecasting\").is_dir()\n",
+    "        ):\n",
+    "            return candidate\n",
+    "    raise FileNotFoundError(\"Could not find the agentic-forecasting repository root.\")\n",
+    "\n",
+    "\n",
+    "SETUP_ROOT = find_repo_root(Path.cwd().resolve())\n",
+    "print(\"Running: uv sync --all-extras --dev --all-packages\")\n",
+    "setup_result = subprocess.run(\n",
+    "    [\"uv\", \"sync\", \"--all-extras\", \"--dev\", \"--all-packages\"],\n",
+    "    cwd=SETUP_ROOT,\n",
+    "    check=False,\n",
+    "    text=True,\n",
+    "    capture_output=True,\n",
+    ")\n",
+    "print(setup_result.stdout)\n",
+    "if setup_result.returncode != 0:\n",
+    "    print(setup_result.stderr)\n",
+    "    raise RuntimeError(\"uv sync failed. Install uv or restart the notebook with the repository environment selected.\")\n",
+    "print(\"Environment synchronized successfully.\")\n",
+    "print(\"If packages changed, restart the notebook kernel before continuing.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b27cd87e",
+   "metadata": {},
+   "source": [
+    "## Load data, task specification, and strategy state\n",
+    "\n",
+    "The notebook uses the same cutoff-aware manufacturing service and smoke task specification as the hybrid workbench. Adaptive execution is opt-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2813cb59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'state_hash': 'ee7d4b090d8898fca705b884e399d1a7df6d8cf6d3e3814f019b42be2f898497', 'strategy_dir': '/home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy', 'target': 'manufacturing_stress'}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>status</th>\n",
+       "      <th>claim</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>confirmed</td>\n",
+       "      <td>Manufacturing stress is unlikely in the three-...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      status                                              claim\n",
+       "0  confirmed  Manufacturing stress is unlikely in the three-..."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from aieng.forecasting.evaluation import BacktestSpec\n",
+    "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+    "from manufacturing_stress_forecasting.adaptive_agent.state import ManufacturingStrategyState\n",
+    "\n",
+    "SPEC_PATH = REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"specs\" / \"manufacturing_stress_smoke.yaml\"\n",
+    "with SPEC_PATH.open(encoding=\"utf-8\") as file:\n",
+    "    base_spec = BacktestSpec.model_validate(yaml.safe_load(file))\n",
+    "spec = base_spec\n",
+    "service = build_manufacturing_stress_service(cache_dir=REPO_ROOT / \"data\" / \"fred\", yahoo_cache_dir=REPO_ROOT / \"data\" / \"yfinance\", refresh=REFRESH_INPUT_DATA)\n",
+    "context = service.context(as_of=pd.Timestamp(AS_OF).to_pydatetime())\n",
+    "state_path = STRATEGY_DIR / \"skill_state.yaml\"\n",
+    "state = ManufacturingStrategyState.model_validate(yaml.safe_load(state_path.read_text(encoding=\"utf-8\")))\n",
+    "state_json = json.dumps(state.model_dump(mode=\"json\"), sort_keys=True, separators=(\",\", \":\"))\n",
+    "state_hash = hashlib.sha256(state_json.encode()).hexdigest()\n",
+    "print({\"state_hash\": state_hash, \"strategy_dir\": str(STRATEGY_DIR), \"target\": spec.task.target_series_id})\n",
+    "display(pd.DataFrame([{\"status\": h.status, \"claim\": h.claim} for h in state.hypotheses]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf1a845c",
+   "metadata": {},
+   "source": [
+    "## Optional adaptive-agent execution\n",
+    "\n",
+    "Set `RUN_ADAPTIVE_AGENT = True` to execute the stateful agent. The agent is built with its durable strategy mutation tools; leave the toggle false for inspection-only runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7172883",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/coder/agentic-forecasting/.venv/lib/python3.12/site-packages/google/adk/tools/function_tool.py:95: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n",
+      "  build_function_declaration(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predictor_id='agent_predictor_manufacturing_stress_adaptive_analyst_gemini_3_1_flash_lite_preview_gemini-3.1-flash-lite-preview_discrete' task_id='manufacturing_stress_3m' issued_at=datetime.datetime(2026, 10, 5, 3, 8, 2, 599871) as_of=datetime.datetime(2017, 1, 1, 0, 0) forecast_date=datetime.datetime(2017, 4, 1, 0, 0) payload=BinaryForecast(probability=0.06) metadata={'rationale': 'The 3-month IPMAN contraction of -0.94% remains well above the -2% stress threshold. Recent monthly stabilization and stable macro indicators (yield and credit spreads) support a slightly lower probability than the statistical anchor.', 'supporting_evidence': ['Current 3-month trailing IPMAN contraction of -0.94% is significantly above the -2% stress threshold.'], 'countervailing_evidence': ['None are material.'], 'direction': 'down', 'langfuse_trace_id': '5c53803b71b1b69a023998f05ce03f8b', 'langfuse_trace_url': 'https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/5c53803b71b1b69a023998f05ce03f8b'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "if RUN_ADAPTIVE_AGENT:\n",
+    "    from manufacturing_stress_forecasting.adaptive_agent.agent import build_manufacturing_adaptive_agent_predictor\n",
+    "    adaptive_predictor = build_manufacturing_adaptive_agent_predictor(strategy_dir=STRATEGY_DIR)\n",
+    "    adaptive_result = adaptive_predictor.predict(spec.task, context)[0]\n",
+    "    print(adaptive_result)\n",
+    "else:\n",
+    "    print(\"Adaptive-agent execution skipped; inspection-only mode is active.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c293d36",
+   "metadata": {},
+   "source": [
+    "## Adaptation audit and executive report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0a73b20d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>event</th>\n",
+       "      <th>tool</th>\n",
+       "      <th>strategy_path</th>\n",
+       "      <th>strategy_version</th>\n",
+       "      <th>result</th>\n",
+       "      <th>state_before_sha256</th>\n",
+       "      <th>state_after_sha256</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2026-10-05 02:54:07.456436+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>open_hypothesis</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>5ca0f5e2e3e9</td>\n",
+       "      <td>Opened hyp-001; 3 confirmations are required b...</td>\n",
+       "      <td>3118aab6b065cc0c5339d828859e9c46c99fe51b6f7f27...</td>\n",
+       "      <td>5ca0f5e2e3e91523872ec8861390931044e6d178a6fd37...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2026-10-05 02:54:08.621283+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>record_observation</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>7fdc705e7171</td>\n",
+       "      <td>Manufacturing observation recorded.</td>\n",
+       "      <td>5ca0f5e2e3e91523872ec8861390931044e6d178a6fd37...</td>\n",
+       "      <td>7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2026-10-05 02:54:09.462313+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>record_observation</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>89ab0cd60924</td>\n",
+       "      <td>Manufacturing observation recorded.</td>\n",
+       "      <td>7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47...</td>\n",
+       "      <td>89ab0cd60924f10c4fba9a874bc544131a5dee5432c561...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2026-10-05 02:54:10.266227+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>record_observation</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>aba23959efdb</td>\n",
+       "      <td>Manufacturing observation recorded.</td>\n",
+       "      <td>89ab0cd60924f10c4fba9a874bc544131a5dee5432c561...</td>\n",
+       "      <td>aba23959efdb5dc1dafc7e8c243f039ee458a725004a99...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2026-10-05 02:54:13.757865+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>record_hypothesis_outcome</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>8ce7d315ce31</td>\n",
+       "      <td>Recorded confirmed outcome for hyp-001.</td>\n",
+       "      <td>aba23959efdb5dc1dafc7e8c243f039ee458a725004a99...</td>\n",
+       "      <td>8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2026-10-05 02:54:14.363148+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>record_hypothesis_outcome</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>8d90e2d77d9b</td>\n",
+       "      <td>Recorded confirmed outcome for hyp-001.</td>\n",
+       "      <td>8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc...</td>\n",
+       "      <td>8d90e2d77d9b0516845227849de75069c993bfa6da9a5d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2026-10-05 02:54:15.129828+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>record_hypothesis_outcome</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>48997251b9f0</td>\n",
+       "      <td>Recorded confirmed outcome for hyp-001.</td>\n",
+       "      <td>8d90e2d77d9b0516845227849de75069c993bfa6da9a5d...</td>\n",
+       "      <td>48997251b9f02b73386e9e2a426f2c2901161db838612b...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2026-10-05 02:54:16.158524+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>graduate_hypothesis</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>609d8b85ec48</td>\n",
+       "      <td>Graduated hyp-001 into a calibration correction.</td>\n",
+       "      <td>48997251b9f02b73386e9e2a426f2c2901161db838612b...</td>\n",
+       "      <td>609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2026-10-05 02:54:17.079875+00:00</td>\n",
+       "      <td>strategy_mutation</td>\n",
+       "      <td>update_approach_narrative</td>\n",
+       "      <td>manufacturing-strategy</td>\n",
+       "      <td>ee7d4b090d88</td>\n",
+       "      <td>Manufacturing approach narrative updated.</td>\n",
+       "      <td>609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737...</td>\n",
+       "      <td>ee7d4b090d8898fca705b884e399d1a7df6d8cf6d3e381...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         timestamp              event  \\\n",
+       "0 2026-10-05 02:54:07.456436+00:00  strategy_mutation   \n",
+       "1 2026-10-05 02:54:08.621283+00:00  strategy_mutation   \n",
+       "2 2026-10-05 02:54:09.462313+00:00  strategy_mutation   \n",
+       "3 2026-10-05 02:54:10.266227+00:00  strategy_mutation   \n",
+       "4 2026-10-05 02:54:13.757865+00:00  strategy_mutation   \n",
+       "5 2026-10-05 02:54:14.363148+00:00  strategy_mutation   \n",
+       "6 2026-10-05 02:54:15.129828+00:00  strategy_mutation   \n",
+       "7 2026-10-05 02:54:16.158524+00:00  strategy_mutation   \n",
+       "8 2026-10-05 02:54:17.079875+00:00  strategy_mutation   \n",
+       "\n",
+       "                        tool           strategy_path strategy_version  \\\n",
+       "0            open_hypothesis  manufacturing-strategy     5ca0f5e2e3e9   \n",
+       "1         record_observation  manufacturing-strategy     7fdc705e7171   \n",
+       "2         record_observation  manufacturing-strategy     89ab0cd60924   \n",
+       "3         record_observation  manufacturing-strategy     aba23959efdb   \n",
+       "4  record_hypothesis_outcome  manufacturing-strategy     8ce7d315ce31   \n",
+       "5  record_hypothesis_outcome  manufacturing-strategy     8d90e2d77d9b   \n",
+       "6  record_hypothesis_outcome  manufacturing-strategy     48997251b9f0   \n",
+       "7        graduate_hypothesis  manufacturing-strategy     609d8b85ec48   \n",
+       "8  update_approach_narrative  manufacturing-strategy     ee7d4b090d88   \n",
+       "\n",
+       "                                              result  \\\n",
+       "0  Opened hyp-001; 3 confirmations are required b...   \n",
+       "1                Manufacturing observation recorded.   \n",
+       "2                Manufacturing observation recorded.   \n",
+       "3                Manufacturing observation recorded.   \n",
+       "4            Recorded confirmed outcome for hyp-001.   \n",
+       "5            Recorded confirmed outcome for hyp-001.   \n",
+       "6            Recorded confirmed outcome for hyp-001.   \n",
+       "7   Graduated hyp-001 into a calibration correction.   \n",
+       "8          Manufacturing approach narrative updated.   \n",
+       "\n",
+       "                                 state_before_sha256  \\\n",
+       "0  3118aab6b065cc0c5339d828859e9c46c99fe51b6f7f27...   \n",
+       "1  5ca0f5e2e3e91523872ec8861390931044e6d178a6fd37...   \n",
+       "2  7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47...   \n",
+       "3  89ab0cd60924f10c4fba9a874bc544131a5dee5432c561...   \n",
+       "4  aba23959efdb5dc1dafc7e8c243f039ee458a725004a99...   \n",
+       "5  8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc...   \n",
+       "6  8d90e2d77d9b0516845227849de75069c993bfa6da9a5d...   \n",
+       "7  48997251b9f02b73386e9e2a426f2c2901161db838612b...   \n",
+       "8  609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737...   \n",
+       "\n",
+       "                                  state_after_sha256  \n",
+       "0  5ca0f5e2e3e91523872ec8861390931044e6d178a6fd37...  \n",
+       "1  7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47...  \n",
+       "2  89ab0cd60924f10c4fba9a874bc544131a5dee5432c561...  \n",
+       "3  aba23959efdb5dc1dafc7e8c243f039ee458a725004a99...  \n",
+       "4  8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc...  \n",
+       "5  8d90e2d77d9b0516845227849de75069c993bfa6da9a5d...  \n",
+       "6  48997251b9f02b73386e9e2a426f2c2901161db838612b...  \n",
+       "7  609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737...  \n",
+       "8  ee7d4b090d8898fca705b884e399d1a7df6d8cf6d3e381...  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['adaptation_timeline.png', 'mutation_activity.png']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from manufacturing_stress_forecasting.adaptive_agent.report import load_audit, write_report\n",
+    "\n",
+    "AUDIT_PATH = STRATEGY_DIR / \".history\" / \"adaptation_audit.jsonl\"\n",
+    "audit = load_audit(AUDIT_PATH) if AUDIT_PATH.exists() else pd.DataFrame()\n",
+    "if audit.empty:\n",
+    "    print(f\"No durable adaptation mutations recorded at {AUDIT_PATH}.\")\n",
+    "else:\n",
+    "    display(audit)\n",
+    "    paths = write_report(AUDIT_PATH, REPORT_DIR)\n",
+    "    print([path.name for path in paths])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "202dbe8f",
+   "metadata": {},
+   "source": [
+    "## Interpretation checklist\n",
+    "\n",
+    "- Compare inspection and execution runs using the strategy hash, trace metadata, and mutation audit.\n",
+    "- Treat adaptation as durable only when the redacted JSONL audit and post-state hash are present.\n",
+    "- Review mutation activity alongside forecast quality; mutation count alone is not evidence of improvement.\n",
+    "- Keep `RUN_ADAPTIVE_AGENT = False` for reproducible inspection-only runs."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.12.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_adaptive_model_comparison.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_adaptive_model_comparison.ipynb
new file mode 100644
index 000000000..356e6dd62
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_adaptive_model_comparison.ipynb
@@ -0,0 +1,661 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "ac3272b4",
+            "metadata": {
+                "id": "cell-1",
+                "language": "markdown"
+            },
+            "source": [
+                "# Manufacturing Stress: Adaptive Model Comparison\n",
+                "\n",
+                "Controlled lite-versus-advanced adaptive comparison. Mutation is disabled; both predictors use the same frozen strategy and historical origins. In the setup cell, set `BACKTEST_STRIDE_MONTHS` to `3` for the faster default (28 origins) or `1` for monthly origins (84 origins). The three-month forecast horizon is unchanged, so monthly targets overlap.\n",
+                "\n",
+                "The final paired table compares each model's predicted probability and stated risk direction (`up`, `down`, or `neutral`), plus its concise rationale (at most 40 words), supporting evidence, and countervailing evidence. These are the model's structured explanations; the notebook does not define a separate binary decision threshold."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "id": "993ffa40",
+            "metadata": {
+                "id": "cell-2",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "{'models': {'adaptive_lite': 'gemini-3.1-flash-lite-preview', 'adaptive_advanced': 'gemini-3.5-flash'}, 'strategy_dir': '/home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2/implementations/manufacturing_stress_forecasting/adaptive_agent/skills/manufacturing-strategy', 'stride_months': 1, 'origins': 84, 'run': True}\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import sys\n",
+                "from pathlib import Path\n",
+                "\n",
+                "import pandas as pd\n",
+                "import yaml\n",
+                "from dotenv import load_dotenv\n",
+                "\n",
+                "REPO_ROOT = next((p for p in (Path.cwd().resolve(), *Path.cwd().resolve().parents) if (p / 'uv.lock').exists() and (p / 'implementations').is_dir()), None)\n",
+                "if REPO_ROOT is None:\n",
+                "    raise FileNotFoundError('Repository root not found.')\n",
+                "for package_root in (REPO_ROOT / 'aieng-forecasting', REPO_ROOT / 'implementations'):\n",
+                "    if str(package_root) not in sys.path:\n",
+                "        sys.path.insert(0, str(package_root))\n",
+                "load_dotenv(REPO_ROOT / '.env', override=False)\n",
+                "\n",
+                "from aieng.forecasting.evaluation import BacktestSpec\n",
+                "from aieng.forecasting.models import ADVANCED_MODEL, LITE_MODEL\n",
+                "from manufacturing_stress_forecasting.adaptive_agent.agent import build_manufacturing_adaptive_agent_predictor\n",
+                "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+                "from manufacturing_stress_forecasting.run_agent_backtest import run_or_load_backtest\n",
+                "\n",
+                "MODEL_VARIANTS = {'adaptive_lite': LITE_MODEL, 'adaptive_advanced': ADVANCED_MODEL}\n",
+                "RUN_COMPARISON = True\n",
+                "MUTATION_ENABLED = False\n",
+                "FORCE_REFRESH = False\n",
+                "BACKTEST_STRIDE_MONTHS = 1  # Choose 1 (monthly) or 3 (every third month).\n",
+                "SPEC_PATH = REPO_ROOT / 'implementations' / 'manufacturing_stress_forecasting' / 'specs' / 'manufacturing_stress_smoke.yaml'\n",
+                "STRATEGY_DIR = REPO_ROOT / 'implementations' / 'manufacturing_stress_forecasting' / 'adaptive_agent' / 'skills' / 'manufacturing-strategy'\n",
+                "CACHE_DIR = REPO_ROOT / 'data' / 'predictions' / 'manufacturing_stress_adaptive_model_comparison'\n",
+                "if MUTATION_ENABLED:\n",
+                "    raise ValueError('Mutation must remain disabled for a controlled model comparison.')\n",
+                "spec_data = yaml.safe_load(SPEC_PATH.read_text(encoding='utf-8'))\n",
+                "if BACKTEST_STRIDE_MONTHS not in (1, 3):\n",
+                "    raise ValueError('BACKTEST_STRIDE_MONTHS must be 1 or 3.')\n",
+                "spec_data['stride'] = BACKTEST_STRIDE_MONTHS\n",
+                "spec = BacktestSpec.model_validate(spec_data)\n",
+                "service = build_manufacturing_stress_service(cache_dir=REPO_ROOT / 'data' / 'fred', yahoo_cache_dir=REPO_ROOT / 'data' / 'yfinance')\n",
+                "print({'models': MODEL_VARIANTS, 'strategy_dir': str(STRATEGY_DIR), 'stride_months': spec.stride, 'origins': len(spec.origins()), 'run': RUN_COMPARISON})"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "id": "ca18a350",
+            "metadata": {
+                "id": "cell-3",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "agent_predictor_manufacturing_stress_adaptive_analyst_gemini_3_1_flash_lite_preview_read_only_gemini-3.1-flash-lite-preview_discrete: loaded /home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2/data/predictions/manufacturing_stress_adaptive_model_comparison/manufacturing_stress_smoke_llmp_v3_14b1deebe4/agent_predictor_manufacturing_stress_adaptive_analyst_gemini_3_1_flash_lite_preview_read_only_gemini-3.1-flash-lite-preview_discrete.yaml\n",
+                        "adaptive_lite 0.09014672498745041\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/home/coder/agentic-forecasting/.venv/lib/python3.12/site-packages/google/adk/tools/function_tool.py:95: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n",
+                        "  build_function_declaration(\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "agent_predictor_manufacturing_stress_adaptive_analyst_gemini_3_5_flash_read_only_gemini-3.5-flash_discrete: saved /home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2/data/predictions/manufacturing_stress_adaptive_model_comparison/manufacturing_stress_smoke_llmp_v3_14b1deebe4/agent_predictor_manufacturing_stress_adaptive_analyst_gemini_3_5_flash_read_only_gemini-3.5-flash_discrete.yaml\n",
+                        "adaptive_advanced 0.07045702380952382\n"
+                    ]
+                }
+            ],
+            "source": [
+                "results = {}\n",
+                "if RUN_COMPARISON:\n",
+                "    for variant_id, model in MODEL_VARIANTS.items():\n",
+                "        predictor = build_manufacturing_adaptive_agent_predictor(strategy_dir=STRATEGY_DIR, model=model, mutation_enabled=MUTATION_ENABLED)\n",
+                "        results[variant_id] = run_or_load_backtest(predictor, spec, service, force_refresh=FORCE_REFRESH, store_dir=CACHE_DIR)\n",
+                "        print(variant_id, results[variant_id].mean_score)\n",
+                "else:\n",
+                "    print('RUN_COMPARISON = False; no LLM calls made.')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "id": "74523950",
+            "metadata": {
+                "id": "cell-4",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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2W4s+hw4d0k8//aTOnTvn6j1z4l//+peSkpLMjxctWqRLly6pU6dOkrL/+TMMI8u2sLl1/fp1i8cODg7mYub+lLvOnTtr165d2r59u7lfSkqKZs2aJX9/f9WpUydPGQDYjxyPWBw5ckRlypR5ZB9XV1eFhoYqNDQ0y4cVADwOy5YtU1JSkrp165bt888++6z5Znk9e/bUe++9p++++04vvPCCwsPDzdueVqlSRQcOHDC/rnnz5ipVqpT69u2rQYMGyWQy6bvvvnvo9I4KFSpo8uTJ+t///qeaNWtq/vz52rdvn2bNmqXixYtLuvftuZeXl6Kjo+Xu7q4SJUqoadOmCggIeOj59ezZU5999pnGjBmjwMBA8zz3+15//XUtWLBAb7/9ttavX68WLVooIyNDx44d04IFC7R69Wo1btw4t5c1xyZMmKA1a9aoZcuW+vOf/6xixYpp5syZSk1N1ZQpU8z9cnrdPTw8NGPGDL3++utq2LChevXqpbJlyyouLk7Lly9XixYtNH369Bznc3Bw0IwZM9S1a1cFBQUpLCxM5cuX17Fjx3T48GGtXr1akvT3v/9dnTp1UrNmzdSvXz/zdrOenp4aO3Zsvl2v+0qXLq2WLVsqLCxMly9f1tSpU1W9enX1799fklSrVi1Vq1ZNQ4cO1YULF+Th4aHFixfned3Cn/70JyUkJKhdu3aqVKmSzp07p88++0xBQUHmn60RI0Zo3rx56tSpkwYNGqTSpUvr22+/1dmzZ7V48eIcTbkCUETkdhuptLQ0IywszDhz5ky+bU0FAPmla9euhouLi5GSkvLQPm+88YZRvHhx49q1a4ZhGMaBAweM1q1bGy4uLkbFihWNDz74wPjnP/+ZZbvZrVu3Gs8++6zh6upqVKhQwXjvvffMW66uX7/e3K9169bG008/bezevdto1qyZ4eLiYlSpUsWYPn16liw//PCDUadOHaNYsWIWW7f+dlvQ+zIzMw0/Pz9DkjFhwoRszy8tLc2YPHmy8fTTTxvOzs5GqVKljEaNGhnjxo0zbt269cjrdz/7b1WpUsXo0qVLlnZJxl/+8heLtr179xrBwcFGyZIlDTc3N6Nt27bGtm3bsrw2p9fdMO5tZRocHGx4enoaLi4uRrVq1Yw33njD2L17t7lPTrabvW/Lli1Gx44dDXd3d6NEiRJGvXr1jM8++8yiz88//2y0aNHCcHV1NTw8PIyuXbsaR44csehz/z2vXr1q0d63b1+jRIkSWd73t9f3/hat8+bNM0aOHGn4+PgYrq6uRpcuXSy2kDUMwzhy5IjRoUMHo2TJkoa3t7fRv39/Y//+/Vm2/H3Ye99/7sGfq0WLFhnPP/+84ePjYzg5ORmVK1c23nrrLePSpUsWrzt9+rTxyiuvGF5eXoaLi4vRpEkT48cff7Toc/9cFi5caNF+9uzZh26rDMC+mAwj96upPD09tW/fvkd+qwYARVWbNm107do1HTp0yNZR8ITbsGGD2rZtq4ULF+qVV16xdRwAyBOrxi9DQkJydPdWAAAAAEWDVfv01ahRQ+PHj9fWrVvVqFGjLLtjDBo0KF/CAQAAACgcrCos/vnPf8rLy0t79uzJcgdZk8lEYQEAAAAUMVatsQAAAACAB+Vpj7i0tDQdP35c6enp+ZUHAAAAQCFkVWFx+/Zt9evXT25ubnr66acVFxcnSRo4cKAmTZqUrwEBAAAAPPmsWmMxcuRI7d+/Xxs2bNALL7xgbu/QoYPGjh2rESNG5FtAW8jMzNTFixfl7u4uk8lk6zgAAACATRiGoaSkJFWoUOF3b4hpVWGxdOlSzZ8/X88++6zFL95PP/20Tp8+bc0hnygXL16Un5+frWMAAAAAT4Tz58+rUqVKj+xjVWFx9epV+fj4ZGlPSUmxi2/43d3dJd27gB4eHjZOAwAAANhGYmKi/Pz8zL8fP4pVhUXjxo21fPlyDRw4UJLMxcRXX32lZs2aWXPIJ8r98/Hw8KCwAAAAQJGXk8EDqwqLiRMnqlOnTjpy5IjS09M1bdo0HTlyRNu2bdPGjRutOSQAAACAQsyqXaFatmypffv2KT09XYGBgfrpp5/k4+Oj7du3q1GjRvmdEQAAAMATjhvkZSMxMVGenp66desWU6EAAABQZOXm92KrRiwcHR115cqVLO3Xr1+Xo6OjNYcEAAAAUIhZVVg8bJAjNTVVTk5OeQoEAAAAoPDJ1eLtTz/9VNK9VeFfffWVSpYsaX4uIyNDmzZtUq1atfI3IQAAAIAnXq4Ki3/84x+S7o1YREdHW0x7cnJykr+/v6Kjo/M3IQAAAIAnXq4Ki7Nnz0qS2rZtqyVLlqhUqVIFEgoAADweJz6oa+sIQJ7UjDxk6wj4/6xaY9G2bVs5Oztnaf/11181fvz4PIcCAAAAULhYVViMGzdOycnJWdpv376tcePG5TkUAAAAgMLF6l2hsrut9/79+1W6dOk8hwIAAABQuORqjUWpUqVkMplkMplUs2ZNi+IiIyNDycnJevvtt/M9JAAAAIAnW64Ki6lTp8owDL355psaN26cPD09zc/d3xWqWbNm+R4SAAAAwJMtV4VF3759JUkBAQFq0aKFihXL1csBAAAA2Cmr1li0bt1a586d06hRoxQaGqorV65IklauXKnDhw/na0AAAAAATz6rCouNGzcqMDBQO3fu1JIlS8w7RO3fv19jxozJ14AAAAAAnnxWFRYjRozQhAkTtGbNGjk5OZnb27Vrpx07duRbOAAAAACFg1WFxcGDB9WjR48s7T4+Prp27VqeQwEAAAAoXKwqLLy8vHTp0qUs7bGxsapYsWKeQwEAAAAoXKwqLHr16qXhw4crPj5eJpNJmZmZ2rp1q4YOHao+ffrkd0YAAAAATzirCouJEyeqVq1a8vPzU3JysurUqaNWrVqpefPmGjVqVH5nBAAAAPCEs+pGFE5OTvryyy8VGRmpQ4cOKTk5WQ0aNFCNGjXyOx8AAACAQiBPd7irXLmy/Pz8JEkmkylfAgEAAAAofKyaCiVJ//znP1W3bl25uLjIxcVFdevW1VdffZWf2QAAAAAUElYVFqNHj1Z4eLi6du2qhQsXauHCheratauGDBmi0aNH5+pYn3/+ufz9/eXi4qKmTZtq165dj+y/cOFC1apVSy4uLgoMDNSKFSssnk9OTta7776rSpUqydXVVXXq1FF0dHSuzxEAAABAzllVWMyYMUNffvmloqKi1K1bN3Xr1k1RUVGaNWuWvvjiixwfZ/78+YqIiNCYMWO0d+9e1a9fX8HBwbpy5Uq2/bdt26bQ0FD169dPsbGxCgkJUUhIiA4dOmTuExERoVWrVunf//63jh49qsGDB+vdd9/VsmXLrDlVAAAAADlgVWFx9+5dNW7cOEt7o0aNlJ6enuPjfPLJJ+rfv7/CwsLMIwtubm76+uuvs+0/bdo0vfDCCxo2bJhq166tDz74QA0bNtT06dPNfbZt26a+ffuqTZs28vf314ABA1S/fv3fHQkBAAAAYD2rCovXX39dM2bMyNI+a9Ys9e7dO0fHSEtL0549e9ShQ4f/C+PgoA4dOmj79u3Zvmb79u0W/SUpODjYon/z5s21bNkyXbhwQYZhaP369Tpx4oSef/75HOUCAAAAkHs53hUqIiLC/HeTyaSvvvpKP/30k5599llJ0s6dOxUXF5fjG+Rdu3ZNGRkZKleunEV7uXLldOzYsWxfEx8fn23/+Ph48+PPPvtMAwYMUKVKlVSsWDE5ODjoyy+/VKtWrR6aJTU1VampqebHiYmJkqTMzExlZmbm6HwAACiMDOv3cQGeCPyuVrByc31zXFjExsZaPG7UqJEk6fTp05Ikb29veXt76/Dhwzl+84Lw2WefaceOHVq2bJmqVKmiTZs26S9/+YsqVKiQZbTjvqioKI0bNy5L+40bN3I1tQsAgMLmdokqto4A5ElCQoKtI9i1pKSkHPfNcWGxfv16q8I8jLe3txwdHXX58mWL9suXL8vX1zfb1/j6+j6y/6+//qr3339f33//vbp06SJJqlevnvbt26ePPvrooYXFyJEjLUZkEhMT5efnp1KlSsnDw8PqcwQA4El3PeWcrSMAeVK6dGlbR7BrxYrl/LZ3ebpBXl44OTmpUaNGWrt2rUJCQiTdG2pZu3at3n333Wxf06xZM61du1aDBw82t61Zs0bNmjWTdG9R+d27d+XgYDms6+jo+MhhHGdnZzk7O2dpd3BwyHIsAADsiUlMI0Hhxu9qBSs319dmhYV0b91G37591bhxYzVp0kRTp05VSkqKwsLCJEl9+vRRxYoVFRUVJUkKDw9X69at9fHHH6tLly6KiYnR7t27NWvWLEmSh4eHWrdurWHDhsnV1VVVqlTRxo0b9a9//UuffPKJzc4TAAAAsHc2LSx69uypq1evavTo0YqPj1dQUJBWrVplXqAdFxdnUSU1b95cc+fO1ahRo/T++++rRo0aWrp0qerWrWvuExMTo5EjR6p3795KSEhQlSpV9OGHH+rtt99+7OcHAAAAFBUmwzAMW4d40iQmJsrT01O3bt1ijQUAwK6d+KDu73cCnmA1Iw/9fidYLTe/FzMpDQAAAECeWVVYfPvtt1q+fLn58XvvvScvLy81b95c586xuwQAAABQ1FhVWEycOFGurq6S7t0N+/PPP9eUKVPk7e2tIUOG5GtAAAAAAE8+qxZvnz9/XtWrV5ckLV26VC+//LIGDBigFi1aqE2bNvmZDwAAAEAhYNWIRcmSJXX9+nVJ0k8//aSOHTtKklxcXPTrr7/mXzoAAAAAhYJVIxYdO3bUn/70JzVo0EAnTpxQ586dJUmHDx+Wv79/fuYDAAAAUAhYNWLx+eefq1mzZrp69aoWL16sMmXKSJL27Nmj0NDQfA0IAAAA4Mln1YiFl5eXpk+fnqV93LhxeQ4EAAAAoPDJcWFx4MAB1a1bVw4ODjpw4MAj+9arVy/PwQAAAAAUHjkuLIKCghQfHy8fHx8FBQXJZDLpwZt2339sMpmUkZFRIGEBAAAAPJlyXFicPXtWZcuWNf8dAAAAAO7LcWFRpUqVbP8OAAAAAFbtCgUAAAAAD6KwAAAAAJBnVm03C+SHoMFTbR0ByJN9UwfbOgIAAE+MXI9YZGRkaNOmTbp582YBxAEAAABQGOW6sHB0dNTzzz+vGzduFEQeAAAAAIWQVWss6tatqzNnzuR3FgAAAACFlFWFxYQJEzR06FD9+OOPunTpkhITEy3+AAAAACharFq83blzZ0lSt27dZDKZzO3ceRsAAAAomqwqLNavX5/fOQAAAAAUYlYVFq1bt87vHAAAAAAKMatvkLd582a99tprat68uS5cuCBJ+u6777Rly5Z8CwcAAACgcLCqsFi8eLGCg4Pl6uqqvXv3KjU1VZJ069YtTZw4MV8DAgAAAHjyWb0rVHR0tL788ksVL17c3N6iRQvt3bs338IBAAAAKBysKiyOHz+uVq1aZWn39PTkjtwAAABAEWRVYeHr66tTp05lad+yZYuqVq2a51AAAAAACherCov+/fsrPDxcO3fulMlk0sWLFzVnzhwNHTpU77zzTn5nBAAAAPCEs2q72REjRigzM1Pt27fX7du31apVKzk7O2vo0KEaOHBgfmcEAAAA8ISzqrAwmUz629/+pmHDhunUqVNKTk5WnTp1VLJkyfzOBwAAAKAQsKqwWLdunZo3by4XFxfVqVMnvzMBAAAAKGSsKiy6deum9PR0PfPMM2rTpo1at26tFi1ayNXVNb/zAQAAACgErFq8fePGDa1du1adOnXSrl271KNHD3l5ealFixYaNWpUro71+eefy9/fXy4uLmratKl27dr1yP4LFy5UrVq15OLiosDAQK1YsSJLn6NHj6pbt27y9PRUiRIl9MwzzyguLi5XuQAAAADknFWFRfHixdWiRQu9//77Wr16tXbs2KHQ0FDt2rVLUVFROT7O/PnzFRERoTFjxmjv3r2qX7++goODdeXKlWz7b9u2TaGhoerXr59iY2MVEhKikJAQHTp0yNzn9OnTatmypWrVqqUNGzbowIEDioyMlIuLizWnCgAAACAHTIZhGLl90YkTJ7RhwwZt2LBBGzduVGpqqp577jm1adNGbdq0Uf369XN0nKZNm+qZZ57R9OnTJUmZmZny8/PTwIEDNWLEiCz9e/bsqZSUFP3444/mtmeffVZBQUGKjo6WJPXq1UvFixfXd999l9vTMktMTJSnp6du3bolDw8Pq4+DRwsaPNXWEYA82Td1sK0jAHl24oO6to4A5EnNyEO/3wlWy83vxVatsahVq5bKli2r8PBwjRgxQoGBgTKZTLk6Rlpamvbs2aORI0ea2xwcHNShQwdt374929ds375dERERFm3BwcFaunSppHuFyfLly/Xee+8pODhYsbGxCggI0MiRIxUSEvLQLKmpqUpNTTU/TkxMNB8vMzMzV+eFnHPI3Y8M8MTh8wH2wLBu8gLwxOCzuGDl5vpaVVgMGjRImzZt0vjx4/Xjjz+aRypatmwpNze3HB3j2rVrysjIULly5Szay5Urp2PHjmX7mvj4+Gz7x8fHS5KuXLmi5ORkTZo0SRMmTNDkyZO1atUqvfTSS1q/fr1at26d7XGjoqI0bty4LO03btxQenp6js4HuVfZk+lpKNwSEhJsHQHIs9slqtg6ApAnfBYXrKSkpBz3taqwmDp1qiTp5s2b2rx5szZu3Ki//e1vOnz4sBo0aKCtW7dac9g8u19Rde/eXUOGDJEkBQUFadu2bYqOjn5oYTFy5EiLkZDExET5+fmpVKlSTIUqQHG37tg6ApAnpUuXtnUEIM+up5yzdQQgT/gsLljFiuW8XLCqsLgvIyNDd+/eVWpqqu7cuaPU1FQdP348R6/19vaWo6OjLl++bNF++fJl+fr6ZvsaX1/fR/b39vZWsWLFstxbo3bt2tqyZctDszg7O8vZ2TlLu4ODgxwcGCIuKJm5Xt0DPFn4fIA9MIlpJCjc+CwuWLm5vlb9SwwaNEj16tVTuXLl9NZbb+nixYvq37+/YmNjdfXq1Rwdw8nJSY0aNdLatWvNbZmZmVq7dq2aNWuW7WuaNWtm0V+S1qxZY+7v5OSkZ555Jktxc+LECVWpwlAvAAAAUFCsGrG4dOmSBgwYoDZt2qhuXet3k4iIiFDfvn3VuHFjNWnSRFOnTlVKSorCwsIkSX369FHFihXNW9iGh4erdevW+vjjj9WlSxfFxMRo9+7dmjVrlvmYw4YNU8+ePdWqVSu1bdtWq1at0n/+8x9t2LDB6pwAAAAAHs2qwmLhwoX58uY9e/bU1atXNXr0aMXHxysoKEirVq0yL9COi4uzGH5p3ry55s6dq1GjRun9999XjRo1tHTpUovipkePHoqOjlZUVJQGDRqkp556SosXL1bLli3zJTMAAACArKy6j4V070Z0U6dO1dGjRyVJderUUXh4uKpVq5avAW2B+1g8HtzHAoUd97GAPeA+FijsuI9FwcrN78VWrbFYvXq16tSpo127dqlevXqqV6+edu7cqaefflpr1qyxKjQAAACAwsuqqVAjRozQkCFDNGnSpCztw4cPV8eOHfMlHAAAAIDCwaoRi6NHj6pfv35Z2t98800dOXIkz6EAAAAAFC5WFRZly5bVvn37srTv27dPPj4+ec0EAAAAoJCxaipU//79NWDAAJ05c0bNmzeXJG3dulWTJ0+2uIM1AAAAgKLBqsIiMjJS7u7u+vjjjzVy5EhJUoUKFTR27FgNGjQoXwMCAAAAePJZVViYTCYNGTJEQ4YMUVJSkiTJ3d09X4MBAAAAKDysKiweREEBAAAAIMeFRYMGDWQymXLUd+/evVYHAgAAAFD45LiwCAkJKcAYAAAAAAqzHBcWY8aMKcgcAAAAAAqxHN/HwjCMgswBAAAAoBDLcWHx9NNPKyYmRmlpaY/sd/LkSb3zzjuaNGlSnsMBAAAAKBxyPBXqs88+0/Dhw/XnP/9ZHTt2VOPGjVWhQgW5uLjoxo0bOnLkiLZs2aLDhw/r3Xff1TvvvFOQuQEAAAA8QXJcWLRv3167d+/Wli1bNH/+fM2ZM0fnzp3Tr7/+Km9vbzVo0EB9+vRR7969VapUqYLMDAAAAOAJk+v7WLRs2VItW7YsiCwAAAAACqkcr7EAAAAAgIehsAAAAACQZxQWAAAAAPKMwgIAAABAnlFYAAAAAMgzqwuL06dPa9SoUQoNDdWVK1ckSStXrtThw4fzLRwAAACAwsGqwmLjxo0KDAzUzp07tWTJEiUnJ0uS9u/frzFjxuRrQAAAAABPPqsKixEjRmjChAlas2aNnJyczO3t2rXTjh078i0cAAAAgMLBqsLi4MGD6tGjR5Z2Hx8fXbt2Lc+hAAAAABQuVhUWXl5eunTpUpb22NhYVaxYMc+hAAAAABQuVhUWvXr10vDhwxUfHy+TyaTMzExt3bpVQ4cOVZ8+ffI7IwAAAIAnnFWFxcSJE1WrVi35+fkpOTlZderUUatWrdS8eXONGjUqvzMCAAAAeMIVy+0LDMNQfHy8Pv30U40ePVoHDx5UcnKyGjRooBo1ahRERgAAAABPOKsKi+rVq+vw4cOqUaOG/Pz8CiIXAAAAgEIk11OhHBwcVKNGDV2/fr0g8gAAAAAohKxaYzFp0iQNGzZMhw4dypcQn3/+ufz9/eXi4qKmTZtq165dj+y/cOFC1apVSy4uLgoMDNSKFSse2vftt9+WyWTS1KlT8yUrAAAAgKysKiz69OmjXbt2qX79+nJ1dVXp0qUt/uTG/PnzFRERoTFjxmjv3r2qX7++goODdeXKlWz7b9u2TaGhoerXr59iY2MVEhKikJCQbIuc77//Xjt27FCFChWsOU0AAAAAOZTrNRaS8vXb/08++UT9+/dXWFiYJCk6OlrLly/X119/rREjRmTpP23aNL3wwgsaNmyYJOmDDz7QmjVrNH36dEVHR5v7XbhwQQMHDtTq1avVpUuXfMsLAAAAICurCou+ffvmy5unpaVpz549GjlypLnNwcFBHTp00Pbt27N9zfbt2xUREWHRFhwcrKVLl5ofZ2Zm6vXXX9ewYcP09NNP50tWAAAAAA9nVWEhSRkZGVq6dKmOHj0qSXr66afVrVs3OTo65vgY165dU0ZGhsqVK2fRXq5cOR07dizb18THx2fbPz4+3vx48uTJKlasmAYNGpSjHKmpqUpNTTU/TkxMlHSvQMnMzMzRMZB7DiZbJwDyhs8H2APDulnRwBODz+KClZvra1VhcerUKXXu3FkXLlzQU089JUmKioqSn5+fli9frmrVqllz2HyxZ88eTZs2TXv37pXJlLPfXKOiojRu3Lgs7Tdu3FB6enp+R8T/V9nTxdYRgDxJSEiwdQQgz26XqGLrCECe8FlcsJKSknLc16rCYtCgQapWrZp27NhhXqx9/fp1vfbaaxo0aJCWL1+eo+N4e3vL0dFRly9ftmi/fPmyfH19s32Nr6/vI/tv3rxZV65cUeXKlc3PZ2Rk6K9//aumTp2q//3vf1mOOXLkSIvpVYmJifLz81OpUqXk4eGRo3NB7sXdumPrCECe5HazCuBJdD3lnK0jAHnCZ3HBKlYs5+WCVYXFxo0bLYoKSSpTpowmTZqkFi1a5Pg4Tk5OatSokdauXauQkBBJ94Zb1q5dq3fffTfb1zRr1kxr167V4MGDzW1r1qxRs2bNJEmvv/66OnToYPGa4OBgvf766+YF4r/l7OwsZ2fnLO0ODg5ycGCIuKBkGrZOAOQNnw+wByYxjQSFG5/FBSs319eqwsLZ2TnbYZHk5GQ5OTnl6lgRERHq27evGjdurCZNmmjq1KlKSUkxFwF9+vRRxYoVFRUVJUkKDw9X69at9fHHH6tLly6KiYnR7t27NWvWLEn3CpwyZcpYvEfx4sXl6+trnrYFAAAAIH9ZVeK9+OKLGjBggHbu3CnDMGQYhnbs2KG3335b3bp1y9WxevbsqY8++kijR49WUFCQ9u3bp1WrVpkXaMfFxenSpUvm/s2bN9fcuXM1a9Ys1a9fX4sWLdLSpUtVt25da04FAAAAQD4wGYaR6wkpN2/eVN++ffWf//xHxYsXlySlp6erW7dumj17tjw9PfM96OOUmJgoT09P3bp1izUWBSho8FRbRwDyZN/UwbaOAOTZiQ/4Yg6FW83IrDdJRv7Jze/FVk2F8vLy0g8//KBTp06Zt5utXbu2qlevbs3hAAAAABRyVt/HQpKqV69OMQEAAADAujUWL7/8siZPnpylfcqUKfrDH/6Q51AAAAAACherCotNmzapc+fOWdo7deqkTZs25TkUAAAAgMLFqsLiYdvKFi9eXImJiXkOBQAAAKBwsaqwCAwM1Pz587O0x8TEqE6dOnkOBQAAAKBwsWrxdmRkpF566SWdPn1a7dq1kyStXbtW8+bN08KFC/M1IAAAAIAnn1WFRdeuXbV06VJNnDhRixYtkqurq+rVq6eff/5ZrVu3zu+MAAAAAJ5wVm8326VLF3Xp0iU/swAAAAAopKxaY3H+/Hn98ssv5se7du3S4MGDNWvWrHwLBgAAAKDwsKqwePXVV7V+/XpJUnx8vDp06KBdu3bpb3/7m8aPH5+vAQEAAAA8+awqLA4dOqQmTZpIkhYsWKDAwEBt27ZNc+bM0ezZs/MzHwAAAIBCwKrC4u7du3J2dpYk/fzzz+rWrZskqVatWrp06VL+pQMAAABQKFhVWDz99NOKjo7W5s2btWbNGr3wwguSpIsXL6pMmTL5GhAAAADAk8+qwmLy5MmaOXOm2rRpo9DQUNWvX1+StGzZMvMUKQAAAABFh1XbzbZp00bXrl1TYmKiSpUqZW4fMGCA3Nzc8i0cAAAAgMLB6vtYODo6WhQVkuTv75/XPAAAAAAKIaumQgEAAADAgygsAAAAAOQZhQUAAACAPMt1YXH37l21b99eJ0+eLIg8AAAAAAqhXBcWxYsX14EDBwoiCwAAAIBCyqqpUK+99pr++c9/5ncWAAAAAIWUVdvNpqen6+uvv9bPP/+sRo0aqUSJEhbPf/LJJ/kSDgAAAEDhYFVhcejQITVs2FCSdOLECYvnTCZT3lMBAAAAKFSsKizWr1+f3zkAAAAAFGJ52m721KlTWr16tX799VdJkmEY+RIKAAAAQOFiVWFx/fp1tW/fXjVr1lTnzp116dIlSVK/fv3017/+NV8DAgAAAHjyWVVYDBkyRMWLF1dcXJzc3NzM7T179tSqVavyLRwAAACAwsGqNRY//fSTVq9erUqVKlm016hRQ+fOncuXYAAAAAAKD6tGLFJSUixGKu5LSEiQs7NznkMBAAAAKFysKiyee+45/etf/zI/NplMyszM1JQpU9S2bdt8CwcAAACgcLCqsJgyZYpmzZqlTp06KS0tTe+9957q1q2rTZs2afLkybk+3ueffy5/f3+5uLioadOm2rVr1yP7L1y4ULVq1ZKLi4sCAwO1YsUK83N3797V8OHDFRgYqBIlSqhChQrq06ePLl68mOtcAAAAAHLGqsKibt26OnHihFq2bKnu3bsrJSVFL730kmJjY1WtWrVcHWv+/PmKiIjQmDFjtHfvXtWvX1/BwcG6cuVKtv23bdum0NBQ9evXT7GxsQoJCVFISIgOHTokSbp9+7b27t2ryMhI7d27V0uWLNHx48fVrVs3a04VAAAAQA6YDBvffKJp06Z65plnNH36dElSZmam/Pz8NHDgQI0YMSJL/549eyolJUU//vijue3ZZ59VUFCQoqOjs32P//73v2rSpInOnTunypUr/26mxMREeXp66tatW/Lw8LDyzPB7ggZPtXUEIE/2TR1s6whAnp34oK6tIwB5UjPykK0j2LXc/F6c412hDhw4oLp168rBwUEHDhx4ZN969erl6JhpaWnas2ePRo4caW5zcHBQhw4dtH379mxfs337dkVERFi0BQcHa+nSpQ99n1u3bslkMsnLyyvb51NTU5Wammp+nJiYKOlekZOZmZmjc0HuOZhsnQDIGz4fYA+MvN0rF7A5PosLVm6ub44Li6CgIMXHx8vHx0dBQUEymUzZ3mnbZDIpIyMjR8e8du2aMjIyVK5cOYv2cuXK6dixY9m+Jj4+Ptv+8fHx2fa/c+eOhg8frtDQ0IdWWVFRURo3blyW9hs3big9PT0npwIrVPZ0sXUEIE8SEhJsHQHIs9slqtg6ApAnfBYXrKSkpBz3zXFhcfbsWZUtW9b898Lg7t27+uMf/yjDMDRjxoyH9hs5cqTFKEhiYqL8/PxUqlQppkIVoLhbd2wdAciT0qVL2zoCkGfXU7j/FAo3PosLVrFiOb/tXY57Vqly7xuNu3fvaty4cYqMjFRAQEDu0z3A29tbjo6Ounz5skX75cuX5evrm+1rfH19c9T/flFx7tw5rVu37pEFgrOzc7b333BwcJCDA0PEBSXTpqt7gLzj8wH2wCSmkaBw47O4YOXm+ub6X6J48eJavHhxbl+WLScnJzVq1Ehr1641t2VmZmrt2rVq1qxZtq9p1qyZRX9JWrNmjUX/+0XFyZMn9fPPP6tMmTL5khcAAABA9qwq8UJCQh65WDo3IiIi9OWXX+rbb7/V0aNH9c477yglJUVhYWGSpD59+lgs7g4PD9eqVav08ccf69ixYxo7dqx2796td999V9K9ouKVV17R7t27NWfOHGVkZCg+Pl7x8fFKS0vLl8wAAAAALOV80tQDatSoofHjx2vr1q1q1KiRSpQoYfH8oEGDcnysnj176urVqxo9erTi4+MVFBSkVatWmRdox8XFWQzBNG/eXHPnztWoUaP0/vvvq0aNGlq6dKnq1r23Xd6FCxe0bNkySfcWnD9o/fr1atOmjRVnDAAAAOBRrLqPxaPWVphMJp05cyZPoWyN+1g8HtzHAoUd97GAPeA+FijsuI9FwSqQ+1g8qLDsCgUAAADg8WAZPQAAAIA8y3VhcfLkSS1evNg8arF8+XK1atVKzzzzjD788MNsb5oHAAAAwL7lairU999/rz/+8Y9ycHCQyWTSrFmz9NZbb6lNmzby8PDQ2LFjVaxYMQ0fPryg8gIAAAB4AuVqxOLDDz/Ue++9pzt37mjGjBl6++23FRUVpZUrV+rHH3/U559/rtmzZxdQVAAAAABPqlwVFsePH9ebb74pk8mkvn37Ki0tTR06dDA///zzz+vcuXP5HhIAAADAky1XhUVKSorc3d3vvdDBQa6urnJzczM/7+rqqtTU1PxNCAAAAOCJl6vCwmQyyWQyPfQxAAAAgKIpV4u3DcNQzZo1zcVEcnKyGjRoYL4zNjtCAQAAAEVTrgqLb775pqByAAAAACjEclVY9O3bt6ByAAAAACjEuPM2AAAAgDyjsAAAAACQZxQWAAAAAPKMwgIAAABAnlFYAAAAAMizXO0KdV9GRoZmz56ttWvX6sqVK8rMzLR4ft26dfkSDgAAAEDhYFVhER4ertmzZ6tLly6qW7cud98GAAAAijirCouYmBgtWLBAnTt3zu88AAAAAAohq9ZYODk5qXr16vmdBQAAAEAhZVVh8de//lXTpk2TYRj5nQcAAABAIWTVVKgtW7Zo/fr1WrlypZ5++mkVL17c4vklS5bkSzgAAAAAhYNVhYWXl5d69OiR31kAAAAAFFJWFRbffPNNfucAAAAAUIhxgzwAAAAAeWbViIUkLVq0SAsWLFBcXJzS0tIsntu7d2+egwEAAAAoPKwasfj0008VFhamcuXKKTY2Vk2aNFGZMmV05swZderUKb8zAgAAAHjCWVVYfPHFF5o1a5Y+++wzOTk56b333tOaNWs0aNAg3bp1K78zAgAAAHjCWVVYxMXFqXnz5pIkV1dXJSUlSZJef/11zZs3L//SAQAAACgUrCosfH19lZCQIEmqXLmyduzYIUk6e/YsN80DAAAAiiCrCot27dpp2bJlkqSwsDANGTJEHTt2VM+ePbm/BQAAAFAEWbUr1KxZs5SZmSlJ+stf/qIyZcpo27Zt6tatm9566618DQgAAADgyWfViIWDg4OKFfu/mqRXr1769NNPNXDgQDk5OeX6eJ9//rn8/f3l4uKipk2bateuXY/sv3DhQtWqVUsuLi4KDAzUihUrLJ43DEOjR49W+fLl5erqqg4dOujkyZO5zgUAAAAgZ6y+Qd7mzZv12muvqVmzZrpw4YIk6bvvvtOWLVtydZz58+crIiJCY8aM0d69e1W/fn0FBwfrypUr2fbftm2bQkND1a9fP8XGxiokJEQhISE6dOiQuc+UKVP06aefKjo6Wjt37lSJEiUUHBysO3fuWHu6AAAAAB7BqsJi8eLFCg4Olqurq2JjY5WamipJunXrliZOnJirY33yySfq37+/wsLCVKdOHUVHR8vNzU1ff/11tv2nTZumF154QcOGDVPt2rX1wQcfqGHDhpo+fbqke6MVU6dO1ahRo9S9e3fVq1dP//rXv3Tx4kUtXbrUmtMFAAAA8DusWmMxYcIERUdHq0+fPoqJiTG3t2jRQhMmTMjxcdLS0rRnzx6NHDnS3Obg4KAOHTpo+/bt2b5m+/btioiIsGgLDg42Fw1nz55VfHy8OnToYH7e09NTTZs21fbt29WrV68sx0xNTTUXR5LM9+K4efOmeS0J8p+RxggSCrebN2/aOgKQZ0l32M0RhRufxQUrMTFRknK086tVhcXx48fVqlWrLO2enp65+se9du2aMjIyVK5cOYv2cuXK6dixY9m+Jj4+Ptv+8fHx5ufvtz2sz29FRUVp3LhxWdqrVKmSsxMBUCSVmjHy9zsBAArWxFK2TlAkJCUlydPT85F9rCosfH19derUKfn7+1u0b9myRVWrVrXmkDY1cuRIi1GQzMxMJSQkqEyZMjKZTDZMBlgvMTFRfn5+On/+vDw8PGwdBwCKJD6LUdgZhqGkpCRVqFDhd/taVVj0799f4eHh+vrrr2UymXTx4kVt375dQ4cOVWRkZI6P4+3tLUdHR12+fNmi/fLly/L19c32Nb6+vo/sf/9/L1++rPLly1v0CQoKyvaYzs7OcnZ2tmjz8vLK8XkATzIPDw/+YwYANsZnMQqz3xupuM+qxdsjRozQq6++qvbt2ys5OVmtWrXSn/70J7311lsaOHBgjo/j5OSkRo0aae3atea2zMxMrV27Vs2aNcv2Nc2aNbPoL0lr1qwx9w8ICJCvr69Fn8TERO3cufOhxwQAAACQN1aNWJhMJv3tb3/TsGHDdOrUKSUnJ6tOnToqWbJkro8VERGhvn37qnHjxmrSpImmTp2qlJQUhYWFSZL69OmjihUrKioqSpIUHh6u1q1b6+OPP1aXLl0UExOj3bt3a9asWeZsgwcP1oQJE1SjRg0FBAQoMjJSFSpUUEhIiDWnCwAAAOB3WFVY3Ofk5KQ6derkKUDPnj119epVjR49WvHx8QoKCtKqVavMi6/j4uLk4PB/AyvNmzfX3LlzNWrUKL3//vuqUaOGli5dqrp165r7vPfee0pJSdGAAQN08+ZNtWzZUqtWrZKLi0uesgKFibOzs8aMGZNlmh8A4PHhsxhFicnIyd5R/9+bb76Zo34PuwcFAAAAAPuUq8LCwcFBVapUUYMGDR65l+3333+fL+EAAAAAFA65mgr1zjvvaN68eTp79qzCwsL02muvqXTp0gWVDQAAAEAhkasRC+neXaqXLFmir7/+Wtu2bVOXLl3Ur18/Pf/889zzAQAAACiicl1YPOjcuXOaPXu2/vWvfyk9PV2HDx+2amcoAAAAAIWbVfexML/YwUEmk0mGYSgjIyO/MgEAAAAoZHJdWKSmpmrevHnq2LGjatasqYMHD2r69OmKi4tjtAIAAEBSWlqajh8/rvT0dFtHAR6bXC3e/vOf/6yYmBj5+fnpzTff1Lx58+Tt7V1Q2QD8jlKlSuV4bVNCQkIBpwEA3L59WwMHDtS3334rSTpx4oSqVq2qgQMHqmLFihoxYoSNEwIFJ9fbzVauXFkNGjR45C8zS5YsyZdwAB7t/n+4JOn69euaMGGCgoOD1axZM0nS9u3btXr1akVGRmrIkCG2igkARUZ4eLi2bt2qqVOn6oUXXtCBAwdUtWpV/fDDDxo7dqxiY2NtHREoMLkqLN54440cfTv6zTff5CkUgNx7+eWX1bZtW7377rsW7dOnT9fPP/+spUuX2iYYABQhVapU0fz58/Xss8/K3d1d+/fvV9WqVXXq1Ck1bNhQiYmJto4IFJg87QoF4MlRsmRJ7du3T9WrV7doP3XqlIKCgpScnGyjZABQdLi5uenQoUOqWrWqRWGxf/9+tWrVSrdu3bJ1RKDA5GlXKABPjjJlyuiHH37I0v7DDz+oTJkyNkgEAEVP48aNtXz5cvPj+zM9vvrqK/M0VcBe5WrxNoAn17hx4/SnP/1JGzZsUNOmTSVJO3fu1KpVq/Tll1/aOB0AFA0TJ05Up06ddOTIEaWnp2vatGk6cuSItm3bpo0bN9o6HlCgmAoF2JGdO3fq008/1dGjRyVJtWvX1qBBg8yFBgCg4J0+fVqTJk3S/v37lZycrIYNG2r48OEKDAy0dTSgQFFYAAAAAMgz1lgAduT06dMaNWqUXn31VV25ckWStHLlSh0+fNjGyQCgaHB0dDR//j7o+vXrcnR0tEEi4PGhsADsxMaNGxUYGKidO3dq8eLF5l2g9u/frzFjxtg4HQAUDQ+bCJKamionJ6fHnAZ4vFi8DdiJESNGaMKECYqIiJC7u7u5vV27dpo+fboNkwGA/fv0008l3dsF6quvvlLJkiXNz2VkZGjTpk2qVauWreIBjwWFBWAnDh48qLlz52Zp9/Hx0bVr12yQCACKjn/84x+S7o1YREdHW0x7cnJykr+/v6Kjo20VD3gsKCwAO+Hl5aVLly4pICDAoj02NlYVK1a0USoAKBrOnj0rSWrbtq2WLFmiUqVK2TgR8PixxgKwE7169dLw4cMVHx8vk8mkzMxMbd26VUOHDlWfPn1sHQ8AioT169dTVKDIYrtZwE6kpaXpL3/5i2bPnq2MjAwVK1ZMGRkZevXVVzV79mx2IwGAAhIREaEPPvhAJUqUUERExCP7fvLJJ48pFfD4UVgAdub8+fM6ePCgkpOT1aBBA9WoUcPWkQDArrVt21bff/+9vLy81LZt24f2M5lMWrdu3WNMBjxeFBYAAAAA8ow1FoCdePnllzV58uQs7VOmTNEf/vAHGyQCAABFCSMWgJ0oW7as1q1bp8DAQIv2gwcPqkOHDrp8+bKNkgGAfXvppZdy3HfJkiUFmASwLbabBexEcnJytnd1LV68uBITE22QCACKBk9PT1tHAJ4IFBaAnQgMDNT8+fM1evRoi/aYmBjVqVPHRqkAwP598803uX7N1q1b1bhxYzk7OxdAIsA2KCwAOxEZGamXXnpJp0+fVrt27SRJa9eu1bx587Rw4UIbpwMAPKhTp07at2+fqlatausoQL6hsADsRNeuXbV06VJNnDhRixYtkqurq+rVq6eff/5ZrVu3tnU8AMADWOIKe8TibQAAgMfM3d1d+/fvZ8QCdoURC8DOpKWl6cqVK8rMzLRor1y5so0SAQCAooDCArATJ0+e1Jtvvqlt27ZZtBuGIZPJpIyMDBslAwAARQGFBWAn3njjDRUrVkw//vijypcvL5PJZOtIAICH4DMa9ojCArAT+/bt0549e1SrVi1bRwEA/A6WuMIeUVgAdqJOnTq6du2arWMAQJGXnp6uDRs26PTp03r11Vfl7u6uixcvysPDQyVLlpQkJSUl2TglkP/YFQqwE+vWrdOoUaM0ceJEBQYGqnjx4hbPe3h42CgZABQd586d0wsvvKC4uDilpqbqxIkTqlq1qsLDw5Wamqro6GhbRwQKDIUFYCccHBwkZZ23y+JtAHh8QkJC5O7urn/+858qU6aMeUvZDRs2qH///jp58qStIwIFhqlQgJ1Yv369rSMAQJG3efNmbdu2TU5OThbt/v7+unDhgo1SAY8HhQVgJ7i7NgDYXmZmZrYjxL/88ovc3d1tkAh4fJgKBdiZ27dvKy4uTmlpaRbt9erVs1EiACg6evbsKU9PT82aNUvu7u46cOCAypYtq+7du6ty5cr65ptvbB0RKDAUFoCduHr1qsLCwrRy5cpsn2eNBQAUvF9++UXBwcEyDEMnT55U48aNdfLkSXl7e2vTpk3y8fGxdUSgwDAVCrATgwcP1s2bN7Vz5061adNG33//vS5fvqwJEybo448/tnU8ACgSKlWqpP379ysmJkYHDhxQcnKy+vXrp969e8vV1dXW8YACxYgFYCfKly+vH374QU2aNJGHh4d2796tmjVratmyZZoyZYq2bNli64gAYPfu3LkjFxcXW8cAbMLB1gEA5I+UlBTzEHupUqV09epVSVJgYKD27t1ry2gAUGT4+Piob9++WrNmjTIzM20dB3isKCwAO/HUU0/p+PHjkqT69etr5syZunDhgqKjo1W+fHkbpwOAouHbb7/V7du31b17d1WsWFGDBw/W7t27bR0LeCyYCgXYiX//+99KT0/XG2+8oT179uiFF15QQkKCnJycNHv2bPXs2dPWEQGgyEhKStKiRYs0b948rVu3TlWrVtVrr72m0aNH2zoaUGAoLAA7dfv2bR07dkyVK1eWt7e3reMAQJF15MgR9e7dWwcOHGCHPtg1pkIBduK3i7Pd3NzUsGFDigoAsIE7d+5owYIFCgkJUcOGDZWQkKBhw4bZOhZQoBixAOyEk5OTKlasqNDQUL322muqU6eOrSMBQJGzevVqzZ07V0uXLlWxYsX0yiuvqHfv3mrVqpWtowEFjsICsBPXrl1TTEyM5s2bp+3bt6tevXrq3bu3QkNDValSJVvHA4Aiwc3NTS+++KJ69+6tzp07q3jx4raOBDw2FBaAHTp79qzmzp2refPm6dixY2rVqpXWrVtn61gAYPeSkpLk7u5u6xiATVBYAHYqIyNDK1euVGRkJAsGAaAAJSYmysPDw/z3R7nfD7BHxWwdAED+2rp1q+bMmaNFixbpzp076t69u6KiomwdCwDsVqlSpXTp0iX5+PjIy8tLJpMpSx/DMGQymfiSB3aNwgKwEyNHjlRMTIwuXryojh07atq0aerevbvc3NxsHQ0A7Nq6detUunRpSdL69ettnAawHaZCAXaiRYsW6t27t/74xz+yxSwA2EhcXJz8/PyyjFoYhqHz58+rcuXKNkoGFDwKCwAAgHzi6Ohonhb1oOvXr8vHx4epULBrTIUCCrFly5bluG+3bt0KMAkAQPq/tRS/lZycLBcXFxskAh4fCgugEAsJCbF4bDKZ9OAg5IP/ceNbMgAoOBEREZLufe5GRkZarG/LyMjQzp07FRQUZKN0wOPhYOsAAKyXmZlp/vPTTz8pKChIK1eu1M2bN3Xz5k2tWLFCDRs21KpVq2wdFQDsWmxsrGJjY2UYhg4ePGh+HBsbq2PHjql+/fqaPXu2rWMCBYo1FoCdqFu3rqKjo9WyZUuL9s2bN2vAgAE6evSojZIBQNERFhamadOmcb8KFElMhQLsxOnTp+Xl5ZWl3dPTU//73/8eex4AKIq++eYb89/Pnz8vSfLz87NVHOCxYioUYCeeeeYZRURE6PLly+a2y5cva9iwYWrSpIkNkwFA0ZGenq7IyEh5enrK399f/v7+8vT01KhRo3T37l1bxwMKFCMWgJ34+uuv1aNHD1WuXNn87dj58+dVo0YNLV261LbhAKCIGDhwoJYsWaIpU6aoWbNmkqTt27dr7Nixun79umbMmGHjhEDBYY0FYEcMw9CaNWt07NgxSVLt2rXVoUOHbLc+BADkP09PT8XExKhTp04W7StWrFBoaKhu3bplo2RAwWPEArAjJpNJzz//vJ5//nlbRwGAIsnZ2Vn+/v5Z2gMCAuTk5PT4AwGPESMWgB1JSUnRxo0bFRcXp7S0NIvnBg0aZKNUAFB0jB8/XseOHdM333wjZ2dnSVJqaqr69eunGjVqaMyYMTZOCBQcCgvATsTGxqpz5866ffu2UlJSVLp0aV27dk1ubm7y8fHRmTNnbB0RAOxejx49tHbtWjk7O6t+/fqSpP379ystLU3t27e36LtkyRJbRAQKDFOhADsxZMgQde3aVdHR0fL09NSOHTtUvHhxvfbaawoPD7d1PAAoEry8vPTyyy9btLHdLIoKRiwAO+Hl5aWdO3fqqaeekpeXl7Zv367atWtr586d6tu3r3lBNwAAQEHgPhaAnShevLgcHO79X9rHx0dxcXGS7u1Qcv8mTQAAAAWFqVCAnWjQoIH++9//qkaNGmrdurVGjx6ta9eu6bvvvlPdunVtHQ8AioxFixZpwYIF2W6ksXfvXhulAgoeIxaAnZg4caLKly8vSfrwww9VqlQpvfPOO7p69apmzZpl43QAUDR8+umnCgsLU7ly5RQbG6smTZqoTJkyOnPmTJZ7WwD2hjUWQBGzdetWNW7c2LwNIgAg/9SqVUtjxoxRaGio3N3dtX//flWtWlWjR49WQkKCpk+fbuuIQIFhxAIoYjp16qQLFy7YOgYA2KW4uDg1b95ckuTq6qqkpCRJ0uuvv6558+bZMhpQ4CgsgCKGQUoAKDi+vr5KSEiQJFWuXFk7duyQJJ09e5bPX9g9CgsAAIB80q5dOy1btkySFBYWpiFDhqhjx47q2bOnevToYeN0QMFijQVQxDw45xcAkL8yMzOVmZmpYsXubbwZExOjbdu2qUaNGnrrrbfk5ORk44RAwaGwAIoYCgsAAFAQuI8FUMSYTCZbRwAAu3LgwIEc961Xr14BJgFsi8ICKGIYpASA/BUUFCSTySTDMH73y5uMjIzHlAp4/Fi8DdiR9PR0/fzzz5o5c6Z5i8OLFy8qOTnZ3CcpKYlpUACQj86ePaszZ87o7NmzWrx4sQICAvTFF18oNjZWsbGx+uKLL1StWjUtXrzY1lGBAsUaC8BOnDt3Ti+88ILi4uKUmpqqEydOqGrVqgoPD1dqaqqio6NtHREA7F6TJk00duxYde7c2aJ9xYoVioyM1J49e2yUDCh4jFgAdiI8PFyNGzfWjRs35Orqam7v0aOH1q5da8NkAFB0HDx4UAEBAVnaAwICdOTIERskAh4fCgvATmzevFmjRo3KspWhv78/d9oGgMekdu3aioqKUlpamrktLS1NUVFRql27tg2TAQWPxduAncjMzMx2UeAvv/wid3d3GyQCgKInOjpaXbt2VaVKlcw7QN3fNerHH3+0ZTSgwLHGArATPXv2lKenp2bNmiV3d3cdOHBAZcuWVffu3VW5cmV98803to4IAEVCSkqK5syZo2PHjkm6N4rx6quvqkSJEjZOBhQsCgvATvzyyy8KDg6WYRg6efKkGjdurJMnT8rb21ubNm2Sj4+PrSMCQJFx5MgRxcXFWUyJkqRu3brZKBFQ8CgsADuSnp6umJgYHThwQMnJyWrYsKF69+5tsZgbAFBwzpw5ox49eujgwYPZ3tuC+1jAnlFYAHbizp07cnFxsXUMACjSunbtKkdHR3311VcKCAjQzp07lZCQoL/+9a/66KOP9Nxzz9k6IlBg2BUKsBM+Pj7q27ev1qxZo8zMTFvHAYAiafv27Ro/fry8vb3l4OAgR0dHtWzZUlFRURo0aJCt4wEFisICsBPffvutbt++re7du6tixYoaPHiwdu/ebetYAFCkZGRkmHfi8/b21sWLFyVJVapU0fHjx20ZDShwFBaAnejRo4cWLlyoy5cva+LEiTpy5IieffZZ1axZU+PHj7d1PAAoEurWrav9+/dLkpo2baopU6Zo69atGj9+vKpWrWrjdEDBYo0FYMeOHDmi3r1768CBAywYBIDHYPXq1UpJSdFLL72kU6dO6cUXX9SJEydUpkwZzZ8/X+3atbN1RKDAUFgAdubOnTtatmyZ5s6dq1WrVqlcuXIKDQ3VpEmTbB0NAIqkhIQElSpVymJ3KMAecedtwE6sXr1ac+fO1dKlS1WsWDG98sor+umnn9SqVStbRwOAIq106dK2jgA8FoxYAHbCzc1NL774onr37q3OnTurePHito4EAACKEAoLwE4kJSWZdyIBAAB43JgKBRRiiYmJ8vDwkCQZhqHExMSH9r3fDwAAoCAwYgEUYo6Ojrp06ZJ8fHzk4OCQ7cJAwzBkMpnYFQoAABQoRiyAQmzdunXmRYHr16+3cRoAAFCUMWIB2Im4uDj5+fllGbUwDEPnz59X5cqVbZQMAAAUBRQWgJ14cFrUg65fvy4fHx+mQgEAgALlYOsAAPLH/bUUv5WcnCwXFxcbJAIAAEUJayyAQi4iIkKSZDKZFBkZKTc3N/NzGRkZ2rlzp4KCgmyUDgAAFBUUFkAhFxsbK+neiMXBgwfl5ORkfs7JyUn169fX0KFDbRUPAAAUEayxAOxEWFiYpk2bxv0qAACATVBYAHbo/PnzkiQ/Pz8bJwEAAEUFi7cBO5Genq7IyEh5enrK399f/v7+8vT01KhRo3T37l1bxwMAAHaONRaAnRg4cKCWLFmiKVOmqFmzZpKk7du3a+zYsbp+/bpmzJhh44QAAMCeMRUKsBOenp6KiYlRp06dLNpXrFih0NBQ3bp1y0bJAABAUcBUKMBOODs7y9/fP0t7QECAxU5RAAAABYHCArAT7777rj744AOlpqaa21JTU/Xhhx/q3XfftWEyAABQFDAVCrATPXr00Nq1a+Xs7Kz69etLkvbv36+0tDS1b9/eou+SJUtsEREAANgxFm8DdsLLy0svv/yyRRvbzQIAgMeFEQsAAAAAecYaCwAAAAB5xlQowI4sWrRICxYsUFxcnNLS0iye27t3r41SAQCAooARC8BOfPrppwoLC1O5cuUUGxurJk2aqEyZMjpz5kyWe1sAAADkN9ZYAHaiVq1aGjNmjEJDQ+Xu7q79+/eratWqGj16tBISEjR9+nRbRwQAAHaMEQvATsTFxal58+aSJFdXVyUlJUmSXn/9dc2bN8+W0QAAQBFAYQHYCV9fXyUkJEiSKleurB07dkiSzp49KwYmAQBAQaOwAOxEu3bttGzZMklSWFiYhgwZoo4dO6pnz57q0aOHjdMBAAB7xxoLwE5kZmYqMzNTxYrd2+wtJiZG27ZtU40aNfTWW2/JycnJxgkBAIA9o7AAAAAAkGfcxwIoxA4cOJDjvvXq1SvAJAAAoKhjxAIoxBwcHGQymWQYhkwm0yP7ZmRkPKZUAACgKGLxNlCInT17VmfOnNHZs2e1ePFiBQQE6IsvvlBsbKxiY2P1xRdfqFq1alq8eLGtowIAADvHiAVgJ5o0aaKxY8eqc+fOFu0rVqxQZGSk9uzZY6NkAACgKGDEArATBw8eVEBAQJb2gIAAHTlyxAaJAABAUUJhAdiJ2rVrKyoqSmlpaea2tLQ0RUVFqXbt2jZMBgAAigKmQgF2YteuXeratasMwzDvAHV/16gff/xRTZo0sWU8AABg5ygsADuSkpKiOXPm6NixY5LujWK8+uqrKlGihI2TAQAAe0dhAdiZI0eOKC4uzmJKlCR169bNRokAAEBRwA3yADtx5swZ9ejRQwcPHsz23hbcxwIAABQkFm8DdiI8PFwBAQG6cuWK3NzcdOjQIW3cuFGNGzfWhg0bbB0PAADYOaZCAXbC29tb69atU7169eTp6aldu3bpqaee0rp16/TXv/5VsbGxto4IAADsGCMWgJ3IyMiQu7u7pHtFxsWLFyVJVapU0fHjx20ZDQAAFAGssQDsRN26dbV//34FBASoadOmmjJlipycnDRr1ixVrVrV1vEAAICdYyoUYCdWr16tlJQUvfTSSzp16pRefPFFnThxQmXKlNH8+fPVrl07W0cEAAB2jMICsGMJCQkqVaqUxe5QAAAABYHCAgAAAECesXgbAAAAQJ5RWAAAAADIMwoLAAAAAHlGYQEAAAAgzygsAAAAAOQZhQUAAACAPKOwAAAAAJBnFBYAAAAA8uz/AdwWjn79gMitAAAAAElFTkSuQmCC",
+                        "text/plain": [
+                            "<Figure size 800x400 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1800x450 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Risk direction agrees on 50 of 84 origins.\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>forecast_date</th>\n",
+                            "      <th>adaptive_advanced_probability</th>\n",
+                            "      <th>adaptive_lite_probability</th>\n",
+                            "      <th>adaptive_advanced_brier</th>\n",
+                            "      <th>adaptive_lite_brier</th>\n",
+                            "      <th>adaptive_advanced_risk_direction</th>\n",
+                            "      <th>adaptive_lite_risk_direction</th>\n",
+                            "      <th>adaptive_advanced_rationale</th>\n",
+                            "      <th>adaptive_lite_rationale</th>\n",
+                            "      <th>adaptive_advanced_supporting_evidence</th>\n",
+                            "      <th>adaptive_lite_supporting_evidence</th>\n",
+                            "      <th>adaptive_advanced_countervailing_evidence</th>\n",
+                            "      <th>adaptive_lite_countervailing_evidence</th>\n",
+                            "      <th>observed_outcome</th>\n",
+                            "      <th>probability_delta_advanced_minus_lite</th>\n",
+                            "      <th>risk_direction_agrees</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>2018-04-01</td>\n",
+                            "      <td>0.05</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>0.0025</td>\n",
+                            "      <td>0.003364</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>Strong three-month IPMAN growth of 0.88% and r...</td>\n",
+                            "      <td>The 3-month IPMAN change is positive (+0.88%),...</td>\n",
+                            "      <td>Positive three-month IPMAN growth of 0.88% and...</td>\n",
+                            "      <td>None are material</td>\n",
+                            "      <td>A slight one-month decline in IPMAN of -0.26%</td>\n",
+                            "      <td>3-month IPMAN change is positive at 0.88%, sig...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.008</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>2018-05-01</td>\n",
+                            "      <td>0.052</td>\n",
+                            "      <td>0.055</td>\n",
+                            "      <td>0.002704</td>\n",
+                            "      <td>0.003025</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>Strong macro indicators, including high XLI an...</td>\n",
+                            "      <td>The 3-month IPMAN contraction of -0.5% is well...</td>\n",
+                            "      <td>Strong XLI 3-month return of 12.07% and SPY 3-...</td>\n",
+                            "      <td>3-month IPMAN contraction of -0.5% is signific...</td>\n",
+                            "      <td>Recent 3-month IPMAN decline of -0.50% and 1-m...</td>\n",
+                            "      <td>None are material; macroeconomic indicators li...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.003</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>2018-06-01</td>\n",
+                            "      <td>0.05</td>\n",
+                            "      <td>0.055</td>\n",
+                            "      <td>0.0025</td>\n",
+                            "      <td>0.003025</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>Strong recent IPMAN growth (+0.87% monthly) an...</td>\n",
+                            "      <td>IPMAN shows positive 3-month growth (0.29%), r...</td>\n",
+                            "      <td>Positive 1-month IPMAN growth of 0.87% and 3-m...</td>\n",
+                            "      <td>None are material</td>\n",
+                            "      <td>Slightly elevated VIX at 19.85 and a narrowing...</td>\n",
+                            "      <td>Positive 3-month IPMAN growth of 0.29% and sta...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.005</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>2018-07-01</td>\n",
+                            "      <td>0.055</td>\n",
+                            "      <td>0.061</td>\n",
+                            "      <td>0.003025</td>\n",
+                            "      <td>0.003721</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>neutral</td>\n",
+                            "      <td>With three-month IPMAN growth positive at 0.50...</td>\n",
+                            "      <td>The 3-month IPMAN change is positive (+0.497%)...</td>\n",
+                            "      <td>Slight negative 1-month IPMAN change of -0.05%...</td>\n",
+                            "      <td>3-month IPMAN change is positive at 0.497%</td>\n",
+                            "      <td>Positive 3-month IPMAN growth of 0.50% and a 0...</td>\n",
+                            "      <td>1-month IPMAN change is slightly negative at -...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.006</td>\n",
+                            "      <td>False</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>2018-08-01</td>\n",
+                            "      <td>0.05</td>\n",
+                            "      <td>0.065</td>\n",
+                            "      <td>0.0025</td>\n",
+                            "      <td>0.004225</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>up</td>\n",
+                            "      <td>Strong recent IPMAN growth of 1.57% over three...</td>\n",
+                            "      <td>IPMAN shows positive 3-month growth (+1.57%), ...</td>\n",
+                            "      <td>Strong 3-month IPMAN growth of 1.57% and low u...</td>\n",
+                            "      <td>Negative 3-month returns for SPY (-5.79%) and ...</td>\n",
+                            "      <td>Negative 3-month equity returns (SPY -5.79%, X...</td>\n",
+                            "      <td>IPMAN 3-month growth is positive at 1.57%, sig...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.015</td>\n",
+                            "      <td>False</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>...</th>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>79</th>\n",
+                            "      <td>2024-11-01</td>\n",
+                            "      <td>0.068</td>\n",
+                            "      <td>0.064</td>\n",
+                            "      <td>0.004624</td>\n",
+                            "      <td>0.004096</td>\n",
+                            "      <td>neutral</td>\n",
+                            "      <td>neutral</td>\n",
+                            "      <td>Recent 1-month IPMAN decline of -0.78% and inv...</td>\n",
+                            "      <td>The 3-month IPMAN contraction of -0.41% remain...</td>\n",
+                            "      <td>Recent 1-month IPMAN decline of -0.78% and inv...</td>\n",
+                            "      <td>3-month IPMAN contraction of -0.41% is signifi...</td>\n",
+                            "      <td>Strong 3-month SPY return of 10.08% and XLI re...</td>\n",
+                            "      <td>None are material; current macro signals and I...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.004</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>80</th>\n",
+                            "      <td>2024-12-01</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>0.06</td>\n",
+                            "      <td>0.003364</td>\n",
+                            "      <td>0.0036</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>With 1-month IPMAN rebounding by 0.48% and the...</td>\n",
+                            "      <td>The 3-month IPMAN contraction of -0.49% remain...</td>\n",
+                            "      <td>Recent 1-month IPMAN growth of 0.48% indicates...</td>\n",
+                            "      <td>3-month trailing IPMAN contraction of -0.49% i...</td>\n",
+                            "      <td>The yield curve remains inverted at -0.20%, an...</td>\n",
+                            "      <td>None are material.</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.002</td>\n",
+                            "      <td>True</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>81</th>\n",
+                            "      <td>2025-01-01</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>0.0622</td>\n",
+                            "      <td>0.003364</td>\n",
+                            "      <td>0.003869</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>neutral</td>\n",
+                            "      <td>With trailing three-month IPMAN contraction at...</td>\n",
+                            "      <td>The current 3-month IPMAN contraction of -0.87...</td>\n",
+                            "      <td>Trailing three-month IPMAN contraction of -0.8...</td>\n",
+                            "      <td>Trailing 3-month IPMAN contraction of -0.87% i...</td>\n",
+                            "      <td>Recent one-month IPMAN declined by -0.56%, ind...</td>\n",
+                            "      <td>Positive 3-month and 12-month SPY and XLI retu...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.0042</td>\n",
+                            "      <td>False</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>82</th>\n",
+                            "      <td>2025-02-01</td>\n",
+                            "      <td>0.062</td>\n",
+                            "      <td>0.06</td>\n",
+                            "      <td>0.003844</td>\n",
+                            "      <td>0.0036</td>\n",
+                            "      <td>neutral</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>IPMAN's 3-month contraction of -0.75% remains ...</td>\n",
+                            "      <td>The current 3-month IPMAN contraction of -0.75...</td>\n",
+                            "      <td>Recent consecutive monthly declines in IPMAN, ...</td>\n",
+                            "      <td>Current 3-month IPMAN contraction of -0.75% is...</td>\n",
+                            "      <td>The 3-month IPMAN change of -0.75% is well abo...</td>\n",
+                            "      <td>None are material.</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.002</td>\n",
+                            "      <td>False</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>83</th>\n",
+                            "      <td>2025-03-01</td>\n",
+                            "      <td>0.06</td>\n",
+                            "      <td>0.063</td>\n",
+                            "      <td>0.0036</td>\n",
+                            "      <td>0.003969</td>\n",
+                            "      <td>down</td>\n",
+                            "      <td>neutral</td>\n",
+                            "      <td>Recent 1-month IPMAN growth of 0.29% and stron...</td>\n",
+                            "      <td>The current 3-month IPMAN contraction of -0.93...</td>\n",
+                            "      <td>Negative trailing 3-month and 6-month IPMAN gr...</td>\n",
+                            "      <td>Current 3-month IPMAN contraction of -0.93% is...</td>\n",
+                            "      <td>Positive 1-month IPMAN growth of 0.29% and str...</td>\n",
+                            "      <td>None are material; current macro signals and r...</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>-0.003</td>\n",
+                            "      <td>False</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "<p>84 rows × 16 columns</p>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "   forecast_date adaptive_advanced_probability adaptive_lite_probability  \\\n",
+                            "0     2018-04-01                          0.05                     0.058   \n",
+                            "1     2018-05-01                         0.052                     0.055   \n",
+                            "2     2018-06-01                          0.05                     0.055   \n",
+                            "3     2018-07-01                         0.055                     0.061   \n",
+                            "4     2018-08-01                          0.05                     0.065   \n",
+                            "..           ...                           ...                       ...   \n",
+                            "79    2024-11-01                         0.068                     0.064   \n",
+                            "80    2024-12-01                         0.058                      0.06   \n",
+                            "81    2025-01-01                         0.058                    0.0622   \n",
+                            "82    2025-02-01                         0.062                      0.06   \n",
+                            "83    2025-03-01                          0.06                     0.063   \n",
+                            "\n",
+                            "   adaptive_advanced_brier adaptive_lite_brier  \\\n",
+                            "0                   0.0025            0.003364   \n",
+                            "1                 0.002704            0.003025   \n",
+                            "2                   0.0025            0.003025   \n",
+                            "3                 0.003025            0.003721   \n",
+                            "4                   0.0025            0.004225   \n",
+                            "..                     ...                 ...   \n",
+                            "79                0.004624            0.004096   \n",
+                            "80                0.003364              0.0036   \n",
+                            "81                0.003364            0.003869   \n",
+                            "82                0.003844              0.0036   \n",
+                            "83                  0.0036            0.003969   \n",
+                            "\n",
+                            "   adaptive_advanced_risk_direction adaptive_lite_risk_direction  \\\n",
+                            "0                              down                         down   \n",
+                            "1                              down                         down   \n",
+                            "2                              down                         down   \n",
+                            "3                              down                      neutral   \n",
+                            "4                              down                           up   \n",
+                            "..                              ...                          ...   \n",
+                            "79                          neutral                      neutral   \n",
+                            "80                             down                         down   \n",
+                            "81                             down                      neutral   \n",
+                            "82                          neutral                         down   \n",
+                            "83                             down                      neutral   \n",
+                            "\n",
+                            "                          adaptive_advanced_rationale  \\\n",
+                            "0   Strong three-month IPMAN growth of 0.88% and r...   \n",
+                            "1   Strong macro indicators, including high XLI an...   \n",
+                            "2   Strong recent IPMAN growth (+0.87% monthly) an...   \n",
+                            "3   With three-month IPMAN growth positive at 0.50...   \n",
+                            "4   Strong recent IPMAN growth of 1.57% over three...   \n",
+                            "..                                                ...   \n",
+                            "79  Recent 1-month IPMAN decline of -0.78% and inv...   \n",
+                            "80  With 1-month IPMAN rebounding by 0.48% and the...   \n",
+                            "81  With trailing three-month IPMAN contraction at...   \n",
+                            "82  IPMAN's 3-month contraction of -0.75% remains ...   \n",
+                            "83  Recent 1-month IPMAN growth of 0.29% and stron...   \n",
+                            "\n",
+                            "                              adaptive_lite_rationale  \\\n",
+                            "0   The 3-month IPMAN change is positive (+0.88%),...   \n",
+                            "1   The 3-month IPMAN contraction of -0.5% is well...   \n",
+                            "2   IPMAN shows positive 3-month growth (0.29%), r...   \n",
+                            "3   The 3-month IPMAN change is positive (+0.497%)...   \n",
+                            "4   IPMAN shows positive 3-month growth (+1.57%), ...   \n",
+                            "..                                                ...   \n",
+                            "79  The 3-month IPMAN contraction of -0.41% remain...   \n",
+                            "80  The 3-month IPMAN contraction of -0.49% remain...   \n",
+                            "81  The current 3-month IPMAN contraction of -0.87...   \n",
+                            "82  The current 3-month IPMAN contraction of -0.75...   \n",
+                            "83  The current 3-month IPMAN contraction of -0.93...   \n",
+                            "\n",
+                            "                adaptive_advanced_supporting_evidence  \\\n",
+                            "0   Positive three-month IPMAN growth of 0.88% and...   \n",
+                            "1   Strong XLI 3-month return of 12.07% and SPY 3-...   \n",
+                            "2   Positive 1-month IPMAN growth of 0.87% and 3-m...   \n",
+                            "3   Slight negative 1-month IPMAN change of -0.05%...   \n",
+                            "4   Strong 3-month IPMAN growth of 1.57% and low u...   \n",
+                            "..                                                ...   \n",
+                            "79  Recent 1-month IPMAN decline of -0.78% and inv...   \n",
+                            "80  Recent 1-month IPMAN growth of 0.48% indicates...   \n",
+                            "81  Trailing three-month IPMAN contraction of -0.8...   \n",
+                            "82  Recent consecutive monthly declines in IPMAN, ...   \n",
+                            "83  Negative trailing 3-month and 6-month IPMAN gr...   \n",
+                            "\n",
+                            "                    adaptive_lite_supporting_evidence  \\\n",
+                            "0                                   None are material   \n",
+                            "1   3-month IPMAN contraction of -0.5% is signific...   \n",
+                            "2                                   None are material   \n",
+                            "3          3-month IPMAN change is positive at 0.497%   \n",
+                            "4   Negative 3-month returns for SPY (-5.79%) and ...   \n",
+                            "..                                                ...   \n",
+                            "79  3-month IPMAN contraction of -0.41% is signifi...   \n",
+                            "80  3-month trailing IPMAN contraction of -0.49% i...   \n",
+                            "81  Trailing 3-month IPMAN contraction of -0.87% i...   \n",
+                            "82  Current 3-month IPMAN contraction of -0.75% is...   \n",
+                            "83  Current 3-month IPMAN contraction of -0.93% is...   \n",
+                            "\n",
+                            "            adaptive_advanced_countervailing_evidence  \\\n",
+                            "0       A slight one-month decline in IPMAN of -0.26%   \n",
+                            "1   Recent 3-month IPMAN decline of -0.50% and 1-m...   \n",
+                            "2   Slightly elevated VIX at 19.85 and a narrowing...   \n",
+                            "3   Positive 3-month IPMAN growth of 0.50% and a 0...   \n",
+                            "4   Negative 3-month equity returns (SPY -5.79%, X...   \n",
+                            "..                                                ...   \n",
+                            "79  Strong 3-month SPY return of 10.08% and XLI re...   \n",
+                            "80  The yield curve remains inverted at -0.20%, an...   \n",
+                            "81  Recent one-month IPMAN declined by -0.56%, ind...   \n",
+                            "82  The 3-month IPMAN change of -0.75% is well abo...   \n",
+                            "83  Positive 1-month IPMAN growth of 0.29% and str...   \n",
+                            "\n",
+                            "                adaptive_lite_countervailing_evidence  observed_outcome  \\\n",
+                            "0   3-month IPMAN change is positive at 0.88%, sig...                 0   \n",
+                            "1   None are material; macroeconomic indicators li...                 0   \n",
+                            "2   Positive 3-month IPMAN growth of 0.29% and sta...                 0   \n",
+                            "3   1-month IPMAN change is slightly negative at -...                 0   \n",
+                            "4   IPMAN 3-month growth is positive at 1.57%, sig...                 0   \n",
+                            "..                                                ...               ...   \n",
+                            "79  None are material; current macro signals and I...                 0   \n",
+                            "80                                 None are material.                 0   \n",
+                            "81  Positive 3-month and 12-month SPY and XLI retu...                 0   \n",
+                            "82                                 None are material.                 0   \n",
+                            "83  None are material; current macro signals and r...                 0   \n",
+                            "\n",
+                            "   probability_delta_advanced_minus_lite  risk_direction_agrees  \n",
+                            "0                                 -0.008                   True  \n",
+                            "1                                 -0.003                   True  \n",
+                            "2                                 -0.005                   True  \n",
+                            "3                                 -0.006                  False  \n",
+                            "4                                 -0.015                  False  \n",
+                            "..                                   ...                    ...  \n",
+                            "79                                 0.004                   True  \n",
+                            "80                                -0.002                   True  \n",
+                            "81                               -0.0042                  False  \n",
+                            "82                                 0.002                  False  \n",
+                            "83                                -0.003                  False  \n",
+                            "\n",
+                            "[84 rows x 16 columns]"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "import matplotlib.pyplot as plt\n",
+                "\n",
+                "if results:\n",
+                "    score_frame = pd.DataFrame([\n",
+                "        {'model': variant_id, 'mean_brier': float(result.mean_score)}\n",
+                "        for variant_id, result in results.items()\n",
+                "    ]).sort_values('mean_brier')\n",
+                "    ax = score_frame.plot.bar(x='model', y='mean_brier', legend=False, figsize=(8, 4), color=['#2f6f8f', '#d9822b'])\n",
+                "    ax.set_title('Adaptive model comparison')\n",
+                "    ax.set_ylabel('Mean Brier score (lower is better)')\n",
+                "    ax.set_xlabel('')\n",
+                "    ax.grid(axis='y', alpha=0.25)\n",
+                "    plt.tight_layout()\n",
+                "    plt.show()\n",
+                "\n",
+                "    origin_frames = []\n",
+                "    for variant_id, result in results.items():\n",
+                "        for prediction, score in zip(result.predictions, result.scores):\n",
+                "            probability = float(prediction.payload.probability)\n",
+                "            zero_score = probability**2\n",
+                "            one_score = (1.0 - probability)**2\n",
+                "            distances = {0: abs(float(score) - zero_score), 1: abs(float(score) - one_score)}\n",
+                "            outcome = min(distances, key=distances.get)\n",
+                "            if distances[outcome] > 1e-5:\n",
+                "                raise ValueError('Could not recover a binary outcome from a Brier score.')\n",
+                "            origin_frames.append({\n",
+                "                'model': variant_id,\n",
+                "                'forecast_date': pd.Timestamp(prediction.forecast_date),\n",
+                "                'probability': probability,\n",
+                "                'outcome': outcome,\n",
+                "                'brier': float(score),\n",
+                "                'risk_direction': prediction.metadata['direction'],\n",
+                "                'rationale': prediction.metadata['rationale'],\n",
+                "                'supporting_evidence': '; '.join(prediction.metadata['supporting_evidence']),\n",
+                "                'countervailing_evidence': '; '.join(prediction.metadata['countervailing_evidence']),\n",
+                "            })\n",
+                "    origin_frame = pd.DataFrame(origin_frames).sort_values(['forecast_date', 'model'])\n",
+                "    wide = origin_frame.pivot(index='forecast_date', columns='model', values='probability')\n",
+                "    comparison_fields = ['probability', 'brier', 'risk_direction', 'rationale', 'supporting_evidence', 'countervailing_evidence']\n",
+                "    decision_comparison = origin_frame.pivot(index='forecast_date', columns='model', values=comparison_fields)\n",
+                "    decision_comparison.columns = [f'{model}_{field}' for field, model in decision_comparison.columns]\n",
+                "    outcomes = origin_frame.drop_duplicates('forecast_date').set_index('forecast_date')['outcome']\n",
+                "    decision_comparison['observed_outcome'] = outcomes\n",
+                "    decision_comparison['probability_delta_advanced_minus_lite'] = (\n",
+                "        decision_comparison['adaptive_advanced_probability'] - decision_comparison['adaptive_lite_probability']\n",
+                "    )\n",
+                "    decision_comparison['risk_direction_agrees'] = (\n",
+                "        decision_comparison['adaptive_lite_risk_direction'] == decision_comparison['adaptive_advanced_risk_direction']\n",
+                "    )\n",
+                "\n",
+                "    fig, axes = plt.subplots(1, 3, figsize=(18, 4.5), constrained_layout=True)\n",
+                "    for model in wide.columns:\n",
+                "        axes[0].plot(wide.index, wide[model], marker='o', label=model)\n",
+                "    axes[0].step(outcomes.index, outcomes, where='mid', color='black', alpha=0.55, label='observed stress')\n",
+                "    axes[0].set_title('Per-origin forecast probabilities')\n",
+                "    axes[0].set_ylabel('Probability / outcome')\n",
+                "    axes[0].legend()\n",
+                "    axes[0].grid(alpha=0.2)\n",
+                "\n",
+                "    if {'adaptive_lite', 'adaptive_advanced'}.issubset(wide.columns):\n",
+                "        difference = wide['adaptive_advanced'] - wide['adaptive_lite']\n",
+                "        axes[1].axhline(0, color='black', linewidth=0.8)\n",
+                "        axes[1].bar(difference.index, difference.values, width=20, color='#b85c38')\n",
+                "        axes[1].set_title('Advanced minus lite probability')\n",
+                "        axes[1].set_ylabel('Probability difference')\n",
+                "        axes[1].grid(axis='y', alpha=0.2)\n",
+                "    else:\n",
+                "        axes[1].set_visible(False)\n",
+                "\n",
+                "    for model, group in origin_frame.groupby('model'):\n",
+                "        bins = pd.cut(group['probability'], bins=[0, .2, .4, .6, .8, 1], include_lowest=True)\n",
+                "        calibration = group.assign(bin=bins).groupby('bin', observed=False).agg(\n",
+                "            predicted=('probability', 'mean'), observed=('outcome', 'mean'), count=('outcome', 'size'))\n",
+                "        calibration = calibration[calibration['count'] > 0]\n",
+                "        axes[2].plot(calibration['predicted'], calibration['observed'], marker='o', label=model)\n",
+                "    axes[2].plot([0, 1], [0, 1], '--', color='gray', label='perfect calibration')\n",
+                "    axes[2].set_title('Calibration by probability bin')\n",
+                "    axes[2].set_xlabel('Mean predicted probability')\n",
+                "    axes[2].set_ylabel('Observed event rate')\n",
+                "    axes[2].legend()\n",
+                "    axes[2].grid(alpha=0.2)\n",
+                "    plt.show()\n",
+                "\n",
+                "    print(f\"Risk direction agrees on {decision_comparison['risk_direction_agrees'].sum()} of {len(decision_comparison)} origins.\")\n",
+                "    display(decision_comparison.reset_index())\n",
+                "else:\n",
+                "    print('Run the comparison to render score, per-origin, difference, and calibration plots.')"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": ".venv (3.12.3)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.12.3"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_data_understanding.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_data_understanding.ipynb
new file mode 100644
index 000000000..69e6350de
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_data_understanding.ipynb
@@ -0,0 +1,4261 @@
+{
+          "cells": [
+                    {
+                              "cell_type": "markdown",
+                              "id": "5f83072e",
+                              "metadata": {
+                                        "id": "5f83072e",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "# Manufacturing Stress Data Understanding\n",
+                                        "\n",
+                                        "This manufacturing-only notebook inspects the registered data, transformations, target definition, and descriptive relationships before interpreting any forecast-model result. It does not run a backtest, fit a model, or call an LLM.\n",
+                                        "\n",
+                                        "The correlation sections align values by their reference month. The final point-in-time section separately demonstrates the stricter release-date cutoff required for forecasting. Correlation is descriptive, not causal evidence or evidence of predictive performance."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 31,
+                              "id": "ac0c7417",
+                              "metadata": {
+                                        "id": "ac0c7417",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "Running: uv sync --all-extras --dev --all-packages\n",
+                                                            "\n",
+                                                            "Environment synchronized successfully.\n",
+                                                            "Restart the kernel before continuing if package versions changed.\n"
+                                                  ]
+                                        }
+                              ],
+                              "source": [
+                                        "# Synchronize the repository environment before running the analysis.\n",
+                                        "import subprocess\n",
+                                        "from pathlib import Path\n",
+                                        "\n",
+                                        "\n",
+                                        "def find_repo_root(start: Path) -> Path:\n",
+                                        "    \"\"\"Find the workspace root without confusing member pyproject files for it.\"\"\"\n",
+                                        "    for candidate in (start, *start.parents):\n",
+                                        "        if (\n",
+                                        "            (candidate / \"uv.lock\").exists()\n",
+                                        "            and (candidate / \"implementations\").is_dir()\n",
+                                        "            and (candidate / \"aieng-forecasting\").is_dir()\n",
+                                        "        ):\n",
+                                        "            return candidate\n",
+                                        "    raise FileNotFoundError(\"Could not find the agentic-forecasting repository root.\")\n",
+                                        "\n",
+                                        "\n",
+                                        "REPO_ROOT = find_repo_root(Path.cwd().resolve())\n",
+                                        "print(\"Running: uv sync --all-extras --dev --all-packages\")\n",
+                                        "setup_result = subprocess.run(\n",
+                                        "    [\"uv\", \"sync\", \"--all-extras\", \"--dev\", \"--all-packages\"],\n",
+                                        "    cwd=REPO_ROOT,\n",
+                                        "    check=False,\n",
+                                        "    text=True,\n",
+                                        "    capture_output=True,\n",
+                                        ")\n",
+                                        "print(setup_result.stdout)\n",
+                                        "if setup_result.returncode != 0:\n",
+                                        "    print(setup_result.stderr)\n",
+                                        "    raise RuntimeError(\"uv sync failed. Install uv or restart with the repository environment selected.\")\n",
+                                        "print(\"Environment synchronized successfully.\")\n",
+                                        "print(\"Restart the kernel before continuing if package versions changed.\")"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "b663959b",
+                              "metadata": {
+                                        "id": "b663959b",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Analysis controls\n",
+                                        "\n",
+                                        "Use cached data by default. Refreshing contacts FRED, Yahoo Finance, and the New York Fed. The date range affects descriptive charts and tables only; it does not alter the registered service or target construction."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 32,
+                              "id": "cc148e73",
+                              "metadata": {
+                                        "id": "cc148e73",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "{'refresh_input_data': False, 'analysis_start': '2000-01-01', 'analysis_end': None, 'correlation_lag_months': 18, 'rolling_window_months': 24}\n"
+                                                  ]
+                                        }
+                              ],
+                              "source": [
+                                        "REFRESH_INPUT_DATA = False\n",
+                                        "ANALYSIS_START = \"2000-01-01\"\n",
+                                        "ANALYSIS_END = None\n",
+                                        "CORRELATION_LAG_MONTHS = 18\n",
+                                        "ROLLING_WINDOW_MONTHS = 24\n",
+                                        "\n",
+                                        "print(\n",
+                                        "    {\n",
+                                        "        \"refresh_input_data\": REFRESH_INPUT_DATA,\n",
+                                        "        \"analysis_start\": ANALYSIS_START,\n",
+                                        "        \"analysis_end\": ANALYSIS_END,\n",
+                                        "        \"correlation_lag_months\": CORRELATION_LAG_MONTHS,\n",
+                                        "        \"rolling_window_months\": ROLLING_WINDOW_MONTHS,\n",
+                                        "    }\n",
+                                        ")"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "deed9473",
+                              "metadata": {
+                                        "id": "deed9473",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Imports, paths, and registered manufacturing data\n",
+                                        "\n",
+                                        "The analysis reads through `build_manufacturing_stress_service()`. This preserves the same transformations and release-date metadata used by the forecasting workflow."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 33,
+                              "id": "dbf5c1bf",
+                              "metadata": {
+                                        "id": "dbf5c1bf",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "Registered manufacturing series: 20\n"
+                                                  ]
+                                        },
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th>description</th>\n",
+                                                                      "      <th>source</th>\n",
+                                                                      "      <th>units</th>\n",
+                                                                      "      <th>frequency</th>\n",
+                                                                      "      <th>n_obs</th>\n",
+                                                                      "      <th>start</th>\n",
+                                                                      "      <th>end</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>CPIAUCSL</td>\n",
+                                                                      "      <td>Consumer Price Index for All Urban Consumers</td>\n",
+                                                                      "      <td>FRED (CPIAUCSL)</td>\n",
+                                                                      "      <td>Index</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>955</td>\n",
+                                                                      "      <td>1947-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1</th>\n",
+                                                                      "      <td>CPI_YOY</td>\n",
+                                                                      "      <td>Consumer Price Index year-over-year change</td>\n",
+                                                                      "      <td>Derived from FRED (CPIAUCSL)</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>2</th>\n",
+                                                                      "      <td>FEDFUNDS</td>\n",
+                                                                      "      <td>Effective federal funds rate</td>\n",
+                                                                      "      <td>FRED (FEDFUNDS)</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>3</th>\n",
+                                                                      "      <td>HY_SPREAD</td>\n",
+                                                                      "      <td>U.S. high-yield option-adjusted spread</td>\n",
+                                                                      "      <td>FRED (BAMLH0A0HYM2)</td>\n",
+                                                                      "      <td>Percentage points</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>37</td>\n",
+                                                                      "      <td>2023-10-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>4</th>\n",
+                                                                      "      <td>ICSA</td>\n",
+                                                                      "      <td>Initial claims for unemployment insurance</td>\n",
+                                                                      "      <td>FRED (ICSA)</td>\n",
+                                                                      "      <td>Number</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>717</td>\n",
+                                                                      "      <td>1967-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>5</th>\n",
+                                                                      "      <td>SPY_RETURN_12M</td>\n",
+                                                                      "      <td>SPY trailing 12-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (SPY), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>334</td>\n",
+                                                                      "      <td>1999-01-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>6</th>\n",
+                                                                      "      <td>SPY_RETURN_3M</td>\n",
+                                                                      "      <td>SPY trailing 3-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (SPY), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>343</td>\n",
+                                                                      "      <td>1998-04-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>7</th>\n",
+                                                                      "      <td>UNRATE</td>\n",
+                                                                      "      <td>U.S. unemployment rate</td>\n",
+                                                                      "      <td>FRED (UNRATE)</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>8</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>CBOE volatility index</td>\n",
+                                                                      "      <td>FRED (VIXCLS)</td>\n",
+                                                                      "      <td>Index</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>441</td>\n",
+                                                                      "      <td>1990-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>9</th>\n",
+                                                                      "      <td>XLI_RETURN_12M</td>\n",
+                                                                      "      <td>XLI trailing 12-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (XLI), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>323</td>\n",
+                                                                      "      <td>1999-12-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>10</th>\n",
+                                                                      "      <td>XLI_RETURN_3M</td>\n",
+                                                                      "      <td>XLI trailing 3-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (XLI), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>332</td>\n",
+                                                                      "      <td>1999-03-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>11</th>\n",
+                                                                      "      <td>YC_SPREAD</td>\n",
+                                                                      "      <td>10-year minus 2-year Treasury yield spread</td>\n",
+                                                                      "      <td>FRED (DGS10 minus DGS2)</td>\n",
+                                                                      "      <td>Percentage points</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>12</th>\n",
+                                                                      "      <td>fed_funds_rate_pct</td>\n",
+                                                                      "      <td>Month-end effective federal funds rate</td>\n",
+                                                                      "      <td>FRED (FEDFUNDS), derived monthly</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>13</th>\n",
+                                                                      "      <td>global_supply_chain_pressure_index</td>\n",
+                                                                      "      <td>New York Fed Global Supply Chain Pressure Index</td>\n",
+                                                                      "      <td>Federal Reserve Bank of New York (GSCPI)</td>\n",
+                                                                      "      <td>Standard deviations from historical average</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>348</td>\n",
+                                                                      "      <td>1997-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>14</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>Trailing 1-month percentage change in IPMAN</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>655</td>\n",
+                                                                      "      <td>1972-02-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>15</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>Trailing 3-month percentage change in IPMAN</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>16</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>Trailing 6-month percentage change in IPMAN</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>650</td>\n",
+                                                                      "      <td>1972-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>17</th>\n",
+                                                                      "      <td>ipman_us_manufacturing_production</td>\n",
+                                                                      "      <td>U.S. manufacturing industrial production index...</td>\n",
+                                                                      "      <td>FRED (IPMAN)</td>\n",
+                                                                      "      <td>Index</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>656</td>\n",
+                                                                      "      <td>1972-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>18</th>\n",
+                                                                      "      <td>manufacturing_stress</td>\n",
+                                                                      "      <td>Binary U.S. manufacturing stress label: 1 when...</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Binary event (0=no stress, 1=stress)</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>19</th>\n",
+                                                                      "      <td>treasury_10y_minus_2y_pct_points</td>\n",
+                                                                      "      <td>Month-end 10-year minus 2-year Treasury yield ...</td>\n",
+                                                                      "      <td>FRED (DGS10 minus DGS2), derived monthly</td>\n",
+                                                                      "      <td>Percentage points</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                             series_id  \\\n",
+                                                                      "0                             CPIAUCSL   \n",
+                                                                      "1                              CPI_YOY   \n",
+                                                                      "2                             FEDFUNDS   \n",
+                                                                      "3                            HY_SPREAD   \n",
+                                                                      "4                                 ICSA   \n",
+                                                                      "5                       SPY_RETURN_12M   \n",
+                                                                      "6                        SPY_RETURN_3M   \n",
+                                                                      "7                               UNRATE   \n",
+                                                                      "8                               VIXCLS   \n",
+                                                                      "9                       XLI_RETURN_12M   \n",
+                                                                      "10                       XLI_RETURN_3M   \n",
+                                                                      "11                           YC_SPREAD   \n",
+                                                                      "12                  fed_funds_rate_pct   \n",
+                                                                      "13  global_supply_chain_pressure_index   \n",
+                                                                      "14                 ipman_change_1m_pct   \n",
+                                                                      "15                 ipman_change_3m_pct   \n",
+                                                                      "16                 ipman_change_6m_pct   \n",
+                                                                      "17   ipman_us_manufacturing_production   \n",
+                                                                      "18                manufacturing_stress   \n",
+                                                                      "19    treasury_10y_minus_2y_pct_points   \n",
+                                                                      "\n",
+                                                                      "                                          description  \\\n",
+                                                                      "0        Consumer Price Index for All Urban Consumers   \n",
+                                                                      "1          Consumer Price Index year-over-year change   \n",
+                                                                      "2                        Effective federal funds rate   \n",
+                                                                      "3              U.S. high-yield option-adjusted spread   \n",
+                                                                      "4           Initial claims for unemployment insurance   \n",
+                                                                      "5         SPY trailing 12-month adjusted-close return   \n",
+                                                                      "6          SPY trailing 3-month adjusted-close return   \n",
+                                                                      "7                              U.S. unemployment rate   \n",
+                                                                      "8                               CBOE volatility index   \n",
+                                                                      "9         XLI trailing 12-month adjusted-close return   \n",
+                                                                      "10         XLI trailing 3-month adjusted-close return   \n",
+                                                                      "11         10-year minus 2-year Treasury yield spread   \n",
+                                                                      "12             Month-end effective federal funds rate   \n",
+                                                                      "13    New York Fed Global Supply Chain Pressure Index   \n",
+                                                                      "14        Trailing 1-month percentage change in IPMAN   \n",
+                                                                      "15        Trailing 3-month percentage change in IPMAN   \n",
+                                                                      "16        Trailing 6-month percentage change in IPMAN   \n",
+                                                                      "17  U.S. manufacturing industrial production index...   \n",
+                                                                      "18  Binary U.S. manufacturing stress label: 1 when...   \n",
+                                                                      "19  Month-end 10-year minus 2-year Treasury yield ...   \n",
+                                                                      "\n",
+                                                                      "                                      source  \\\n",
+                                                                      "0                            FRED (CPIAUCSL)   \n",
+                                                                      "1               Derived from FRED (CPIAUCSL)   \n",
+                                                                      "2                            FRED (FEDFUNDS)   \n",
+                                                                      "3                        FRED (BAMLH0A0HYM2)   \n",
+                                                                      "4                                FRED (ICSA)   \n",
+                                                                      "5               Yahoo Finance (SPY), derived   \n",
+                                                                      "6               Yahoo Finance (SPY), derived   \n",
+                                                                      "7                              FRED (UNRATE)   \n",
+                                                                      "8                              FRED (VIXCLS)   \n",
+                                                                      "9               Yahoo Finance (XLI), derived   \n",
+                                                                      "10              Yahoo Finance (XLI), derived   \n",
+                                                                      "11                   FRED (DGS10 minus DGS2)   \n",
+                                                                      "12          FRED (FEDFUNDS), derived monthly   \n",
+                                                                      "13  Federal Reserve Bank of New York (GSCPI)   \n",
+                                                                      "14                   Derived from FRED IPMAN   \n",
+                                                                      "15                   Derived from FRED IPMAN   \n",
+                                                                      "16                   Derived from FRED IPMAN   \n",
+                                                                      "17                              FRED (IPMAN)   \n",
+                                                                      "18                   Derived from FRED IPMAN   \n",
+                                                                      "19  FRED (DGS10 minus DGS2), derived monthly   \n",
+                                                                      "\n",
+                                                                      "                                          units frequency  n_obs      start  \\\n",
+                                                                      "0                                         Index        MS    955 1947-01-01   \n",
+                                                                      "1                                       Percent        MS    943 1948-01-01   \n",
+                                                                      "2                                       Percent        MS    866 1954-07-01   \n",
+                                                                      "3                             Percentage points        MS     37 2023-10-01   \n",
+                                                                      "4                                        Number        MS    717 1967-01-01   \n",
+                                                                      "5                                       Percent        MS    334 1999-01-01   \n",
+                                                                      "6                                       Percent        MS    343 1998-04-01   \n",
+                                                                      "7                                       Percent        MS    943 1948-01-01   \n",
+                                                                      "8                                         Index        MS    441 1990-01-01   \n",
+                                                                      "9                                       Percent        MS    323 1999-12-01   \n",
+                                                                      "10                                      Percent        MS    332 1999-03-01   \n",
+                                                                      "11                            Percentage points        MS    604 1976-06-01   \n",
+                                                                      "12                                      Percent        MS    866 1954-07-01   \n",
+                                                                      "13  Standard deviations from historical average        MS    348 1997-09-01   \n",
+                                                                      "14                                      Percent        MS    655 1972-02-01   \n",
+                                                                      "15                                      Percent        MS    653 1972-04-01   \n",
+                                                                      "16                                      Percent        MS    650 1972-07-01   \n",
+                                                                      "17                                        Index        MS    656 1972-01-01   \n",
+                                                                      "18         Binary event (0=no stress, 1=stress)        MS    653 1972-04-01   \n",
+                                                                      "19                            Percentage points        MS    604 1976-06-01   \n",
+                                                                      "\n",
+                                                                      "          end  \n",
+                                                                      "0  2026-08-01  \n",
+                                                                      "1  2026-08-01  \n",
+                                                                      "2  2026-08-01  \n",
+                                                                      "3  2026-10-01  \n",
+                                                                      "4  2026-09-01  \n",
+                                                                      "5  2026-10-01  \n",
+                                                                      "6  2026-10-01  \n",
+                                                                      "7  2026-08-01  \n",
+                                                                      "8  2026-09-01  \n",
+                                                                      "9  2026-10-01  \n",
+                                                                      "10 2026-10-01  \n",
+                                                                      "11 2026-09-01  \n",
+                                                                      "12 2026-08-01  \n",
+                                                                      "13 2026-08-01  \n",
+                                                                      "14 2026-08-01  \n",
+                                                                      "15 2026-08-01  \n",
+                                                                      "16 2026-08-01  \n",
+                                                                      "17 2026-08-01  \n",
+                                                                      "18 2026-08-01  \n",
+                                                                      "19 2026-09-01  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 33,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "from datetime import datetime\n",
+                                        "\n",
+                                        "import matplotlib.pyplot as plt\n",
+                                        "import numpy as np\n",
+                                        "import pandas as pd\n",
+                                        "from dotenv import load_dotenv\n",
+                                        "\n",
+                                        "from manufacturing_stress_forecasting.data import (\n",
+                                        "    IPMAN_SERIES_ID,\n",
+                                        "    STRESS_SERIES_ID,\n",
+                                        "    build_manufacturing_stress_service,\n",
+                                        ")\n",
+                                        "from manufacturing_stress_forecasting.features import (\n",
+                                        "    FEATURE_SERIES_IDS,\n",
+                                        "    IPMAN_CHANGE_3M_SERIES_ID,\n",
+                                        "    IPMAN_FEATURE_SERIES_IDS,\n",
+                                        "    MACRO_FEATURE_SERIES_IDS,\n",
+                                        "    build_feature_snapshot,\n",
+                                        ")\n",
+                                        "from manufacturing_stress_forecasting.targets import (\n",
+                                        "    DEFAULT_LOOKBACK_MONTHS,\n",
+                                        "    DEFAULT_STRESS_THRESHOLD_PCT,\n",
+                                        "    derive_manufacturing_stress_labels,\n",
+                                        ")\n",
+                                        "\n",
+                                        "\n",
+                                        "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+                                        "FULL_HISTORY_AS_OF = datetime(2100, 1, 1)\n",
+                                        "ANALYSIS_START_TS = pd.Timestamp(ANALYSIS_START)\n",
+                                        "ANALYSIS_END_TS = pd.Timestamp(ANALYSIS_END) if ANALYSIS_END else None\n",
+                                        "\n",
+                                        "service = build_manufacturing_stress_service(\n",
+                                        "    cache_dir=REPO_ROOT / \"data\" / \"fred\",\n",
+                                        "    yahoo_cache_dir=REPO_ROOT / \"data\" / \"yfinance\",\n",
+                                        "    refresh=REFRESH_INPUT_DATA,\n",
+                                        ")\n",
+                                        "service_summary = service.summary().sort_values(\"series_id\").reset_index(drop=True)\n",
+                                        "print(f\"Registered manufacturing series: {len(service_summary)}\")\n",
+                                        "service_summary"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "3fefe669",
+                              "metadata": {
+                                        "id": "3fefe669",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Variable inventory and availability\n",
+                                        "\n",
+                                        "All registered series are monthly. `timestamp` is the reference month represented by a value; `released_at` is the date when that value becomes visible to a forecast origin. The tables below audit both. GSCPI is included for exploration but is not one of the active model features."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "e1c52683",
+                              "metadata": {
+                                        "id": "e1c52683",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th>description</th>\n",
+                                                                      "      <th>source</th>\n",
+                                                                      "      <th>units</th>\n",
+                                                                      "      <th>frequency</th>\n",
+                                                                      "      <th>n_obs</th>\n",
+                                                                      "      <th>start</th>\n",
+                                                                      "      <th>end</th>\n",
+                                                                      "      <th>category</th>\n",
+                                                                      "      <th>observations</th>\n",
+                                                                      "      <th>missing_values</th>\n",
+                                                                      "      <th>missing_rate</th>\n",
+                                                                      "      <th>reference_start</th>\n",
+                                                                      "      <th>reference_end</th>\n",
+                                                                      "      <th>first_release</th>\n",
+                                                                      "      <th>last_release</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>CPIAUCSL</td>\n",
+                                                                      "      <td>Consumer Price Index for All Urban Consumers</td>\n",
+                                                                      "      <td>FRED (CPIAUCSL)</td>\n",
+                                                                      "      <td>Index</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>955</td>\n",
+                                                                      "      <td>1947-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>955</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1947-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1947-01-02</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1</th>\n",
+                                                                      "      <td>CPI_YOY</td>\n",
+                                                                      "      <td>Consumer Price Index year-over-year change</td>\n",
+                                                                      "      <td>Derived from FRED (CPIAUCSL)</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1948-01-02</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>2</th>\n",
+                                                                      "      <td>FEDFUNDS</td>\n",
+                                                                      "      <td>Effective federal funds rate</td>\n",
+                                                                      "      <td>FRED (FEDFUNDS)</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1954-07-02</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>3</th>\n",
+                                                                      "      <td>HY_SPREAD</td>\n",
+                                                                      "      <td>U.S. high-yield option-adjusted spread</td>\n",
+                                                                      "      <td>FRED (BAMLH0A0HYM2)</td>\n",
+                                                                      "      <td>Percentage points</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>37</td>\n",
+                                                                      "      <td>2023-10-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>37</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>2023-10-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>2023-11-01</td>\n",
+                                                                      "      <td>2026-10-02</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>4</th>\n",
+                                                                      "      <td>ICSA</td>\n",
+                                                                      "      <td>Initial claims for unemployment insurance</td>\n",
+                                                                      "      <td>FRED (ICSA)</td>\n",
+                                                                      "      <td>Number</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>717</td>\n",
+                                                                      "      <td>1967-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>717</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1967-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>1967-01-30</td>\n",
+                                                                      "      <td>2026-09-14</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>5</th>\n",
+                                                                      "      <td>SPY_RETURN_12M</td>\n",
+                                                                      "      <td>SPY trailing 12-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (SPY), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>334</td>\n",
+                                                                      "      <td>1999-01-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>334</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1999-01-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>1999-02-01</td>\n",
+                                                                      "      <td>2026-10-05</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>6</th>\n",
+                                                                      "      <td>SPY_RETURN_3M</td>\n",
+                                                                      "      <td>SPY trailing 3-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (SPY), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>343</td>\n",
+                                                                      "      <td>1998-04-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>343</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1998-04-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>1998-05-01</td>\n",
+                                                                      "      <td>2026-10-05</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>7</th>\n",
+                                                                      "      <td>UNRATE</td>\n",
+                                                                      "      <td>U.S. unemployment rate</td>\n",
+                                                                      "      <td>FRED (UNRATE)</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1948-01-02</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>8</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>CBOE volatility index</td>\n",
+                                                                      "      <td>FRED (VIXCLS)</td>\n",
+                                                                      "      <td>Index</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>441</td>\n",
+                                                                      "      <td>1990-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>441</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1990-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>1990-02-01</td>\n",
+                                                                      "      <td>2026-09-23</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>9</th>\n",
+                                                                      "      <td>XLI_RETURN_12M</td>\n",
+                                                                      "      <td>XLI trailing 12-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (XLI), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>323</td>\n",
+                                                                      "      <td>1999-12-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>323</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1999-12-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>2000-01-03</td>\n",
+                                                                      "      <td>2026-10-05</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>10</th>\n",
+                                                                      "      <td>XLI_RETURN_3M</td>\n",
+                                                                      "      <td>XLI trailing 3-month adjusted-close return</td>\n",
+                                                                      "      <td>Yahoo Finance (XLI), derived</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>332</td>\n",
+                                                                      "      <td>1999-03-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>332</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1999-03-01</td>\n",
+                                                                      "      <td>2026-10-01</td>\n",
+                                                                      "      <td>1999-04-01</td>\n",
+                                                                      "      <td>2026-10-05</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>11</th>\n",
+                                                                      "      <td>YC_SPREAD</td>\n",
+                                                                      "      <td>10-year minus 2-year Treasury yield spread</td>\n",
+                                                                      "      <td>FRED (DGS10 minus DGS2)</td>\n",
+                                                                      "      <td>Percentage points</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>1976-07-01</td>\n",
+                                                                      "      <td>2026-09-22</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>18</th>\n",
+                                                                      "      <td>manufacturing_stress</td>\n",
+                                                                      "      <td>Binary U.S. manufacturing stress label: 1 when...</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Binary event (0=no stress, 1=stress)</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>Binary target</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1972-05-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>13</th>\n",
+                                                                      "      <td>global_supply_chain_pressure_index</td>\n",
+                                                                      "      <td>New York Fed Global Supply Chain Pressure Index</td>\n",
+                                                                      "      <td>Federal Reserve Bank of New York (GSCPI)</td>\n",
+                                                                      "      <td>Standard deviations from historical average</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>348</td>\n",
+                                                                      "      <td>1997-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>Exploratory GSCPI challenger</td>\n",
+                                                                      "      <td>348</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1997-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1997-10-06</td>\n",
+                                                                      "      <td>2026-09-04</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>17</th>\n",
+                                                                      "      <td>ipman_us_manufacturing_production</td>\n",
+                                                                      "      <td>U.S. manufacturing industrial production index...</td>\n",
+                                                                      "      <td>FRED (IPMAN)</td>\n",
+                                                                      "      <td>Index</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>656</td>\n",
+                                                                      "      <td>1972-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>IPMAN level</td>\n",
+                                                                      "      <td>656</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1972-02-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>14</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>Trailing 1-month percentage change in IPMAN</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>655</td>\n",
+                                                                      "      <td>1972-02-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>IPMAN momentum feature</td>\n",
+                                                                      "      <td>655</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-02-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1972-03-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>15</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>Trailing 3-month percentage change in IPMAN</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>IPMAN momentum feature</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1972-05-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>16</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>Trailing 6-month percentage change in IPMAN</td>\n",
+                                                                      "      <td>Derived from FRED IPMAN</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>650</td>\n",
+                                                                      "      <td>1972-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>IPMAN momentum feature</td>\n",
+                                                                      "      <td>650</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1972-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>12</th>\n",
+                                                                      "      <td>fed_funds_rate_pct</td>\n",
+                                                                      "      <td>Month-end effective federal funds rate</td>\n",
+                                                                      "      <td>FRED (FEDFUNDS), derived monthly</td>\n",
+                                                                      "      <td>Percent</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>Legacy macro alias</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1954-07-02</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>19</th>\n",
+                                                                      "      <td>treasury_10y_minus_2y_pct_points</td>\n",
+                                                                      "      <td>Month-end 10-year minus 2-year Treasury yield ...</td>\n",
+                                                                      "      <td>FRED (DGS10 minus DGS2), derived monthly</td>\n",
+                                                                      "      <td>Percentage points</td>\n",
+                                                                      "      <td>MS</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>Legacy macro alias</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>1976-07-01</td>\n",
+                                                                      "      <td>2026-09-22</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                             series_id  \\\n",
+                                                                      "0                             CPIAUCSL   \n",
+                                                                      "1                              CPI_YOY   \n",
+                                                                      "2                             FEDFUNDS   \n",
+                                                                      "3                            HY_SPREAD   \n",
+                                                                      "4                                 ICSA   \n",
+                                                                      "5                       SPY_RETURN_12M   \n",
+                                                                      "6                        SPY_RETURN_3M   \n",
+                                                                      "7                               UNRATE   \n",
+                                                                      "8                               VIXCLS   \n",
+                                                                      "9                       XLI_RETURN_12M   \n",
+                                                                      "10                       XLI_RETURN_3M   \n",
+                                                                      "11                           YC_SPREAD   \n",
+                                                                      "18                manufacturing_stress   \n",
+                                                                      "13  global_supply_chain_pressure_index   \n",
+                                                                      "17   ipman_us_manufacturing_production   \n",
+                                                                      "14                 ipman_change_1m_pct   \n",
+                                                                      "15                 ipman_change_3m_pct   \n",
+                                                                      "16                 ipman_change_6m_pct   \n",
+                                                                      "12                  fed_funds_rate_pct   \n",
+                                                                      "19    treasury_10y_minus_2y_pct_points   \n",
+                                                                      "\n",
+                                                                      "                                          description  \\\n",
+                                                                      "0        Consumer Price Index for All Urban Consumers   \n",
+                                                                      "1          Consumer Price Index year-over-year change   \n",
+                                                                      "2                        Effective federal funds rate   \n",
+                                                                      "3              U.S. high-yield option-adjusted spread   \n",
+                                                                      "4           Initial claims for unemployment insurance   \n",
+                                                                      "5         SPY trailing 12-month adjusted-close return   \n",
+                                                                      "6          SPY trailing 3-month adjusted-close return   \n",
+                                                                      "7                              U.S. unemployment rate   \n",
+                                                                      "8                               CBOE volatility index   \n",
+                                                                      "9         XLI trailing 12-month adjusted-close return   \n",
+                                                                      "10         XLI trailing 3-month adjusted-close return   \n",
+                                                                      "11         10-year minus 2-year Treasury yield spread   \n",
+                                                                      "18  Binary U.S. manufacturing stress label: 1 when...   \n",
+                                                                      "13    New York Fed Global Supply Chain Pressure Index   \n",
+                                                                      "17  U.S. manufacturing industrial production index...   \n",
+                                                                      "14        Trailing 1-month percentage change in IPMAN   \n",
+                                                                      "15        Trailing 3-month percentage change in IPMAN   \n",
+                                                                      "16        Trailing 6-month percentage change in IPMAN   \n",
+                                                                      "12             Month-end effective federal funds rate   \n",
+                                                                      "19  Month-end 10-year minus 2-year Treasury yield ...   \n",
+                                                                      "\n",
+                                                                      "                                      source  \\\n",
+                                                                      "0                            FRED (CPIAUCSL)   \n",
+                                                                      "1               Derived from FRED (CPIAUCSL)   \n",
+                                                                      "2                            FRED (FEDFUNDS)   \n",
+                                                                      "3                        FRED (BAMLH0A0HYM2)   \n",
+                                                                      "4                                FRED (ICSA)   \n",
+                                                                      "5               Yahoo Finance (SPY), derived   \n",
+                                                                      "6               Yahoo Finance (SPY), derived   \n",
+                                                                      "7                              FRED (UNRATE)   \n",
+                                                                      "8                              FRED (VIXCLS)   \n",
+                                                                      "9               Yahoo Finance (XLI), derived   \n",
+                                                                      "10              Yahoo Finance (XLI), derived   \n",
+                                                                      "11                   FRED (DGS10 minus DGS2)   \n",
+                                                                      "18                   Derived from FRED IPMAN   \n",
+                                                                      "13  Federal Reserve Bank of New York (GSCPI)   \n",
+                                                                      "17                              FRED (IPMAN)   \n",
+                                                                      "14                   Derived from FRED IPMAN   \n",
+                                                                      "15                   Derived from FRED IPMAN   \n",
+                                                                      "16                   Derived from FRED IPMAN   \n",
+                                                                      "12          FRED (FEDFUNDS), derived monthly   \n",
+                                                                      "19  FRED (DGS10 minus DGS2), derived monthly   \n",
+                                                                      "\n",
+                                                                      "                                          units frequency  n_obs      start  \\\n",
+                                                                      "0                                         Index        MS    955 1947-01-01   \n",
+                                                                      "1                                       Percent        MS    943 1948-01-01   \n",
+                                                                      "2                                       Percent        MS    866 1954-07-01   \n",
+                                                                      "3                             Percentage points        MS     37 2023-10-01   \n",
+                                                                      "4                                        Number        MS    717 1967-01-01   \n",
+                                                                      "5                                       Percent        MS    334 1999-01-01   \n",
+                                                                      "6                                       Percent        MS    343 1998-04-01   \n",
+                                                                      "7                                       Percent        MS    943 1948-01-01   \n",
+                                                                      "8                                         Index        MS    441 1990-01-01   \n",
+                                                                      "9                                       Percent        MS    323 1999-12-01   \n",
+                                                                      "10                                      Percent        MS    332 1999-03-01   \n",
+                                                                      "11                            Percentage points        MS    604 1976-06-01   \n",
+                                                                      "18         Binary event (0=no stress, 1=stress)        MS    653 1972-04-01   \n",
+                                                                      "13  Standard deviations from historical average        MS    348 1997-09-01   \n",
+                                                                      "17                                        Index        MS    656 1972-01-01   \n",
+                                                                      "14                                      Percent        MS    655 1972-02-01   \n",
+                                                                      "15                                      Percent        MS    653 1972-04-01   \n",
+                                                                      "16                                      Percent        MS    650 1972-07-01   \n",
+                                                                      "12                                      Percent        MS    866 1954-07-01   \n",
+                                                                      "19                            Percentage points        MS    604 1976-06-01   \n",
+                                                                      "\n",
+                                                                      "          end                        category  observations  missing_values  \\\n",
+                                                                      "0  2026-08-01  Active macro or market feature           955               0   \n",
+                                                                      "1  2026-08-01  Active macro or market feature           943               0   \n",
+                                                                      "2  2026-08-01  Active macro or market feature           866               0   \n",
+                                                                      "3  2026-10-01  Active macro or market feature            37               0   \n",
+                                                                      "4  2026-09-01  Active macro or market feature           717               0   \n",
+                                                                      "5  2026-10-01  Active macro or market feature           334               0   \n",
+                                                                      "6  2026-10-01  Active macro or market feature           343               0   \n",
+                                                                      "7  2026-08-01  Active macro or market feature           943               0   \n",
+                                                                      "8  2026-09-01  Active macro or market feature           441               0   \n",
+                                                                      "9  2026-10-01  Active macro or market feature           323               0   \n",
+                                                                      "10 2026-10-01  Active macro or market feature           332               0   \n",
+                                                                      "11 2026-09-01  Active macro or market feature           604               0   \n",
+                                                                      "18 2026-08-01                   Binary target           653               0   \n",
+                                                                      "13 2026-08-01    Exploratory GSCPI challenger           348               0   \n",
+                                                                      "17 2026-08-01                     IPMAN level           656               0   \n",
+                                                                      "14 2026-08-01          IPMAN momentum feature           655               0   \n",
+                                                                      "15 2026-08-01          IPMAN momentum feature           653               0   \n",
+                                                                      "16 2026-08-01          IPMAN momentum feature           650               0   \n",
+                                                                      "12 2026-08-01              Legacy macro alias           866               0   \n",
+                                                                      "19 2026-09-01              Legacy macro alias           604               0   \n",
+                                                                      "\n",
+                                                                      "    missing_rate reference_start reference_end first_release last_release  \n",
+                                                                      "0            0.0      1947-01-01    2026-08-01    1947-01-02   2026-08-03  \n",
+                                                                      "1            0.0      1948-01-01    2026-08-01    1948-01-02   2026-08-03  \n",
+                                                                      "2            0.0      1954-07-01    2026-08-01    1954-07-02   2026-08-03  \n",
+                                                                      "3            0.0      2023-10-01    2026-10-01    2023-11-01   2026-10-02  \n",
+                                                                      "4            0.0      1967-01-01    2026-09-01    1967-01-30   2026-09-14  \n",
+                                                                      "5            0.0      1999-01-01    2026-10-01    1999-02-01   2026-10-05  \n",
+                                                                      "6            0.0      1998-04-01    2026-10-01    1998-05-01   2026-10-05  \n",
+                                                                      "7            0.0      1948-01-01    2026-08-01    1948-01-02   2026-08-03  \n",
+                                                                      "8            0.0      1990-01-01    2026-09-01    1990-02-01   2026-09-23  \n",
+                                                                      "9            0.0      1999-12-01    2026-10-01    2000-01-03   2026-10-05  \n",
+                                                                      "10           0.0      1999-03-01    2026-10-01    1999-04-01   2026-10-05  \n",
+                                                                      "11           0.0      1976-06-01    2026-09-01    1976-07-01   2026-09-22  \n",
+                                                                      "18           0.0      1972-04-01    2026-08-01    1972-05-01   2026-09-01  \n",
+                                                                      "13           0.0      1997-09-01    2026-08-01    1997-10-06   2026-09-04  \n",
+                                                                      "17           0.0      1972-01-01    2026-08-01    1972-02-01   2026-09-01  \n",
+                                                                      "14           0.0      1972-02-01    2026-08-01    1972-03-01   2026-09-01  \n",
+                                                                      "15           0.0      1972-04-01    2026-08-01    1972-05-01   2026-09-01  \n",
+                                                                      "16           0.0      1972-07-01    2026-08-01    1972-08-01   2026-09-01  \n",
+                                                                      "12           0.0      1954-07-01    2026-08-01    1954-07-02   2026-08-03  \n",
+                                                                      "19           0.0      1976-06-01    2026-09-01    1976-07-01   2026-09-22  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 34,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "CATEGORY_BY_SERIES = {\n",
+                                        "    IPMAN_SERIES_ID: \"IPMAN level\",\n",
+                                        "    **{series_id: \"IPMAN momentum feature\" for series_id in IPMAN_FEATURE_SERIES_IDS},\n",
+                                        "    **{series_id: \"Active macro or market feature\" for series_id in MACRO_FEATURE_SERIES_IDS},\n",
+                                        "    STRESS_SERIES_ID: \"Binary target\",\n",
+                                        "}\n",
+                                        "\n",
+                                        "\n",
+                                        "def full_series(series_id: str) -> pd.DataFrame:\n",
+                                        "    frame = service.get_series(series_id, as_of=FULL_HISTORY_AS_OF).copy()\n",
+                                        "    frame[\"timestamp\"] = pd.to_datetime(frame[\"timestamp\"])\n",
+                                        "    frame[\"released_at\"] = pd.to_datetime(frame[\"released_at\"])\n",
+                                        "    return frame.sort_values(\"timestamp\").reset_index(drop=True)\n",
+                                        "\n",
+                                        "\n",
+                                        "series_frames = {series_id: full_series(series_id) for series_id in service_summary[\"series_id\"]}\n",
+                                        "coverage_rows = []\n",
+                                        "for series_id, frame in series_frames.items():\n",
+                                        "    coverage_rows.append(\n",
+                                        "        {\n",
+                                        "            \"series_id\": series_id,\n",
+                                        "            \"category\": CATEGORY_BY_SERIES.get(series_id, \"Registered macro or market input\"),\n",
+                                        "            \"observations\": len(frame),\n",
+                                        "            \"missing_values\": int(frame[\"value\"].isna().sum()),\n",
+                                        "            \"missing_rate\": frame[\"value\"].isna().mean(),\n",
+                                        "            \"reference_start\": frame[\"timestamp\"].min(),\n",
+                                        "            \"reference_end\": frame[\"timestamp\"].max(),\n",
+                                        "            \"first_release\": frame[\"released_at\"].min(),\n",
+                                        "            \"last_release\": frame[\"released_at\"].max(),\n",
+                                        "        }\n",
+                                        "    )\n",
+                                        "\n",
+                                        "coverage = pd.DataFrame(coverage_rows).sort_values([\"category\", \"series_id\"])\n",
+                                        "variable_dictionary = (\n",
+                                        "    service_summary.assign(\n",
+                                        "        category=service_summary[\"series_id\"].map(\n",
+                                        "            lambda series_id: CATEGORY_BY_SERIES.get(series_id, \"Registered macro or market input\")\n",
+                                        "        )\n",
+                                        "    )\n",
+                                        "    .merge(coverage, on=[\"series_id\", \"category\"], how=\"left\")\n",
+                                        "    .sort_values([\"category\", \"series_id\"])\n",
+                                        ")\n",
+                                        "variable_dictionary"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 26,
+                              "id": "86aeb5b8",
+                              "metadata": {
+                                        "id": "86aeb5b8",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th>category</th>\n",
+                                                                      "      <th>observations</th>\n",
+                                                                      "      <th>missing_values</th>\n",
+                                                                      "      <th>missing_rate</th>\n",
+                                                                      "      <th>reference_start</th>\n",
+                                                                      "      <th>reference_end</th>\n",
+                                                                      "      <th>median_release_delay_days</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>CPIAUCSL</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>955</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1947-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1</th>\n",
+                                                                      "      <td>CPI_YOY</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>2</th>\n",
+                                                                      "      <td>FEDFUNDS</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>3</th>\n",
+                                                                      "      <td>HY_SPREAD</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>37</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>2023-09-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>4</th>\n",
+                                                                      "      <td>ICSA</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>717</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1967-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>28.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>5</th>\n",
+                                                                      "      <td>SPY_RETURN_12M</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>333</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1999-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>6</th>\n",
+                                                                      "      <td>SPY_RETURN_3M</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>342</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1998-04-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>7</th>\n",
+                                                                      "      <td>UNRATE</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>943</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1948-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>8</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>441</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1990-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>9</th>\n",
+                                                                      "      <td>XLI_RETURN_12M</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>322</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1999-12-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>10</th>\n",
+                                                                      "      <td>XLI_RETURN_3M</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>331</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1999-03-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>11</th>\n",
+                                                                      "      <td>YC_SPREAD</td>\n",
+                                                                      "      <td>Active macro or market feature</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>18</th>\n",
+                                                                      "      <td>manufacturing_stress</td>\n",
+                                                                      "      <td>Binary target</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>13</th>\n",
+                                                                      "      <td>global_supply_chain_pressure_index</td>\n",
+                                                                      "      <td>Exploratory GSCPI challenger</td>\n",
+                                                                      "      <td>348</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1997-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>35.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>17</th>\n",
+                                                                      "      <td>ipman_us_manufacturing_production</td>\n",
+                                                                      "      <td>IPMAN level</td>\n",
+                                                                      "      <td>656</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>14</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>IPMAN momentum feature</td>\n",
+                                                                      "      <td>655</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-02-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>15</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>IPMAN momentum feature</td>\n",
+                                                                      "      <td>653</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-04-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>16</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>IPMAN momentum feature</td>\n",
+                                                                      "      <td>650</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1972-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>12</th>\n",
+                                                                      "      <td>fed_funds_rate_pct</td>\n",
+                                                                      "      <td>Legacy macro alias</td>\n",
+                                                                      "      <td>866</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1954-07-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>1.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>19</th>\n",
+                                                                      "      <td>treasury_10y_minus_2y_pct_points</td>\n",
+                                                                      "      <td>Legacy macro alias</td>\n",
+                                                                      "      <td>604</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>1976-06-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>31.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                             series_id                        category  \\\n",
+                                                                      "0                             CPIAUCSL  Active macro or market feature   \n",
+                                                                      "1                              CPI_YOY  Active macro or market feature   \n",
+                                                                      "2                             FEDFUNDS  Active macro or market feature   \n",
+                                                                      "3                            HY_SPREAD  Active macro or market feature   \n",
+                                                                      "4                                 ICSA  Active macro or market feature   \n",
+                                                                      "5                       SPY_RETURN_12M  Active macro or market feature   \n",
+                                                                      "6                        SPY_RETURN_3M  Active macro or market feature   \n",
+                                                                      "7                               UNRATE  Active macro or market feature   \n",
+                                                                      "8                               VIXCLS  Active macro or market feature   \n",
+                                                                      "9                       XLI_RETURN_12M  Active macro or market feature   \n",
+                                                                      "10                       XLI_RETURN_3M  Active macro or market feature   \n",
+                                                                      "11                           YC_SPREAD  Active macro or market feature   \n",
+                                                                      "18                manufacturing_stress                   Binary target   \n",
+                                                                      "13  global_supply_chain_pressure_index    Exploratory GSCPI challenger   \n",
+                                                                      "17   ipman_us_manufacturing_production                     IPMAN level   \n",
+                                                                      "14                 ipman_change_1m_pct          IPMAN momentum feature   \n",
+                                                                      "15                 ipman_change_3m_pct          IPMAN momentum feature   \n",
+                                                                      "16                 ipman_change_6m_pct          IPMAN momentum feature   \n",
+                                                                      "12                  fed_funds_rate_pct              Legacy macro alias   \n",
+                                                                      "19    treasury_10y_minus_2y_pct_points              Legacy macro alias   \n",
+                                                                      "\n",
+                                                                      "    observations  missing_values  missing_rate reference_start reference_end  \\\n",
+                                                                      "0            955               0           0.0      1947-01-01    2026-08-01   \n",
+                                                                      "1            943               0           0.0      1948-01-01    2026-08-01   \n",
+                                                                      "2            866               0           0.0      1954-07-01    2026-08-01   \n",
+                                                                      "3             37               0           0.0      2023-09-01    2026-09-01   \n",
+                                                                      "4            717               0           0.0      1967-01-01    2026-09-01   \n",
+                                                                      "5            333               0           0.0      1999-01-01    2026-09-01   \n",
+                                                                      "6            342               0           0.0      1998-04-01    2026-09-01   \n",
+                                                                      "7            943               0           0.0      1948-01-01    2026-08-01   \n",
+                                                                      "8            441               0           0.0      1990-01-01    2026-09-01   \n",
+                                                                      "9            322               0           0.0      1999-12-01    2026-09-01   \n",
+                                                                      "10           331               0           0.0      1999-03-01    2026-09-01   \n",
+                                                                      "11           604               0           0.0      1976-06-01    2026-09-01   \n",
+                                                                      "18           653               0           0.0      1972-04-01    2026-08-01   \n",
+                                                                      "13           348               0           0.0      1997-09-01    2026-08-01   \n",
+                                                                      "17           656               0           0.0      1972-01-01    2026-08-01   \n",
+                                                                      "14           655               0           0.0      1972-02-01    2026-08-01   \n",
+                                                                      "15           653               0           0.0      1972-04-01    2026-08-01   \n",
+                                                                      "16           650               0           0.0      1972-07-01    2026-08-01   \n",
+                                                                      "12           866               0           0.0      1954-07-01    2026-08-01   \n",
+                                                                      "19           604               0           0.0      1976-06-01    2026-09-01   \n",
+                                                                      "\n",
+                                                                      "    median_release_delay_days  \n",
+                                                                      "0                         1.0  \n",
+                                                                      "1                         1.0  \n",
+                                                                      "2                         1.0  \n",
+                                                                      "3                        31.0  \n",
+                                                                      "4                        28.0  \n",
+                                                                      "5                        31.0  \n",
+                                                                      "6                        31.0  \n",
+                                                                      "7                         1.0  \n",
+                                                                      "8                        31.0  \n",
+                                                                      "9                        31.0  \n",
+                                                                      "10                       31.0  \n",
+                                                                      "11                       31.0  \n",
+                                                                      "18                       31.0  \n",
+                                                                      "13                       35.0  \n",
+                                                                      "17                       31.0  \n",
+                                                                      "14                       31.0  \n",
+                                                                      "15                       31.0  \n",
+                                                                      "16                       31.0  \n",
+                                                                      "12                        1.0  \n",
+                                                                      "19                       31.0  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 26,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "release_gap = coverage.assign(\n",
+                                        "    median_release_delay_days=[\n",
+                                        "        (frame[\"released_at\"] - frame[\"timestamp\"]).dt.days.median()\n",
+                                        "        for frame in (series_frames[series_id] for series_id in coverage[\"series_id\"])\n",
+                                        "    ]\n",
+                                        ")[\n",
+                                        "    [\n",
+                                        "        \"series_id\",\n",
+                                        "        \"category\",\n",
+                                        "        \"observations\",\n",
+                                        "        \"missing_values\",\n",
+                                        "        \"missing_rate\",\n",
+                                        "        \"reference_start\",\n",
+                                        "        \"reference_end\",\n",
+                                        "        \"median_release_delay_days\",\n",
+                                        "    ]\n",
+                                        "]\n",
+                                        "release_gap"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "50cab4a1",
+                              "metadata": {
+                                        "id": "50cab4a1",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## IPMAN level and manufacturing momentum\n",
+                                        "\n",
+                                        "The target is derived from the trailing three-month IPMAN change, so inspect the level and momentum features before considering any correlation or model result."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 27,
+                              "id": "6844dd75",
+                              "metadata": {
+                                        "id": "6844dd75",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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+                                                            "text/plain": [
+                                                                      "<Figure size 1400x800 with 2 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        }
+                              ],
+                              "source": [
+                                        "\n",
+                                        "def in_analysis_window(frame: pd.DataFrame) -> pd.DataFrame:\n",
+                                        "    mask = frame[\"timestamp\"] >= ANALYSIS_START_TS\n",
+                                        "    if ANALYSIS_END_TS is not None:\n",
+                                        "        mask &= frame[\"timestamp\"] <= ANALYSIS_END_TS\n",
+                                        "    return frame.loc[mask].copy()\n",
+                                        "\n",
+                                        "\n",
+                                        "ipman = in_analysis_window(series_frames[IPMAN_SERIES_ID])\n",
+                                        "ipman_momentum = {series_id: in_analysis_window(series_frames[series_id]) for series_id in IPMAN_FEATURE_SERIES_IDS}\n",
+                                        "\n",
+                                        "fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True, layout=\"constrained\")\n",
+                                        "axes[0].plot(ipman[\"timestamp\"], ipman[\"value\"], color=\"#1f4e79\", linewidth=1.8)\n",
+                                        "axes[0].set(title=\"U.S. manufacturing industrial production (IPMAN)\", ylabel=\"Index\")\n",
+                                        "axes[0].grid(alpha=0.25)\n",
+                                        "for series_id, frame in ipman_momentum.items():\n",
+                                        "    axes[1].plot(frame[\"timestamp\"], frame[\"value\"], linewidth=1.4, label=series_id)\n",
+                                        "axes[1].axhline(0, color=\"black\", linewidth=0.8)\n",
+                                        "axes[1].axhline(DEFAULT_STRESS_THRESHOLD_PCT, color=\"#b22222\", linestyle=\"--\", linewidth=1.0, label=\"stress threshold\")\n",
+                                        "axes[1].set(title=\"Trailing IPMAN percentage changes\", xlabel=\"Reference month\", ylabel=\"Percent\")\n",
+                                        "axes[1].legend(ncol=2)\n",
+                                        "axes[1].grid(alpha=0.25)\n",
+                                        "plt.show()"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "dfcea780",
+                              "metadata": {
+                                        "id": "dfcea780",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Target construction and event frequency\n",
+                                        "\n",
+                                        "A stress month has a trailing three-month IPMAN change at or below $-2\\%$. The target is known only once the current IPMAN observation is released, and the forecasting task predicts the target three months ahead."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 7,
+                              "id": "b921baea",
+                              "metadata": {
+                                        "id": "b921baea",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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+                                                            "text/plain": [
+                                                                      "<Figure size 1400x450 with 1 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        },
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>stress_months</th>\n",
+                                                                      "      <th>observed_months</th>\n",
+                                                                      "      <th>stress_base_rate</th>\n",
+                                                                      "      <th>lookback_months</th>\n",
+                                                                      "      <th>threshold_pct</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>0.0625</td>\n",
+                                                                      "      <td>3</td>\n",
+                                                                      "      <td>-2.0</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "   stress_months  observed_months  stress_base_rate  lookback_months  \\\n",
+                                                                      "0             20              320            0.0625                3   \n",
+                                                                      "\n",
+                                                                      "   threshold_pct  \n",
+                                                                      "0           -2.0  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 7,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "reconstructed_target = derive_manufacturing_stress_labels(\n",
+                                        "    series_frames[IPMAN_SERIES_ID],\n",
+                                        "    lookback_months=DEFAULT_LOOKBACK_MONTHS,\n",
+                                        "    threshold_pct=DEFAULT_STRESS_THRESHOLD_PCT,\n",
+                                        ").rename(columns={\"value\": \"reconstructed_target\"})\n",
+                                        "registered_target = series_frames[STRESS_SERIES_ID].rename(columns={\"value\": \"registered_target\"})\n",
+                                        "target_check = reconstructed_target.merge(\n",
+                                        "    registered_target[[\"timestamp\", \"registered_target\"]], on=\"timestamp\", how=\"inner\"\n",
+                                        ")\n",
+                                        "if not target_check[\"reconstructed_target\"].equals(target_check[\"registered_target\"]):\n",
+                                        "    raise AssertionError(\"The registered target does not match the IPMAN-derived target.\")\n",
+                                        "\n",
+                                        "target = in_analysis_window(registered_target).dropna(subset=[\"registered_target\"]).copy()\n",
+                                        "ipman_change_3m = in_analysis_window(series_frames[IPMAN_CHANGE_3M_SERIES_ID])\n",
+                                        "stress_months = target.loc[target[\"registered_target\"] == 1, \"timestamp\"]\n",
+                                        "\n",
+                                        "fig, ax = plt.subplots(figsize=(14, 4.5), layout=\"constrained\")\n",
+                                        "ax.plot(ipman_change_3m[\"timestamp\"], ipman_change_3m[\"value\"], color=\"#1f4e79\", label=\"IPMAN 3-month change\")\n",
+                                        "ax.axhline(DEFAULT_STRESS_THRESHOLD_PCT, color=\"#b22222\", linestyle=\"--\", label=\"stress threshold\")\n",
+                                        "for timestamp in stress_months:\n",
+                                        "    ax.axvspan(timestamp, timestamp + pd.offsets.MonthEnd(1), color=\"#b22222\", alpha=0.18)\n",
+                                        "ax.set(title=\"Stress target over the IPMAN three-month change\", xlabel=\"Reference month\", ylabel=\"Percent\")\n",
+                                        "ax.legend()\n",
+                                        "ax.grid(alpha=0.25)\n",
+                                        "plt.show()\n",
+                                        "\n",
+                                        "target_summary = pd.DataFrame(\n",
+                                        "    {\n",
+                                        "        \"stress_months\": [int(target[\"registered_target\"].sum())],\n",
+                                        "        \"observed_months\": [len(target)],\n",
+                                        "        \"stress_base_rate\": [target[\"registered_target\"].mean()],\n",
+                                        "        \"lookback_months\": [DEFAULT_LOOKBACK_MONTHS],\n",
+                                        "        \"threshold_pct\": [DEFAULT_STRESS_THRESHOLD_PCT],\n",
+                                        "    }\n",
+                                        ")\n",
+                                        "target_summary"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 8,
+                              "id": "626aaeb5",
+                              "metadata": {
+                                        "id": "626aaeb5",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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",
+                                                            "text/plain": [
+                                                                      "<Figure size 1400x400 with 1 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        },
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>year</th>\n",
+                                                                      "      <th>stress_months</th>\n",
+                                                                      "      <th>observed_months</th>\n",
+                                                                      "      <th>stress_rate</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>2000</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1</th>\n",
+                                                                      "      <td>2001</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>2</th>\n",
+                                                                      "      <td>2002</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>3</th>\n",
+                                                                      "      <td>2003</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>4</th>\n",
+                                                                      "      <td>2004</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>5</th>\n",
+                                                                      "      <td>2005</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>6</th>\n",
+                                                                      "      <td>2006</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>7</th>\n",
+                                                                      "      <td>2007</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>8</th>\n",
+                                                                      "      <td>2008</td>\n",
+                                                                      "      <td>8.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.666667</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>9</th>\n",
+                                                                      "      <td>2009</td>\n",
+                                                                      "      <td>6.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.500000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>10</th>\n",
+                                                                      "      <td>2010</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>11</th>\n",
+                                                                      "      <td>2011</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>12</th>\n",
+                                                                      "      <td>2012</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>13</th>\n",
+                                                                      "      <td>2013</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>14</th>\n",
+                                                                      "      <td>2014</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>15</th>\n",
+                                                                      "      <td>2015</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>16</th>\n",
+                                                                      "      <td>2016</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>17</th>\n",
+                                                                      "      <td>2017</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>18</th>\n",
+                                                                      "      <td>2018</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>19</th>\n",
+                                                                      "      <td>2019</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>20</th>\n",
+                                                                      "      <td>2020</td>\n",
+                                                                      "      <td>4.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.333333</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>21</th>\n",
+                                                                      "      <td>2021</td>\n",
+                                                                      "      <td>1.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.083333</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>22</th>\n",
+                                                                      "      <td>2022</td>\n",
+                                                                      "      <td>1.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.083333</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>23</th>\n",
+                                                                      "      <td>2023</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>24</th>\n",
+                                                                      "      <td>2024</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>25</th>\n",
+                                                                      "      <td>2025</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>12</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>26</th>\n",
+                                                                      "      <td>2026</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>8</td>\n",
+                                                                      "      <td>0.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "    year  stress_months  observed_months  stress_rate\n",
+                                                                      "0   2000            0.0               12     0.000000\n",
+                                                                      "1   2001            0.0               12     0.000000\n",
+                                                                      "2   2002            0.0               12     0.000000\n",
+                                                                      "3   2003            0.0               12     0.000000\n",
+                                                                      "4   2004            0.0               12     0.000000\n",
+                                                                      "5   2005            0.0               12     0.000000\n",
+                                                                      "6   2006            0.0               12     0.000000\n",
+                                                                      "7   2007            0.0               12     0.000000\n",
+                                                                      "8   2008            8.0               12     0.666667\n",
+                                                                      "9   2009            6.0               12     0.500000\n",
+                                                                      "10  2010            0.0               12     0.000000\n",
+                                                                      "11  2011            0.0               12     0.000000\n",
+                                                                      "12  2012            0.0               12     0.000000\n",
+                                                                      "13  2013            0.0               12     0.000000\n",
+                                                                      "14  2014            0.0               12     0.000000\n",
+                                                                      "15  2015            0.0               12     0.000000\n",
+                                                                      "16  2016            0.0               12     0.000000\n",
+                                                                      "17  2017            0.0               12     0.000000\n",
+                                                                      "18  2018            0.0               12     0.000000\n",
+                                                                      "19  2019            0.0               12     0.000000\n",
+                                                                      "20  2020            4.0               12     0.333333\n",
+                                                                      "21  2021            1.0               12     0.083333\n",
+                                                                      "22  2022            1.0               12     0.083333\n",
+                                                                      "23  2023            0.0               12     0.000000\n",
+                                                                      "24  2024            0.0               12     0.000000\n",
+                                                                      "25  2025            0.0               12     0.000000\n",
+                                                                      "26  2026            0.0                8     0.000000"
+                                                            ]
+                                                  },
+                                                  "execution_count": 8,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "annual_target_rate = (\n",
+                                        "    target.assign(year=target[\"timestamp\"].dt.year)\n",
+                                        "    .groupby(\"year\", as_index=False)[\"registered_target\"]\n",
+                                        "    .agg(stress_months=\"sum\", observed_months=\"count\", stress_rate=\"mean\")\n",
+                                        ")\n",
+                                        "\n",
+                                        "fig, ax = plt.subplots(figsize=(14, 4), layout=\"constrained\")\n",
+                                        "ax.bar(annual_target_rate[\"year\"], annual_target_rate[\"stress_rate\"], color=\"#b22222\")\n",
+                                        "ax.set(title=\"Annual manufacturing-stress event rate\", xlabel=\"Year\", ylabel=\"Share of observed months\")\n",
+                                        "ax.set_ylim(bottom=0)\n",
+                                        "ax.grid(axis=\"y\", alpha=0.25)\n",
+                                        "plt.show()\n",
+                                        "annual_target_rate"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "a1e31076",
+                              "metadata": {
+                                        "id": "a1e31076",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Macro, risk, market, and supply-chain trends\n",
+                                        "\n",
+                                        "The source data have different units, so each feature receives its own axis. Daily/weekly source series have already been reduced to monthly observations; SPY and XLI are trailing monthly-origin returns."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "06a20c78",
+                              "metadata": {
+                                        "id": "06a20c78",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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ffEIvvPACPfjgg5STkyNqBd9333304osvmnrRAAAAwESOJGeL644kB/i4u5CPmzNdzCkywJKBpUEwGAAAAMAIEjML6Mftx2j1gXNUVVtHXq5O9Potl9OU+K5ofyvj4eFBH374obgAAAAAsMMXssR1fFRQhxokwt+TTqfnU0ODgmxt25dZDNYFwWAAAAAAPcotqaB959LFMD/m6epEG44k0a4zaeqO+u1j+tKsod3JDdnAAAAAAMD1gi9kk4OdLfWKCOhQe3Afk8uP5ZSUU7C3O9oUWoRgMAAAAIAelFXV0BfrDtKyHcepura+2f3D48Lo9rH9aEzPSGRrAAAAAIBaVU2dmPytT5cAcrS361DLRPh5ievUvBIEg6FVCAYDAAAA6KEDf9+X/4iMDp4M7tYxfalrkI+4L7+sknpHBFC3EF+0MwAAAAA0cyI1l+oaGmhAVMcnE+bMYCkYPCQ2FK0MLUIwGAAAAKAT6hsUtGDpZhEInjG4G71y07gOZ3QAAAAAgPU5pKoX3JHJ4yQRfqpgcH6x3pYLLBOCwQAAAAAdVFvfQK+t2kebT6bSiLgwBIIBAAAAoN0SVMHg/tEdmzyuaWYwQGtsW70XAAAAALQqr66lJ7/fKALBg2JC6KO7piAjGAAAAADaRaFQUEJytgjm+nu4drj1AjxdydnBnlIQDIY2IDMYAAAAoJ2Op+TQ0z9uoou5xTQoOpA+mzuV3Jwd0Y4AAAAA0C7cnywqrxKTDHeGjY0Nhft5UGo+MoOhdQgGAwAAAOigtr6e9pxNp593nKBtJy6SjQ3R3ePj6ZZhMeTq5IA2BAAAAIB2O6wqERHfiRIRkgh/L0rMKqTiimrycnXCtwFaIRgMAAAA0MqwvQPnM2n1wXO08egFkbXBhnQNoXnThtCgmGAqKChA+wEAAABAh3CJCBbficnjmk0il1dMXpGB+EZAKwSDAQAAALRoaFDQe3/voe+2HBH/B3m50e1j+9L0Qd2or6pz3dDQgLYDAAAAgE5lBrs5OVBssE+nW1FzErk+CAZDCxAMBgAAAGiivqGBXvxlG63ad0Z0zF+8fgwNiA4mW1sbtBUAAAAA6EVWURmdzyqky3pGkJ2trd6CwWmoGwytQDAYAAAAoElpiLdX7RKB4PioIFo8dxp5uzmjjQAAAABAr7YcTxbXl/eJ0svrqctEIBgMrUAwGAAAAEClpq6evt5wiH7afpziQv3oi/uuIA8XTL4BAAAAAPq3+Zh+g8HB3u7iOquoXC+vB5YJwWAAAACwehwE5kxgDgRnFJZRiI87fYlAMAAAAAAYSFlVDe1LzKA+kQEU6OWml9d0drQnX3dnyiws08vrgWVCMBgAAACsVl5pBf21/ywt+++46DQ7O9jTHeP60d0TBpCfh4upFw8AAAAALNSOUylUV99A4/WUFSwJ8fGgCzlFovSZjQ3mu4DmEAwGAAAAq8Od46Xbj9M7f+0WnXAXR3uac3l/unN8f/L3cDX14gEAAACAhdt2IkWvJSIkPMLtRGoulVTWkJcryp1Bcx2eqvDzzz+nfv36kaenp7iMGDGC/v33X/X9VVVVNG/ePPLz8yN3d3e69tprKTs7u9FrpKSk0PTp08nV1ZUCAwPpqaeeorq6uo4uEgAAAECrOPC791w6Pf7dBlq0cqeYGO7F6y+jLQtvpydnjkAgGAAAAACM4lxWAbk6OVC3EF+9vm6wt7LkRGZhqV5fFyxHhzODw8PD6c0336Ru3bqJ7Jrvv/+eZs6cSYcPH6bevXvT448/Tv/88w8tX76cvLy86KGHHqJrrrmGdu7cKZ5fX18vAsHBwcG0a9cuyszMpNmzZ5ODgwO98cYb+vyMAAAAAJSaV0KPLVlHp9PzRWsMjAmm9++YRAF6qtEGAAAAAKCr9PwSCvfz1HspBy4TwbgEWo8wf3whoL9g8IwZMxr9//rrr4ts4T179ohA8TfffEPLli2j8ePHi/uXLFlCPXv2FPcPHz6c1q9fTydPnqSNGzdSUFAQxcfH06uvvkrPPPMMvfzyy+To6NjRRQMAAABoZH9iBj3yzVoxXO6qwXE0a1h3Gtw1hOxsOzxICgAAAACgQ4orqkW/dHBXZeBW32UiGCaRA4PWDOYsX84ALi8vF+UiDh48SLW1tTRx4kT1Y3r06EGRkZG0e/duEQzm6759+4pAsGTKlCn0wAMP0IkTJ2jAgAFa36u6ulpcJCUlJeK6oaFBXIyB34ezoY31ftYC7Yp2lROsr2hXubHmdTY5p4ge/mYtVdfW0+s3j6OrhsSp7+tse1hzuxoC2hEAAACsJSuYhfkZIBjsjWAwGDAYfOzYMRH85frAXBd45cqV1KtXL0pISBCZvd7e3o0ez4HfrKws8TdfawaCpful+1qyaNEiWrhwYbPbCwsLjVZvmA9USktLxcGfLTKK0K5mDusr2lVOsL6ibfW2LikUtO98FuWXVdEvu89SaWUNPT9rKI3u6k8FBQX6ex/0CfSK+1cAAAAAli6tQNnnCff1NFhmcFZRmd5fGyxDp4LB3bt3F4Hf4uJiWrFiBd1xxx20bds2MqQFCxbQ/PnzG2UGR0REkI+Pj5jIzhj4wI9ruvB7IhiMdjV3WF/RrnKC9RVtqw88e/JrK3bQ8dRc9W13j4+nG8fE672Bsc7ql729XgatAQAAAFhtZrCfhyvZ29miTAS0qFM9bs7+jY2NFX8PGjSI9u/fTx999BHdeOONVFNTQ0VFRY2yg7Ozs8WEcYyv9+3b1+j1+H7pvpY4OTmJS1MclDVmYJaDwcZ+T2uAdkW7ygnWV7Sr3FjDOsuB4Ns//pNq6xtEbeBRPcLJ39ONhnUL1fvkHNbUrsaCNgQAAABrkKoKBkf46T+p0dbWhoK93RAMhhbZ6js7huv5cmDYwcGBNm3apL7vzJkzlJKSIspKML7mMhM5OTnqx2zYsEFk93KpCQAAAID2TsTx+JL1VNfQQIvnTqNFt42nKwfH0fC4MIMFggEAAAAA2is9X1kmItRX/5nBUt3gnOJyqqvHvBagx8xgLtcwbdo0MSkc13dbtmwZbd26ldatW0deXl509913i3IOvr6+IsD78MMPiwAwTx7HJk+eLIK+t99+O7399tuiTvDzzz9P8+bN05r5CwAAANCS6to6evL7DZReUErzpg6mcb27oLEAAAAAwCxxn9XP3YVcnRwM8vohPh7UoMiknJJyCvUxTMAZrDAYzBm9s2fPpszMTBH87devnwgET5o0Sdz/wQcfiKF+1157rcgWnjJlCn322Wfq59vZ2dHq1avpgQceEEFiNzc3UXP4lVde0c8nAwAAAIvHk7nmlVTQs0s3056z6XR5nyi6b/JAUy8WAAAAAIBWDQ0KEQzuEeZnsBYKVk0il1lQhmAw6C8Y/M0337R6v7OzMy1evFhcWtKlSxdas2ZNRxcBAAAArFBGQSnd9MEfVF5VS1z9obKmTtw+uX8MvXX7BLJD7V4AAAAAMFO5JeVUU1dP4QaoFyzpFuIrrl9b8R99cd8VFOStDA4DMEzZDAAAALKycu9pyi+tpK7BPuTsYE9hvh7Ur0sg3T62n5g5GQAAAADAXKUVKOsFh/sZrnzD1PiudPRiNv247Rjd9tEqWvXMDeTm7Giw9wN5QTAYAAAAZFUWYvXBc6K+2q/zryEXR8PUWQMAAAAAMOTkcWG+hssMtrW1oWdmjRQTyP284wTtS8wQ5dQAxPqBZgAAAAC5OJaSQyl5JTSxbzQCwQAAAAAgO2n5JQbPDGY2NjaijBo7lJRl0PcCeUEwGAAAAGRj9YFz4vrKwd1MvSgAAAAAAO2WXVwuroONUMe3b5dAsre1pUMXMg3+XiAfCAYDAACALPBEG2sPnyc/Dxca1i3M1IsD0Cnp6el02223kZ+fH7m4uFDfvn3pwIEDaFUAAAALl1dSIa4DPF0N/l5cUq1XhD8dT8mlKtWkywAIBgMAAIAs/HsokfLLKumqIXGYKA5krbCwkEaNGkUODg7077//0smTJ+m9994jHx8fUy8aAAAAGFheaQW5ONqLOTCMYWBMiKgdfCI11yjvB+YPE8gBAACALCaO+3ZzgggC3zamr6kXB6BT3nrrLYqIiKAlS5aob4uOjkarAgAAWIG8kkry93QVNX2NYWB0MH235QgdSsqkQV1DjPKeYN4QDAYAAACzt/1kCiVmFdKsod2NUl8NwJD++usvmjJlCl1//fW0bds2CgsLowcffJDmzp3b4nOqq6vFRVJSopx8pqGhQVyMgd+HT8wY6/2sBdoV7SonWF/RrnJjbussLwtnBveNDDDaMvWPChTXB5My6W49vae5tavcNRi5HREMBgAAALN2Oj2P3l61S/w9Z3x/Uy8OQKclJSXR559/TvPnz6fnnnuO9u/fT4888gg5OjrSHXfcofU5ixYtooULF2otOVFXV2e0A5XS0lJx8Gdri2pzaFfzhvUV7SonWF+tp22LK6pFyQZPJ3sqKCgw2vtG+nnQ4QtZlJ+fr5eMZHNrV7krLS016vshGAwAAABma82hRFqwdLPoNM+dOIBig31NvUgAejmAGjx4ML3xxhvi/wEDBtDx48fpiy++aDEYvGDBAhE81swM5lITXGfY09PTaMvNB5D8njjwQ7uaO6yvaFc5wfpqPW1bUKMMAIf4eZGvr/H6tVFBPpRyMoVc3D31UqvY3NpV7uztjRueRTAYAAAAzBJnGnz8zz5ysLOlz++dRiO7R5h6kQD0IiQkhHr16tXotp49e9Lvv//e4nOcnJzEpSk+ADPmQRgf+Bn7Pa0B2hXtKidYX9GucmNO62xBWZW4DvRyM+ryeLs5i+uSqhpyd2nen5B7u8qdrZHbEN8YAAAAmKVDSVmUml9CU+O7IhAMFmXUqFF05syZRredPXuWunTpYrJlAgAAAMPLLakQ1zyBnDH5qILBxeWX5h8A64VgMAAAAJilP/crg2Uzh3Y39aIA6NXjjz9Oe/bsEWUiEhMTadmyZfTVV1/RvHnz0NIAAAAWLE8KBnsYNxgsZQYXllca9X3BPCEYDAAAAGansqaW1h4+T2G+HjQoJsTUiwOgV0OGDKGVK1fSzz//TH369KFXX32VPvzwQ7r11lvR0gAAABYsr1QZjA0wcmawt6syGFyEzGBAzWAAAAAwR8v+O07l1bU0e1w/srXt/IzHAObmyiuvFBcAAACwHrkl5SYpE+HtLgWDlTWLwbohMxgAAADMyvmsQvr03wMiY+L2sf1MvTgAAAAAAHqRV1JJNjZEPqrgrLF4uyonjUMwGBiCwQAAAGA26uob6Pmft1BNXT29fONY8lJ1XAEAAAAA5C6vtEJM5uZgZ2eSmsEIBgNDMBgAAADMxkf/7KOjF3PoqiFxNK53F1MvDgAAWIFjKTlUXFFt6sUAACuZQM7f083o76sOBlegTAQgGAwAAABmYvOxC/Tt5gSKCfSm568dberFAQAAK5BbUkG3friSnvlxo6kXBQAsHI984xNP/h4uRn/vS5nBOPEFCAYDAACAGcgpLqf/W7aFXBzt6YM5k8nN2dHUiwQAAFZSp76+QUH/nUqlMxn5pl4cALDwEhGmmDyOOdrbkauTA8pEgIAyEQAAAGBSCoWCXln+H5VU1tCzV4+i2BBffCMAAGAUF3OL1H9/t/kIWh0ADFoigvEkyabAk8ihZjAwBIMBAADApP49fJ62HE+mEXFhdO3wHvg2AADAaC7kKIPBHs6OtOZQImUWlqH1AaDTGhoUIuFBU1J2kWmDwW7OCAaDgGAwAAAAmExFdS29vWqXKA/x8o1jycbGBt8GAAAYzcXcYnH98PShVNfQQH/uP4PWB4BOe2X5dhr5f9/RzzuOU31Dg7jt150nyNbGhi7vE2WyYHB5da2oXQzWDcFgAAAAMJnvthwRk/fcNT6ewv088U0AAIBRJecUiSy9Kwd1Iz4feSAxA98AAHQ6K3j9kSQqqaim11bsoPu++If2J2bQ0Ys5NL5vlMn6vNIkcsXlVSZ5f7CAYPCiRYtoyJAh5OHhQYGBgTRr1iw6c+bSWdTk5GSR3aPtsnz5cvXjtN3/yy+/dP6TAQAAgNlPovHt5gQxicYdl/c39eIAAICV4ey49IJSigrwIi9XJ4oL8aOE5GyqrUfWHAB0rvxMcUU1je8TReN6d6HdZ9Pp/i/XiPtuH9vXZE0rBYOLKqpNtgwg82Dwtm3baN68ebRnzx7asGED1dbW0uTJk6m8vFzcHxERQZmZmY0uCxcuJHd3d5o2bVqj11qyZEmjx3FgGQAAACzbV+sPUWVNHT00bTC5OTmYenEAAMDKpOWXUH2DgroEeov/B3cNEfulk6l5pl40AJCxwxeyxPWY3pH0wZzJNLJ7OFXV1lGPMD8aFBNisuXykYLByAy2evYdbYG1a9c2+v+7774TGcIHDx6kMWPGkJ2dHQUHBzd6zMqVK+mGG24QAWFN3t7ezR4LAAAAlp0VvGLPKYrw86Srh2LSOAAAML5kVb1gzgxmg2NDael/x8Vw7v5RQfhKAKBTweAB0cHkaG9HH901hb5cf4imxMeYdH4MLwSDobPB4KaKi5U7Ul9fX633c5A4ISGBFi9e3Ow+zjC+5557KCYmhu6//36aM2dOiz+Q6upqcZGUlJSI64aGBnExBn4fnhXSWO9nLdCuaFc5wfqKdpUbc1tnv99yhKpr6+mu8f3J1ka5fHJkbu0qd2hHADCmizlF4jpKlRksZewdTMqke2gAvgwA6JCEC1nk6eJIMYE+4n9XJwd6fMYwk7emt6uTuC5EZrDVs9dXx/2xxx6jUaNGUZ8+fbQ+5ptvvqGePXvSyJEjG93+yiuv0Pjx48nV1ZXWr19PDz74IJWVldEjjzzSYq1iLjfRVGFhIdXV1RnlC+XPW1paKg7+bG0xBx/a1bxhfUW7ygnWV+to25LKGvplxwkK8HCh0V39qaCggOTKnNrVEnBbAgAYPzNYGQz283ChmCAfOpSURfUNDWSH7ToAtFNBWaXYtozpFUm2nPFgRjCBHOg1GMyZvcePH6cdO3Zovb+yspKWLVtGL7zwQrP7NG8bMGCAqDn8zjvvtBgMXrBgAc2fP79RZjDXJ/bx8SFPT0+jHfhx5jK/Jw780K7mDusr2lVOsL5afttyjbIF32+jipo6eviKIRQcGEByZi7tains7fU2aA0AQKfMYDtbGwrz81DfxnWDf9t1ks6k51OvCHnvowDANFnBUokIcyPVDN50LFmUsnj0ymHUPdTP1IsFJtDpHvdDDz1Eq1evpu3bt1N4eLjWx6xYsYIqKipo9uzZbb7esGHD6NVXXxWlIJyclCnsmvg2bbfzAZgxD8L4wM/Y72kN0K5oVznB+op2lRtTr7PHU3LouWVb6HxWIV03oifdOqavRexHTd2ulgRtCADGlJJXQqE+HqKmpyQuVFn2MDW/BMFgAGi3I8k54jreDOuO+7i7iOtjKcpl7B7mj2CwlepwMJiHQz788MNiUritW7dSdHR0i4/lEhFXXXUVBQS0fWaV6wpzdo22gC8AAADIT119A721ahf9vOM4KRREc8b3pydmDDfpBBoAAGDduAwET2baNHvPy1WZOVdccWmeGgAAXaUXKOe1ig5S1gs2JyE+7vTMrJFUVlVDi9ceoNziclMvEsgtGMylIbj0w59//kkeHh6UlaVMhffy8iIXF+XZBpaYmCiyhtesWdPsNf7++2/Kzs6m4cOHk7OzM23YsIHeeOMNevLJJzu6WAAAAGBGauvr6dkfN9PahPMUF+JL/3fdaBrcNdTUiwUAAFauoKyK6hsUFODp1uh2L9UESwgGA0BHZBeXk72tLfmpsnDNzexx/ai6tk4Eg3lZwTp1OBj8+eefi+tx48Y1un3JkiV05513qv//9ttvRfmIyZMnN3sNBwcHWrx4MT3++OMi0zg2Npbef/99mjt3bkcXCwAAAMzIq8v/E4HgIbGh9NncaWI2ZQAAMF+HkjIpKtCbfM00kKEvUkZcoJdro9s9VcHgEmQGA0AHZBeVU4CXq9lNHqfJycFeTCaXW1Jh6kUBOZaJ0AVn+vJFm6lTp4oLAAAAWOYEGr/vOU29wv3p83unkYsjAsEAAOYsv7SS7vz0L5o+sBstum08WbIcVRAk0KulzOAqkywXAMgXx8lyisuptwwmnwz0dEVmsBXDTCcAAACgdw0NClq0cqf4+/nrLkMgGABABjiIwaUTDiZlkqXLKSrXGgz2dFFlBlfWmGS5AEC+CsurqLa+gYK8G29XzFGAl5soh8MlI8D6IBgMAAAAevfn/jN0PCWXZgzuRv3NcDZlAABorrC8UlynF5SKLGFLllOivUyEh4sj8fymyAwGgI6UiFBuV9xkkRnMUCrCOiEYDAAAAHqVnl9Cb67cRW5ODvT4lcPQugAAMlFYdqk0wtGL2WTpWdCs6QRydra25OHsiJrBANBu0oRsQXIIBquylzGJnHVCMBgAAAD0pq6+gZ75aTOVVdXQC9dfRkHe7mhdAABZBoNzyJJJ2XABquw4TV6uzmL4NABAR8rPyKH/G6g6ESZNpgnWBcFgAAAA0JtP/91Phy9k0fRBsTRjcBxaFgBAZvUuJcdTciw+M5gzgF2dmk9u6unqhMxgAGi3rOIy+WQGq0rk5BQrT4yBdUEwGAAAAPRi49EL9PXGwxQV4EUvXj8GrQoAINOawc4O9nTsYo6YDNRS5RZXiAmUtPFydaLy6lqqra83+nIBgOVNTGmOpO2fVD8drAuCwQAAANBpiZkF9NzSzSLD6qO7p5C7syNaFQBApmUihsWFUWlVDSXnFpFc5JVW0OJ/91NlTW2bj62pq6f8sspmk8dpBoNZaWWN3pcTACyXVH+3pW2LOZGyl/nEGFgfBIMBAACg0wfgD3y1RmRRvXHL5RQb7IsWBQCQaZkIWxsbGt0jQvx/PCWX5GLV3jP02bqDtHLvGZ32W5o1M5vydFEGg4vLUTcYANoXDPZxcyYnB3uzbzZfdxexvccEctYJwWAAAADosNPpeXT34r8po7CMHrtyGE3qH4PWBACQqaLyKpEVG+nvJf6XU5Ags6hMXbKI/d+yLbTg151aS11ImXAtDeX2clMFgysu1VAGANClTESQt/mXiGD2drbk5+Fi9RPIncssoJOp8jnxqS/mf7oCAAAAzE5BWSV9t+UIfb/1KNXVN9Ccy/vTPRPiTb1YAADQycxgzmrjAAErKFXWEJaDbFWtzgPnM2jHqVT668A58f/qg+do1rAezSaPa20ot5QZXFKJzGAA0A2PkOPyOnKYPE7CJ8Qu5MinHJC+KRQKeuh//1JVbT1te2U2WRMEgwEAAKBd/thzmt74YwdV1tRRiI87vXrTOBrRPRytCAAg84PiorIqivT3JF93Z/WJv/ZacyiR/D1caGi3MDKmLFVmcH2Dgp75aZP4287Whj76Zx9Niu9Kbk4O6sfmlCgzg1ueQE75+YsrEAwGAMubPE7Cy3oiNZfKq2rIzQrn+0jJK6G0/FL1/o5LZ1gLlIkAAAAAndQ3NNBnaw/QC79sFTPNv3DdZfTPczchEAzQSW+++SbZ2NjQY489hrYEk+HJ0uoaGsTBsI/qgLi9wWCeTPSpHzbS3Z+tpt/3nCJjyi4qI0/VxG9c7qJfZCDNHt1TBH6/3ZTQ6LG5UmawZwuZwarXKUEwGAB03QaptitB3u6yaTNpG5hjpZPI7T2Xrv47KbuQrAmCwQAAANBqphh3jn7YepSmv/ELLV57gCL8PWnpY1fTTaN7y2KCDABztn//fvryyy+pX79+pl4UsHJcIoJ5uzmTo70debo4Un47y0Qs23FcXPPzX/xlmyjRYAzVtXVUUFZFfSICKC7UT9x21/j+dNOIOBHs+HHb0UZZvlLQpsWawapgMDKDAUBX0sSUAS2cZDJH/lIwuEQ+9eH1ae9ZjWBwlnWVy0AwGAAAAJrhCXfWJZynq978jWYs+pXeWrVL1Fi8cWQvWvro1dQlQDm5EAB0XFlZGd1666309ddfk4+PD5oSTIqzaRnXDGacIVxY3jgYzDXia+vrtT6/tLKa/tp/lsJ8PWj5k9eKESQf/7OvxcfrkxTcDfZ2pyevGk53T4iny/tEiWW44/J+opbn0u3H1J9hf2IGuTjaqwMhLWUGIxgMALribSDzcJFPuQWpLEJhB0oCWcKxzr7EdLK3VYZFz1tZZjDSeQAAACz84P6fQ4m0PuE8peUVk62tLb1w/WU0pleXVofaPvPTZnGwzB2kKwbG0oi4cLq8Txf10GEA6Lx58+bR9OnTaeLEifTaa6+1+tjq6mpxkZSUlIjrhoYGcTEGfh8eLWCs97MW5tKu+aqsNs6K5WXhIEHaxRKqr68XZUzY7R+vEpOrfX7vtGbPX7n3tKglf9OoXhQV4EXXjehBP20/TqsPnKOZQ+IMuuyZBcqaj0FerjQiLkxcpHa9Zmh3+t/GBPpp+zG6fUwf2n4qhTILy+i2y/qQva2N1nb3cFbWFy6uqDL592JuzGV9tTRoV/m3balq9AHXJ5fL78PbTXnii0eBtHeZ5b7OnsnIFyNKpsTH0LqEJDES0pSfxdjvjWAwAACABaqqqaMlW47QN5sOi4NznkQnwMNFdPYe/mYdvTt7Ik3qH9PseTwL+2PfrhfDhSf2i6YnrxohykIAgH798ssvdOjQIVEmQheLFi2ihQsXNru9sLCQ6urqjHagUlpaKg7++MQSWFa7pmXni2tHqqeCggJyd7KjugYFpWRki0y32voGOnYxh2xtbCg9K0dk1mpaseskOdrb0ri4IPH8WfFd6NedJ+nLdQdoZLSf2A8ZSlJ6jrh2d7AR763Zrh4KBV03NJa+3nKcHl+yjnJLKokX5cr+EerHNlVfo/xN5RWXtfgYa2Uu66ulQbvKv21zi5QnaRU1VbLZbtg31IrrjNyCdi+z3NfZrUcSxfWQLv6UkJRF5zPb3wb6xG1pTAgGAwAAWBDukK09fJ7e+3uPyHziumUPXzGEpg+MJaqppIvFNfTAV//Skz9spN+fvI5iQ3zVzz2UlEn3f7lGDKF96YYxdP2InupsMADQn9TUVHr00Udpw4YN5OysHJLflgULFtD8+fMbZQZHRESI8hKensY5YcMHfrxN4PeU44GfuTKXdq2lVHEdHuRHvr6+FOzL61U6NTg4k6+vN6XkFpOCA6UKBaWX1tHQboHq53I5hfM5xSIjNyosWNzm60t09bDu9NuuU3Q2r4JG9Ygw2LKX1qWI65jQQLHsTdt17pShdDKjiHaeSRP3TY2Pod4xEa3uSx3sbKmytkH9emBe66ulQbvKv23rVFVYQ4P8xTZTDrrUKPv5VQ027d7WyX2dPZ9XJq7H9IulLaczxP7B2c2DXJ2UI0OMzd7euOFZBIMBAAAsxInUXHpz5U46lJQlJu+5d9JAumfiAPVwtYKCShoQHUzvzJ5ID379L7302zb68eFZZGtrQ0eSs0WQmDO/Pr5rCo3t3XIZCQDonIMHD1JOTg4NHDhQfRsPxd++fTt9+umnohyEnZ1do+c4OTmJS1N8AGbMgzA+8DP2e1oDc2jXwgplzWBfD1exHH4eynq6ReXV4v+MIuWBMztyMZuGdw9X/38sRZmZOzAmpNFniI8KFsHgvNJKg342rmnPQn09Gr2P1K5uzvb02b1X0Aer94pJ7e6dPKjN5eG6wSWVNVjXzXR9tURoV3m3bVmVMsvW09VZNr8NP8/G23lrWmdT80vIycGOQnw8KDrIRwSDL+aVUO+IAJMsj7HbEMFgAAAAmeLJHk6l59HxlFzadvKiCOgqFCRqX82fMZzC/bRnC3Kgl+sArzmUKA6Me4b704u/bKXaugZ6546JCAQDGNiECRPo2DHlZFaSOXPmUI8ePeiZZ55pFggGMIaisuYTyLF81cRC6aq6vCwhObvRcw9fyBLXfMJRk5RhxeWKDCm7SBkMDvJ2a/Ex9na29NTMEWKCOV1GvXi5OouawdamrKqGPl93UNSO5pPKAKD7b4d5OMtnAjn+nfPmsKDJZKHWIDWvhCL8PEVSTNcg5SS+XDfYVMFgY0MwGAAAwMxVVNfSrjNpVFJRTfWKBnJ3cqTtJ1NEMLdONdkA12IcGhtGD04dRIO7hrb5ms9ePUq85rebE8T/nEn80d1TaBwyggEMzsPDg/r06dPoNjc3N/Lz82t2u7naeTqVjl7MoQemDGp0e3l1rRiNAPLDteKZNFGor7syKFxQqgoG5yuDwVwzmIPBPBM7H0Szw0lZ4vZ+XS6VjmAujg7q/ZghZRWVicCzuw5BGF3LH3m6OlJqXrEoGWEtJZP4pPL87zaI9uTv84aRvchbdXIAAFpXUlktystwtqlc2NnakrerMxWqTgZai+KKanHh0SwsJthbHQy2FggGAwAAmLFTaXn05PcbKDm3uNl9cSG+YhK47qF+NDg2VJzd15WfhwutePI6+u9kCl3MK6ZJ/aIpvklGFwBAS75cf4gOJmWKYBFvT1hiZgFd/fZyeuKq4XTn5f11ajwesr/u8HlRvsa5yYRkYFxF5VXixKCr6nuQMoMLVJnBaapg8Iju4eJkQFJOIcUG+1JtfT0dS8ml7mF+5NYkGOvqZG+0zOBgb3e9Bm05M5hLJ/Gym6qGpDHxfAELlm6mvJIKio8KEgF/PmnMI4kAoG1llTVisk25nTziE4DSdl7aFsz/bj052NnRDaN60dDYUNl9prak5imPqzgzmMWoMoPPZignkDuXWUCvLv9PjCTpFxVElgg9LgAAADP1x57T9Mry7eJg9LYxfahvZKDojJVW1lCEvyeN7B7eqc5ZiI+76OQBgOlt3bqV5CQ5t0hcZxaWqoPBIltUoaCP1+yj8X2jKNLfq83X+XrDIUrMKqS/D56l60dge2RKHAzgEhHSfsXXo3GZiIyCUhEsntgvWgSDEy5ki2Awn7Ssqq1rViKCSUFUQ2YG19TVi2XkYLQ+uaqymq0lGPzv4US6mFtMN43uTTeO7CVO7PAJYwSDAXQvE6HL6ARzw6NALuYWqUd7cCB007Fkcd/ahPO06NbxdNWQOLIkKXkl4joywFN98pOTbHacSqXsojL6fN0BccKb68ZbKvlVeQYAALBwpZXV9Mpv2+mFX7aKTuVX90+nBdeMpisHx9H0Qd3EgRrPym5pZ+kBQB74hFS+qnRAlqpWK+NAEquurafXV+wQw+vbqtfHgWD2/Zaj4kAUOobb+r2/9tDGoxc6/PyCsiryUZWGYH5SZrDqu04rKKFQH3caGKMM+h48n9lqveBGZSJqag1fL9ir5XrBHSEN9a6uM2xWszmob2gQ2f5cV/meCQOoW4gvBXu70Y7TKfhdArRj3+jhovsoPXPBpWDqGxSizIU0ITXjkT9s+8mLZGlS1ZnBl05azx7XT5Tfe23FDlqXkET9uwTRqB6XJkq1NMgMBgAAMDLOkPp9zylate+MGBYd4OlGOUXlVF5dIzLpuGYfZznFhfrRp3dPobAWJoIDADCFFNVBlJQZ3PT2+Ogg2nE6lfYnZtDQbmEtvs7WE8rMI39PV7qQUyQmwry8T5T6/pzictpzNp1mDO6Gk186zIrONeCjArxE5m5H6gVzVpvmxKNcJoHrxnLGcFVNnTgB0CPMX0y0E+brQZuPJ1N1bR2tT0gSExANUgWJtU8gZ8BgcHGZuA72cTdMMLimnizdLztOiN8gB3941BC7rGckLd99ik6k5YqRSQDQ+gkVrpkvz8xg5Yk/rhvMgeGTqmDwjaN60fojSXQyLY8sTWqTzGDGCTcf/rNP7NvYg1MHW3Tfo8OZwYsWLaIhQ4aICTACAwNp1qxZdObMmUaPGTdunGg8zcv999/f6DEpKSk0ffp0cnV1Fa/z1FNPUZ0VnH0FAADrwllXh5Iy6eVft9H4l36kN1fuEgdex1NyaYPoaOVSTkmF6IBU1tbR/BnD6Nf51yAQDABmJzlHMxisDMSxlNxiMYv6/CuHq+sBt2bL8YsiiPjO7RPE/99vPdro/o//2SdqmHL5CWiddPDO9eWTc5QlPNqD90csOlA5iQ7j4cKcKcyZwekFyqA/B4H5mG76oFgRPP7k3/3i+xnfJ4qCvJsHY6X6wxXVhju+y1Ktg0Hees4Mtre3+Mxg7pt8t+UIvfHHTlEi5N5JA9X3cTCYcbAfAFpXXqU84cU1g+VGGhFSUK4cBcIngLgkUNdgH+oZ5idG/ZRX1VhcmQg7Wxv1yS/Gn/mW0b3F35aeFdypzOBt27bRvHnzRECYg7fPPfccTZ48mU6ePClmQ5bMnTuXXnnlFfX/HPSV1NfXi0BwcHAw7dq1izIzM2n27Nnk4OBAb7zxRmc+FwAAgElxBtW5zHw6n1UoLrvPpqnrU3HH487x/emmUb1Fp5Fns+WZfPnAu7Cskhwd7MnNCuoTAoA8cW1BSVaRMhDHJR54G8cHj1wugCfz4hNdz193majpyjUIOdjEE9V4ujiK4bRcZoAPuDh7mK8PJ2WJych40hp2QFWGgB+nrQQBXHJcFQxmW09cpDs1gro6faeqAH+XgMZ1nrlURHZxOaUXlKiDwVIG1VcbDtOSzUfUw2u1kSYFNGRmsFSqhNc5g2QG11pmZvC+c+n04ep9dORitmi7/z1wZaPAyPDu4eI3yxnnXD7i4WlDRD8FAJrjk2NMlpnBbpcyg3kfzJOo8eTUvC/uGR5Au8+m0+mMfBoUE9Lia3AdeZ4v4O3bJzbbj5ij1PxiCvXxUPc3JLdc1kdMlsrXlpwV3Klg8Nq1axv9/91334nM3oMHD9KYMWMaBX852KvN+vXrRfB448aNFBQURPHx8fTqq6/SM888Qy+//DI5OsrvhwQAANaN62x9vfEwbTyaRJrlMl0c7WnmkDiaObQ7Deka2uiAShqexThQAgBgzjj7tGlmcE5JuZhEjA8CefvGk05xEIlL4nAt0tySCvVz7G1tydXJXtTmG6cqCxET5C2CUhkFZeI1OMjMpQ/YIVVNWmh938MlHRSkoK3Hk+nOy/u38ztVBvijmgSReRK5s5kF6nrQ4X7KYDBPHNcjzI9Op+dTr3D/FoMEdra25Oxgb9AJ5DhYbZhgsCozuNbyMoNzi8vp3i/+ERPUTomPoadnjWzWfnxS+odHZtJD/1tLX204RAGeriJAAgDNlaqCwbLODC6rpMTMQnECt1eEv7itZ7jy+lRqXqvBYJ70mkc7PvPjJvrx0ZnNgqzmhE9O5hRXiIm4m+Kaz6/ePI6sgd5qBhcXKzsIvr6+jW5funQp/fTTTyIgPGPGDHrhhRfU2cG7d++mvn37ikCwZMqUKfTAAw/QiRMnaMCAAc3ep7q6WlwkJSXKTmJDQ4O4GAO/Dw+pMdb7WQu0K9pVTrC+ol0lPNkC17TkLDeuq7XrTJq4vU9EAF3WM4Jign1EfUWu4+hgL3WMFEafKAnrLNpVDtC3kgcODPIJLg4OScFgLhHBpIwgKRjME7Ewnoncyd5O1Kbl7CMeBeHv6SbKDbBI1fO47jC/hpQVLE1QJs1yDs1x2/CBOgfU3V0cRfCcR5x4uTafyCgtv4T8PVzVGbua3ynjfZUm6WSlNKFQqCozmF09tActWrmT5oyPbzWDigP/hgwGq8tEGGoCOQvMDD6cnC0CwY9cMYTumzyoxcfFBPnQt/Nm0ISXf6LdZ9IQDAZoZfJnxqWS5FszuFKUrWO9IwLEdc8wVTA4veW6wRwb238+Q/x9LCWHvlp/iOZNG0LmijN/WYS/dc/JYq+vjvtjjz1Go0aNoj59Lp0tvOWWW6hLly4UGhpKR48eFRm/XFf4jz/+EPdnZWU1CgQz6X++r6VaxQsXLmx2e2FhodFqDfPnLS0tFSu9rW2Hyy4D2tUosL6iXeVETusrnzX/dc9Z+mXPWSpT1Qljg6ID6baRPWhAVECjg+PSkkuZdKYgp7aVE7SrfvE6CuaNtyEcOOSALU8wti8xXWwPL6omj+NJMBlnjXIgKSm7kK4f0ZNeumFMqwFDKYgsgso9laUhpKykU2l5dD67kLqFNE46gUvDXTkr7fKIKNHmCReyacepFFHKQdPxlBy65cOVdNuYviITtGnNYA4eNx2dIgUJNh1TTqijOcEcZ4kOjAmmXqqgQUt4ErnKmjqDZgbze+g7I0+dGVxnecHgo6o63IO7hrb5WM4YDvVxp6MpOeL3DwDNcVII4xNy8s0MrlKPtOgVHqDeN/P2lffDmpO7Mn8PF/VoIS6Px7Xjz2bm05cbDtGoHhEUb6blnVKb9FeslV6CwVw7+Pjx47Rjh/LMv+Tee+9V/80ZwCEhITRhwgQ6f/48de3atUPvtWDBApo/f36jzOCIiAjy8fEhT09Pox34cWeW3xMH1GhXc4f1Fe0qJ3JZX7kT9MQPG8SBka+7Mz00rb+ooxgb7CNmWjdHcmlbuUG76pe9asImMF8F5dVixnSeaIyDZRwb4m2ilFkqBXV5e/PMrJGiXvqj04e2WXtPOijjzGDGmcFce5Frq7/06zYxASeCwdqdSFUepPcOD6D+0crEmqMXcxoFgzmI9/aq3VTfoBDlODTVNzSIILx08K9pWFwYrdx7WmSRcuCXa8hKOFO7rUAwc3F0oDyNMiH6xiVFgr3c9F7fkTPZLbVMBE/6x+VapKHgbenXJYjWJpynTFVbA4D2msEezs1HZMgmM7i8klJyS8REarEhPurtPNcPPnYxR5z4ZTd/8IeoI756wY3if2kkz9jeXWjO+P50xyd/0bNLN9PvT15HbmaYKS2VoIrQOLlpjTrd437ooYdo9erVtH37dgoPb322vWHDhonrxMREEQzm0hH79u1r9JjsbGXnpKU6w05OTuLSFB/YGvPgljsbxn5Pa4B2RbvKCdZX62tXrrG3fPcp+mXHCcovq6RZQ7vTc9eOls1kb+bctnKGdtUfrJvmL1U1vLJLgDdJsTcuFdG0TAQb3TNCXHQRqRqueVGVYcQZxWN6RdKQWGWNwkNJWXTjKOUs39CYVMKhd2SAOCnJ30tiVkGjx2w8eoEOJikP2M9nForgsBQ85e+Pg71dAptnSXGm17637u5Uk7s6Gi4zmIMTvL7EGSBr3NlCy0Rwm/FQ8O5hfiJQr4u+XQJFMJgDQsH9og2+jAByI+fMYG/VST6eOI4nveYJWzVr/vIIHS7XdCQ5W5ywlSbt5DJ5PQPd1MFg3l9z3+CeiQNEnfF3/9ojRgWZG56boGnZI2vU4aNB7kBwIHjlypW0efNmio5ue6eQkJAgrjlDmI0YMYKOHTtGOTk56sds2LBBZPj26tWro4sGAACg10kGeGjUG7/voMmvLqPFaw+IAykOAr928zjZBIIBAPQhJU8ZDI4K9FJPOMWZmSl5JeTp6qQ+qGwvzh7y93QVweA9Z5W113myGs4Y9nN3UQcyrTnrjGd5b23yuB6q4B5nO53LuBQM5ue9//cekcnVr0ugKCkhDQVmyTna6wXri4uTvZhckDOQ9S3HQJPHMUd1mQjLygw+m5EvAty8LuhKeiwHgwCgtcxg+QWDOROYl/tcZgE1KBQ0c2hco/sn948R1wt/205LNh8R+xu2at8ZERfkYDDPISCN8Hlw6iCxP/lz/xmD1ovvqMxCZT8mxEf/+w2rCAZzaQieGG7ZsmXk4eEhavzypbKyUtzPpSBeffVVOnjwICUnJ9Nff/1Fs2fPpjFjxlC/fv3EYyZPniyCvrfffjsdOXKE1q1bR88//7x4bW3ZvwAAAMbCnZuP1+yjoc98S9e9u4KW/necgrzd6OUbx9DmhbfTrZf10fuQVAAAc3c2q1Bcc0kc6UAqvaBUOfFbJ+vv8fM5Y4cPMNnEftFiO9svKkhkr/JM5+aAs2w//Xe/0eqn8mRwk19Zqp6Mr6nEzAKRkS1leXI5DR69whmz7O/950Sw/qZRvcQwXuVzlN8jS84pEtdRgd4GWX7ODGZVBsgOzlZlqAUZIBhsqZnBRy4qE7H6RzWeu6c1XEKEy0pw+REAsKzMYOatqhvMk8NOjW9c0nVIbCjdNT5e1Jbny9XDuovRGFxLfv2xFHFSjuuPS8dFnFU8bWCs2HbuPJ1K5ob7E65ODlonWbUmHQ4Gf/7551RcXEzjxo0Tmb7S5ddffxX3Ozo60saNG0XAt0ePHvTEE0/QtddeS3///bf6Nezs7ESJCb7mLOHbbrtNBIxfeeUV/Xw6AACADs7MvuiPnfTl+kOiFjBPkvPW7RNo9YKb6PoRvUQHAgDAGp3JLBQHi1wzWAoG/7nvjDjo6xqsrDHYUVwqoq6hgXadSaP+XYLUwcm4UF/1EFZz8NHqvfT5uoPqoLWhbT2eLALCfx/gCUuVAQcJ128uLK9qNCt6txA/cc1ZXjyS5Yv1B8nZwZ7umTiQugUr21KzjERyrmGDwZwZzAyRIcY1bBmfrNU3R1UNc0urGSxNHteeYLCzo734HXJ5CS4pAgCWkxnMfN2UdYOnDYjVWuf3kelDxAgBO1sbumNcf5o1rLvYFrzx136RWXzDyJ6NHj+hr7JywKZjF8jcZBSWiUkxrT2pp8M1g9s6E86Tum3btq3N1+nSpQutWbOmo4sBAACgV3mlFfR/S7fQjtOpYsKE/z14pXpiBQAAa8ZBsfM5xeKAkEsOSEPzOeuUA8T3ThrYqdfXrDd81ZBLw1R5W8zOZebT8LgwMqWi8ipKUmXSvr1qF43uEUEBBp5Qa8NR5cE0B9zXJyTRNcN7qO9LV02EwycuJVLwnDOGOWObM7fvvLy/GMYrTQrE90mkyf+kus2GygyuqNF/MDhbFQwOMUBmsJMqM7jKwjKDj17MFhMBtnfyJK4bfDItj5b9d5ym9zXt7xDAXDODPVzkmW3q56E81tHcv2jibN+vH7hSZNXyiV8fd2da/O8BcndyoI/vmUp9IhuXneGyRbxf2nbioihVpFmD2FD4fbiEhV0rc6PwSUnej/eNbHvyU0uHKZsBAMDi1dU3iNnTd5xKFX9z0IIPaPgAOdzXQ2T65pdWiCG1PMMsT3TDQ2kX3Tre6ocQAQBIODO3vkFBvcOVB1HSMEvOWl1wzahGwdyOiFQ9nwPNUwdcGqbKZQ+k9zc1nkCL8cEwT7Tz9cbDooa8oXDmLw+z5SwmzoL9a/+ZRgfraQWlzYLBsars37OZ+XQ4KUtkbfEQXxbu5ymCnOc0MoN5sj7O8tZ1MrH2kkbTVFQbskyE/gPynE3NOLvaUpRWVouTN6N6RLQ7K+7a4T3p30OJYlKotYf86OsHZ5Cna8dqhANYmrKqanHtIdMyEXMnDaCh3UIpvpURA+7Ojur9MSfK/PXsDVRXVU6hQf7NHsvbl/F9o+jHbcdof2IGjeyu22Sy5VU1tP1kCk2Oj2k1qNsUT2b3+HfrqbauXpSx6h3hT2G+nuKEaKCXm/pkNQezWaiPdU8exxAMBgAAi3YmI5+e/G6DOpOraaeGJ1KRcFCDD6h5xvqbR/e2+uFDAACaTqTliuteEZcyam4b01cELK8Zpj2bqD26BimzVsf17tJoIjqelIYDmBzcNLUE1RD7J2YMpwe//lfUTzSkHadSRDDy6mE9xCR6fMCbll8igrosI18VDNbI8uSgvIOdLa09fF58N7OGdldnffHBNbczB7K5JBJPksozw3Nw0FBcDJgZzJMXGqpmMAfRGU9+ZymkEyqctddevSMC6O/nbqLXVvxHG45coBd/2UYfzJmMvhKAKjOYtxnSdkNu+nUJEpf24CBrQb0yCK4N1/3nYDDXu79hZC/RX+CTva1VH3j6x0209cRFeqlqjHiOLv47lUKPfrtO/M0lrPik7eELWY0ew2VxhnULUweDQ6x88jiGYDAAAFgkPgD+ZcdxWrz2gBhay4EKPsvs5eIkZlKPC/UTQ2Z5qBBnAvOBslw7cAAAxnAiVRkM7qMxvPLBqYP19vqxIb707uyJNLhrSKPb+eBRCmDWNzS0K1tI3xKSs4gTKgfGBJOvu7OYOMeQOOjGJvWPoVBfDxEM5gnsuOwDSytQlongUS6a7RUT5CNOhrKbRvdu9JqcOcyjYzIKS8U+kMUEGaZeMHNV1QyuNEBmMAeyebSPpwGy8aQJ5GosqEzE6fQ8cc2Zcx3h7+FKb982ge4q+lOUL1my5Yg66xzAmvGxhVzrBRvKgOhgunZ4D/pz/1l658/dYt/J9YZb8vOOEyIQzL7bckQ8t639PY92ePL7jWRDNrR47lQaHhcuSlqdzSygjIJSUTbisSXr6ZM1+2noI6Fiv8dCNPaZ1sp0PSkAAAA946HKP+84Tk//uFHMvP7+33tFRtLn906jV28eR5f1jBSz0nMGFAeCGWef8dlhBIIBAFp3PCWXXB3tqYt/58pBtIZnINdWg5eHpvKJO86KNRUORB+9mCMC01wXMsDTzeDB4L3n0ijcz4Nig31ohKpesmbGU7qWzGDNusEcuO/bpJajVDeYD5aTspWZzRw8lmtmMNeuNsREQI6qMhHVdZaTGXwqTRkM7tnBYLB0suHla4aLftRnaw9YVBkN0L9959Jp5d7Tbc45JXdllTXkLtMSEYbCgdxXbhpH6164RfzP5fpaciG7SASM/dxdxMlPrmW/5bgyMNya3/ecFpP3zZs2WASCmZODvdjvTYnvKl6LM5R5v8nzwWRKwWAfZAYjMxgAAGSPJ5D5Ydsx+m3XSfVs5f6ervTIFUPo5tF9yNNVnpM5AACYC962cuCwX6Q/2doafwZuHs0hDXPvEmC4LNbWJGYVinaIjw4W/wd6uYrsWy61YIh6u5zxVFBWJYbucrCTSyEEe7vRkeRsEVjh27hmMGejcZmjZkP6D5wT+8Cm+D6WcCFLZE1plugwbM1g3YLBF3OLRBBBKoXREg5Ccq1/qYaloTKDeXSRofD6w4GyR6cPNVjNZk2nM/JFJnVkQOcmC/R2c6Ip8TH00/bjdCwlhwbFNM7mB/PDfWSudW6M74pH5+05k0a/7zlF206miNv49zwkNpQsOTOYjz2gOT5hxxPBHkrKEtttbQk47/29R9z30g1jxHq68WgSLdmcIAK5LeF5YH7afkxs064b3rPFxz00bQhtOnZBZAdzGQkWiprBCAYDAIC8cAfzeEoOebo4iYysNYcSaW3CedEh4IPkeVMH04S+0SKTyhCZQgAA1oiHlzcoFNRDlVVqbFKm67nMApHpYwpHLijrBUsT7HC9RJZbUiHqGutbap4yC1rztbnu4bqEJDGZXIi3O6Xnl1CEf/PAHtdajAr0ptFaagHHRwWLg3EuOSFlR0UbtEyEMsjJQfO2cJD77s9Wi3rG6168pdUZ6AvKKsV1oKf+J4+TsssYDzk2lBW7T9Gy/45TuK8n3Ta2LxkSB1r499MnIkAvpVa4/iYHg/eeTUcw2MzxiIqFv20XIwV+nX+twd/r6reXq0/+8HvyqJJfd56w2GAwjxrhzyvXyeOMgbcXfPKL6/kOalIKirPHtxxPFo/hSef4+G1sry6iZARnCLc0OS0HeLkG8K2XtZ74wycMpw2IFceMXOffztZGnMy1dsgMBgAA2ZSAWLr9mDgDzH9r4jPIXLPuioGxKPcAAGAAQV5uYrRFN3/DBN50zQzm7DYe7nn3hHj1kFBj2ZeYrg6mNgoGFxsmGMyzoDPNYC9nCXMw+GhyNrnGOYgTpNoyaDmQyaWRtHF2tBc1j/eeS6fcknJRLolnhjcUztrSNTOYg9zSBD+bjl6gy/tEiYApZ9zxZ9cc2ptXUiGu/TwNs+w8CR+fU64yYGaw9Bl+3H6Ubr6st0HrYSdlF4oT5907MHmcNpxhypnlHMjRZ+1w0L/tquzc0+n5LWZm6svmY8nit85zddw4qpcYiXDNOytEjem80gpRd9rSlFUpt21cPgi0GxYXRj9sO0p7zqY1Cgbzib93/9ojtrVPzRyhTuQZ2i1UBIP5RLS2YDBPqPrWyl3iebqcSONkIZ5UldfNUB93k849YC4QDAYAALNWVVNHX244JALBfNDr4+ZMcy7vT/UNCjG7PA8f4o4msoABAAyHa9LOnTiACgoKTNLMHEDgACYPM+VM3OOpufTnMzeoA7KGxgEUDqjwQWlUoFejjFRD1Q3WlhksZSUnJGerg8BhHZgIZ3i3MJEZnFNcIdrVkC5lBredYcsZhJJl/52gfYkZ9OvOk+J/Puj/bf611EtV5iKvVJkZbKjgEvcrnOztDTqBHAfHWFp+qZgYkGtcGgoHAjszeVxTnAXZK8JfrIuGKpUC+rHjlDIYzCcDTqflifkzDGX32TRx/cj0oer5OW4a1YteWf4f/bHnNN07aSBZmrJKZZIKJpBrGU8Myxm5e89l0Lxpl27nk308Oe1VQ+KoZ/ilbZO0neLtVtPt4uZjF8SkcOyZWSN1OhnLI2X4PVbtOyMmYwVMIAcAAAZWW18vdvKcycXDgzQnkOBhVVwbUBrq2dTJ1Fy6/r3f6asNh8jN2UHs8Ne/eCs9OXMEPXP1SHrsymHUJzIQgWAAACvw4yOz6MSH99PLN46hkopqevm37UablIizaPmEpDSElQWohpnmlBgmGJyiCgZrZgbzwTJP4HU0OYfSC7RPHqeL4d0vZVUbcvI45ipNIKdDZjCXgWI8hJczvzgQzLUmbxvTh/ir3nVGGWhi+apAqr+H4bKaHR3sqMqAZSI4M5gzp3mV+n7LUTLK5HEaAZfO4mHdtfUNlKAqoQLmmVTBATgJ13g25Emz/YkZFBfiqw4EsysHdRMnhTjwZ4qJ5IrKqwz6viWVNeIaZSJa5u7sSH0iAunIxWz1voBPhn2weg95ujjSE1cNb/R43u6zM+nK7Zamn3ecEElBSx66im4f20/n7+mBKf/P3n2AR1VmDRw/pBeSkISWQELvHQSkiGABEXvvWNbeWV3F1VUsH+u6a1tdV13ruq5txYKVIk2K0nvvJbT03uZ7zpvMOAnpmZKZ+/89zzBkMpm5eedm5r3nnvecIeb91pXvgb6MzGAAgFmis/toupnMH8/OM0sj9eytHnDqMhqdwI3o3l7i63HAtf1Qqrw1Z7XM27DbMUmyH3QO75Zo6tZt3H/MMSHQer/aHKdFWJCc1K29HM7INXX0tEbl5LH9TXMVe/0+AIB1aaOYH1fvlPkb9pg6g6f1q77BjCuXPiutSW/nXCbCHfYdyzCfxYlxv5VG0M/B3u1bysb9R03tQ9W+AVlO+hh6AK6fz53dWC9YRYTWvUyEZgZrYPThC0fJlHdnSVhwkDx33RmmHqTWp9Us1MolFtzZtEmbyGmAy100u1nnRbrqSTvdu3MZvc67dGy7tY1zaTBY53qaZT7C6QQDmo5fdxw0JzS04Z+WmHFnMFibUuoKgMolfCLDQuS0vh1l5optsuNwmnR14T5Yl5Mglz//P7nzzAFy8wTXlEipLC2nLKlF/45Rc6kIDQbrZ/fEwV3lb18uNZ9Bf7r0lBPe97R8kZYF0kQiZ7oKYfmOQ+YzrL7NEHU1zaw/XW32RxAMBgDL0rPkP67ZKfPW7zYHV5Xr8FamdeFG9Wwv904aXuMZ1aMZOab20zcrt5ksnqT4aDl7SDfT6fzA8SzznFq3Tg9wu7SNk17t4s1EQGtCaSawZpjM31xWl1E7vuoEYVi3di7//QEAvkkzc++eONRkiWoZAXcHg/WEqQad9YRo/w6tHbe3Lg9CHs5wX2awdjyv3ERNa+eu3XPEdEZX7eLrHwzWE7362aqlCdydGWwvH1C5TISOq570Pb1/J3PQr1/rSiLdnjP7d5brxw2QwZ3amr4AShvDrtmdYjL8dB+wl4mId2MNUi0T4a7MYA2O60X3q37JrU0weMuB49Kyp3t+H517aaM6rRntKlpiRBMItDTA/TLcZY8L11lYXi/4qlP6mqC9vne4u0TEyCpODIzqmWSCwT9v3ufRYLA2DdMs0m9X75abJwxzy3OkZeeb6xbNCQbXROtIvzVnlbw5e5W0bxktXy3fahoMXjqid5X31+xgrRuclp0nseV17X/ZdtCcoBtdTU382tgfBwSDAcBStCO2NnD4ZsU2Wbx5vxSXlprbtQbi2D4dTFMcPajU8g3FpTZTW6ykpNQsgdUg7sJN+8zByrVj+ssfLvityL8GljVrSjOVvl6+TbLzC82H+10Th5pO5s71fKdeNMp0GtaDu8r15UpKSmTb3oOyMy1fCotLaQgHAKhSt8Q4c5KyctaQO2jpAq1TfMmIXhWazsRFhZtt0JOg7ljarUHmqoIqV47uY+oUp6Rnm8ZvHVs1LLP3urH9zedzfbOrGlozuHJmsJaPmj7jZ9l5JE3+dOkY0zVe5w+nJ3WUgIBmppmQswEd28g3K7bLvuOZpkakIzPYjWUitDdBbnbtGc0Ncdyp5rFjSfTB4yZo5mpZeQVmHz61d8MCKNXReZzWAl2y9YD5feqzggyesWTLflPLVuf49pMOOm/XzEtX0xN0enLAuUGY3cieZe9lizbtk4mDusr97/4ot5wxWE7t00HcSU/kqe2H02X3kXTp7IZAtI6nio1k/6+JljzSZt96rHjvWz+Y2/5wwUjzfl8VrRuswWB9X7Rnmy8sr389xsXvZVbEelsA8HOH07NN5o8229HmN/YJiwZr9QNZi/K3bfHbEtTqTB47wNRMfOZ/i0w3WK3hq1k7WrfprdmrJCu/rBSEZgA/ecWpcuGwnlV+uOvks7oJqB6U6kFd9w7tJIAurwCAGoJQeiJz68FUR6aou3yzcru5PqNSBrIGhltGh8uR8qCkK+lJ08r1gp0b4bxww/hGP4cGgd0dCFZao9G+vNeZLhd3rmW7fl9ZxqL2AqjKgI5tTTB4ze7DJhiswceggACJiXBfNp6W5ShwU5kIe/M43Yd6trPXx3TPyY1dh8tKinRyQxa4Bq81GLxkyz4556TuLn98NJzWOd91NN00jNTSb/06lAWDtRzL6F6uPemwdvdhk9mvpUPsJ4Cc6UkPXVmotcBfmLnMlKb7dMkmtwaDdb/XcjrR4aGSmVdgEltuc0MwOM0RDCYzuDa/O2OQCQbryc7T+3Ws8TPI/r6oTeQ0GKyf9XoyQcsGOa/SQcP8dmobAOBXtqekyh8//EnGP/Wh/N/nP8tXv26VwGbNTDf4b/94pXw85WIT4K1LINhOJ3jv3XWeOQB/7YcVMu7xf8uLM5dJYGCAKfz/2QOXyLwnr5OLT+5V7VleAABcoXtinDnBqRmP7qLLUXU1jTZDqqomaqvoSJOlW1NzIl1tM2PZ5mqbpVZlb3kwuC5d0ps6DZqbDNuCiuUWNEtPadaXrkSyL1/XgFVVBnVsY67tdYM1mBoXFebW+YZut7vKRPyW2Rxhuttr9qa7Mt3tgXd31Ie2L9f+efNvzf3QNGw1jZvLMizVSV0SzfXCTXtc+jz691vW0FPMqsDqjOqRJAVFJeaYRK3YcdC8P7rL3PW7zPU9Zw+VkMAA+XFN2dfuCga7I9va32iJkImDukhIUKDcf07FpnGV2fdbfc/Xz9gdKWlmZcioHu0rrNJBw5AZDAB+QidTOhnRemB64KpZwErLMVx7aj8Z1jVRkuJjGn3QpLWWXrvlbLn25S9M3d/bxg+Wy0f1MV1iAQDwFF1arw2RNIBmb+bmavpZqp+tN5420GTWVabPq9lwmoFX3efgxz9vNKtqrju1vzx04cg6N49TyVVkBvuiiJDgEzKD7cFgDQ7p/3/ZdsBkFNqzwSrrnhhvsoy1SZU9mKonp90pNCiwrGRWaanLgw+/ZQZHmMx2/f1W704xJb1c3TDX3mxQezG4Wte2sdImJtLUgtW6zyQDNB3aj0PZ/6a0pIcGLHXF4EMXjHLZa/Xv+WvN+/ClI3rJoE5tq72fZiP/a84qx6qHfccyTTZ876RW4g5z1+82xypnDews89fvlIVbDpps4U4uPimSXl4zOJaawXXy9JXj5MHz803j8Jq0i4uSxNjmMmvNTrnpHzMd+7NzI1c0HMFgAGiCNBNJz5jWRCcznyzeKIu37DNLJbUBXKlTZpI29bjp9EEypleyyyfmevD142NXmwPjqg6OAQBwNw2eKQ0mnNLAZjK1+eKXLeb6/GFVL39vHVPW7Euzg6sKBmuQ+J8/rDD//2V7WXPUujaPU0l+kBmsNIibWykYbA9QKl26vj0lzfQZqNwwz07nG32TWsmKnSmmoZCOrQZS3ckelNWAdURogNtqBqse7eLNEnrdnzfsP2rKL7gqM3xneZkIdzQL1EC2buvnyzabgGBNTYbhWbq83jnDUv+GdGn+/5ZulnV7j5g63I2h2ZrvzVsrf/t6qcQ3D5f7zqm5iaA+n/7Nto+LkitP6SsP/XuOaQLqjmCwvidrSZmhXRMlJjJMhnVpa4LBWo7G1cFgPWGolYq0VB5qp00sw0JqX5mqx6/v3nWeyTrXetT6GfvHi0fLWYO6MMwuQDAYAJoY7Xqr5R307L2WXrBP4OxBYj2br0HgX7cfNLfpB2ObFpHSNSFO4pqHmfuPH9DZ1BR0J1d2owYAoL7sTbe2HnLP0nrN3FywcY9puqRLW6tiz0g+kpFbZaDtvZ/WyPHsstq2GijTE7d1CRjsPZphggvt46PEH2jGr3OZDJ3PHEjNMqURtOfAhwvXm9s1cFOTvh1ay687DpmGtkoDUO6kZSLs21tVHVSXlImIDq+QvamBD91XtP7oP289u9oayvWx63CamSO6axn7qJ7tTTB46db9BIObEM2k1H3Y+ZjgzAGdTTBYsy0bGwz+1+xV8uI3v0jbFpHyj1vOrnX/0kSXGX+41JxkyS3vNaLHM9qDxNVmrthmylZoszrVuvzvzB1lhTQYrLXLKV3geu3io+WN2yaZvjVd2saZkk1wDY7kAcALtEu4ZsRoCQd7BrAuQ/x86WZ58rMF5qBRz4Aufu4zGdixjenKq3URtSNuavlSJO0wfunI3jKub4dqs2gAAPBXCbHNzQlRbSLnDjOXb5OSUptcMKxHtfdpHW0PBudUmfn5zk9rTCbcBUN7mOXRmvl5Wt+OtT73zsNpkhgbZRrl+QP9PfIKsxxf6/JwHduxfTuYk+AaGFbDutUcDNbAvJq/oazmqdszg4PKDpfdUTf4WOXMYHum+8HjJkiuDa9uePVr+e99F5oT/g2lgWyts1nT8v3G6tW+LLNTa3r6Enc3n/QmPa7YdihVuiXEVVjFp/0/dP/SZmqadFLd76/lSrYeSnX8zWmZl4BmzRzZ8roy8aVvfzHL+P9z34V1LtUTV34CJzI02JQtWbHjkMvLsOjr+uUvW8wx1lkDy7JI48oD1faTMK6UnpNH8zg30n1UG8jBtQgGA4CHJ2aa/fLW3NVmMqITIe2GGhIcJOv3HjEHjpoJ8ubt50hGbr5ZeqVdU+3NUjSr46bTB8olI3r5RVMZAAAaqqzOapys3X2kTuWV6htM0BIR+pgTB5dlllXFHgDZX97wzdkbs1ZIbkGRPHDeySaYp8HgX7cdrDUYnJNfKCnpOaZkgr/QrNq8wmJHTVl7vWBdzbRp/zFTIkLv07s8qFgdLRNhLyvhHEh1d2awlolwNZ0HavkMe8axZp9rsE1Lfmlt6eCgQLOM/t2f1sjTV41r8PPsPZZhAu/uqBfsXNszODCgQumP6miNbS2d8tSVY73acEtX2j392UJ58opTZUzvDuJv9G9M91vnFYZK39PG9etomrhpGYnqynpoo+g3Z6+Sv980wTTPvOgvn5qVDdeN7W8Ct7rqQZNXXrxxQoNrtutKAF3tqNvRx4WlIvTx9D1FA8HREaFSWloq8eX1fF2dGayfFWnZ+dIuzj/qu8M6KPQIAB6SlVcgd7z5nTz7xWKTGXzeSd3NUpclWw+UZbjYRK46pa98eP+FZmKmZ0C1UdvcadfKf+69QL555AqZ88S1MuXckwkEA4CPmz59ugwdOlSioqKkdevWcsEFF8iWLWX1aVF3mk1ZXFpqAoqupI+nWXVaX7Omsg59k1uZBkVLtu6vcLtmvn7080ZTY/+ik3ua7Lqw4CD5dUdZiaea7D5a1jzO1XUtvcke8Mwrz7C1Bw11+bo9ADykc9ta+xAkxkWZAGJ2+RLz+CjPlYlwRxkSDWbbMzO1/NaY3skyons7OX9oDzl7UFfp2CpGvl213QThGsqereuOesF2+rrpvr7zSLoJjtXkpW9+MY295qzbJd6i8/Dpny8ygcEH35/jODnhz83jnNlrrGtZj6roSZuvl29zvF5vz1lt6pjn5BfJ37/9VV6cucxkrj9+2ZhGBXHtJ7y0AZ27a73HRIaa0jv2xo2uoif8ikpKyQyGzyEzGAA84GhGjvzutZnmLPW4vh3lmavGOQ4u9QCjqLjELKGsqtGbBoypjwQA/mX+/Ply5513moBwcXGxPPLIIzJ+/HjZuHGjREY2LMvKik7t3UH+u2iDfLNiW6PrX1YdTKi+RITSwKQuv1+5M8WUc7JnOmoARVcD3TtpmKOU08BObUzdQ3vdYM0A1qBoy6hwCXQKoGl9V9W5tfuCd56mGbD2wImuirIH3zq1jjEnwL9avrXWesFKA6eaHezIDPZQAzlXl4nQgKlmBlcOpL1688QKpQsuG9Vb/vLFEvnil80yeWzD6qraT5RUFRR0JQ026zxXy5lVF6TXkyQ/l792y3cckotP7iXeoMFHzb4f2iXB1KC++60f5N/3nO/VTGV3NY/rVSkz2F4qQi3dekBuOG3gCd9fvTtFUtKzTba3vqbbU1aYZpmf/P4Sk8Ci2bba28Re8qGh9JhIT5Rp4PmKUX1koAtKmejfz+y1O81qypE9fltdoVnMur3HMnJdXi9YxZZnHgO+gmAwALiA1vb7ZsV20yl8QIc2MmlIN0c9YD0DfeM/Zpr7XHdqf3ng/JMr1MXS+7hyaSsAoOn7/vvvK3z97rvvmgzhFStWyJgxY064f0FBgbnYZWaWlSXQ5a968QR9Hj3Q9tTz1cXwbokmmPrNym3y+3OHm6X1jaVZcfp4raMj5ORuibX+vqf2TjaBLQ2SnHtSNxNI/m7Vdumf3FrO6NfR8fPDuiaa4Ms7c1fL5SN7yxUvfO6oG3vx0C7yp8vLSgHsKA8Gd2gV3aTG2hXB4Jy8ArNcW4Pgmk2a0KK5nDe0mwmMXnxyzzr9vn2cgsEa8KnuZ1yxv4aUZyrnFxa59LXQrErNJtSgaVWPa8+u1VVkL3/zi3z880a5enTfKpMGaqNlyOzBYFf8DtWNa8fWZeXLdqSkSmxkQpU/+8niDY7/a63Yyo+hJ1Tue2eW3Dp+kIxwU43QjJx8U/5AA4NaAuGN2avk7blr5Hf/+NqUaatLg0dfeH/V113LjnRtG3vCY8ZGhppawlrDvKCw6IT3Ta3jrTTz98lPF5rElXvPHmb+di8a/tsJMlds60MXjJBrXv5Spn/+s1kJWXkf1+OoH1bvlMtG9jbB6dpoyR4N9J81sLPoQ9k/I3Vs9bPiYFq2S/+WU8szjXW/8Zf3a1+eE/iyUg+PI8FgAGigI5m58tWaffLtqh0VlqdqdpA2jNEJWFR4iOQUFJnsIK31e/85w/22UQUAoOEyMspKA8TFxVVbVmLatGkn3J6WlmYyiz11oJKVlWUO/gJc2Oynsc7okyQfLd0q3/66UU7pUZbx1hiH0nIkPadAxvdLloz02pePD2hXFgSbvXqbxIU2k6c+XShRYcHyyLlDzOtjd1afRPnfkuYmEPX9ym0mEHx6nyT5dedhmbthn9x5/LgEBgbKln1lwbu4UJHUVPc0x/O0sICy4Oaug4eleWCJyX5uFxspmRll43vdyK5SnJcjqXknNuKrrEPsbxl4AcX51Y6RK/bX0uKychTHjqdJagvXBQn3HCs7mdM8JKDW13hktwSZu3G/bNy1zzTrqg/93TUomBwfJYW52ZLqgqTI6sa1dURZKZD1uw5I59gTx0oDitoouUVEqPRqFydLth2Szbv2m4xTu29W7zIByi+WhEqPlu7J+l6w+YCZm185orsU5GbLdSO6yvGMLPlyxU65681v5aVrTxVvcOX7qx53rN1zRLq0iZH8nCzJr+LPamBSnCmFs2jddhnQ4bcMda0v/cOq7RIXGSojO8XLnWf2lx2HM2Rk53i3vB8lRQfL2F7tZd6m/bJy627pXH5Swf573PPvebJhf6oU5efLeUM61/p4C9buNtc928Y4ttc+ttFhQbIlr1AOHTkqoS5KxNmbctRchzYr9Zv3a1+fE/iqrKzfmqx6AsFgAKhHppAuE9x04Jj8a/YqWbhxr5b5NWeptRnMpCFdZVTPJPll20FZsGmv7DmaYbIPIsKCzfdvPG0ggWAAQBWfL6Vy3333yahRo6Rv375VjtDUqVNlypQpFTKDk5KSJDY2VqKjoz22nXpCU5+zKR34XXZKfxMMnrvpkJw/ol+jH299Sra57p3cttrgvDMdj6T4aFm09ZAs2HJQSkpL5eXrJkifLhUbwOkjvXTjBLn6pS9kX2q2nHdSN3n6yrHyyIc/ycwV2yWjOEC6toqTA+m5Eh0eKp3bJ/jNvKFbey3hsUUyCkUCQiMkI69QhnRJrNP4Vja8lwYaF5tVVckJbaodI1fsry2io8x1SHhEg7a1OjtSy5aWt2sZW+vjdklsaYLBhc1C6r0NWpYhK79ITu3TwWXbX9249u1cVlf5SE5Rlc81Z+0uSc8tkBvGDTABYA0G6zj07PRbBvCGg6vN9cGMvBq3V4OEtdWXrs7O42W1cEf16ex4jievPF2OZBWYPh76d+jOZnueeH/VEwAFxSU1/o2d2q+LfPrLdtl4OEvGDfot21frn6fmFMiVo/tIq5bxcuOZ7i0vokb0TDbB4GN5pXKS0/a+OPMXEwhWM9fskclnDKn1PXHz4XXmeky/Lo7f3T62CXExIjuPSGlQmMTFlf1tN1Zxs7KEoMRWcS59j/AFTXVO4KuCgjwbnm3ws2l2wueffy6bN2+W8PBwGTlypDz77LPSo0fZG4meFXn88cflxx9/lL1790qrVq1MY4ynnnpKYmJ+O9tT1R/zf//7X7niiisaumkA4BKp2Xmy5cBx04V54aZ9snjLvgrdpPsntZSLR/SWMwd2qbCk7PT+ncwFAIC60NrB69evl0WLFlV7n9DQUHOpTA/APHkQpnN3Tz9nbXq0aym927eUBRv3SnZ+kaln2RjaBEt1TYir8+95Wr+O8t68tSZz80+XjXE0aKqsV1Ir+dv1Z8rCTXvlwfNHmExgrd+pweBfd6RI57bx5mSylkLQ7/kLbS6m9qdmObJitUFeQ/ajtrFR0j4+SkKCgmodo8bur2HBZdmuhcWlLt3nNRiuYpuH1/q4rWLKsoGPZ+fVexs2lq9c65vc2qXbX9W4dm5bFgjTetBVPdfsdWUZm1pKzV4GY+WuFDl3aFmTL73tl+0HHX+D+hxVxQq0HMs9b/0gb94+SYaV176tj3V7j5imj85jolcXDu9pgsGz1u6S28YPEW9w1fvrmj1lqwsGd06o9rGGdmtnxmHZtoNy99m/3eeTxZsc9dI99T7fLbEs4Lz9cJrjOXXV5VtzV5smih1atzCv+/p9x2qtDa+Z5Xpc1i0hvkLJCR3bVtFl9fi1rnVSy99iUo2hJzhUXFREk/pctPKcwFcFeHgMg9zV9OLgwYPm8te//lV69+4te/bskdtuu83c9tlnn1V4rHfeeUfOOussx9ctWvhP51wAvkc76/57/jpzoKZLpey0tpZ2sY6LDDOdwZOjg80ZYD78AAANddddd8nMmTNlwYIF0r69e2pkWsEZ/TuZwJdmtU0Y2KVRj7UjpbyBW5u6N3C746yTTCO00/t1kojQsgBiTU2T9GKntYTVL9sOyOheyaaWrDcyE93JHnjRE+yJh8sy8hrzO/7zlkniCaHBZcFmzbJ0pfzCstIute0ryt5E+GgDGl9t2Fe2hL1yozp30MaAbVtEmnrQVZWImLdhj8mgN7WLbTZzf621bbfzcLoczSz7HTNzC0xjrsoNynSV3oszl0lxaakJ2tY3GKwZxTomOqevPPZj+3Qw2eZan9ZbwWBXWbUrxVxrc8vqNA8Lkf4d2sjaPYdNo+tWMZFyMDVL5q7bLf07tDaN3Tyla/mJBPt7r7I3Grxn0jCJjQwzweCPft5ggsF5hUXyxw9/kpO6JMpVp/y2muZIRo7sPZZp3l+ra8qt7PuZK2ida6XbCPiSIHc1vdAlbv/73/8c3+/SpYs888wzcs0115jgsXMKtAZ/27ZtfOdIAGgMnQy98t1yx+Sjb3Ir06W8fXy06QCe7HQGWZfFWK0uFADAdTQL7u6775YZM2bIvHnzpFMnVpQ0xpjeHeTlb381JZwaEgzWIFF2fqG0iAwzAQkNCmn2aV1pYOXck8oyHOtLTzQntoiUX3cccjSP06xZf9ImJtKMqZYtaBNTFizs2IhgsKfGxxEMLnJtXW4NZjk31quJNjJsaABr/b6jpodFz3YtxRM0wL902wET7A5z+t2WbNlv/r60CZhmEgY2ayZDuiSYbP6bX5tpgq9bDh43923bormkpGfLrsPpJwSD56zbJVsPlc2/fy3PIq6P7SmpkldYbIKglUWGhciY3skye+0u89y++jeony0aDNa/uYRaakyfP6y7ue/nyzbLreOHyH8XbTCB+qudAqyeoI0UNZi6vfy1tWdw2wPaGsTVk3Pfr9oht5wxWGav22WC9nrREw3XjxvgaEqoTupSdQPD+Oiy/UkbVrqKnrRQ+tkB+JIgTzW9sN9Ha5pVroWhGca/+93vpHPnziZ7+IYbbqi2FgydlP0X3SgZV0/bezRD3v5pjRzPypPUrDxZWz7p0Lq/95491GT4VN5Hnf9P91TXY1zdh7FlXH2BlTpS6/z3ww8/lC+//FKioqIkJaUsk0vLqWkJNtSPZhtqwGDh5n3m87m+tXZf+e5X+WDBOvnioctMQFaDWoEeXLI5qGNr0zzr9R9XmK/9LTNYs/T05LoGg1tGRfjM7xgaXHbc6lwmzBU0IKnCyh+/Jr9lM9beXK9yFu3GfUela9vYOmUgu0KnNrGm1MLirftNvwy7WWt2muszB/x20usP5480gbzFW/bLsm0HTBBYXTqyl/z9219l55E0EzC207/r135YYYLbndu0MM3PtKRb5YBxTbSpmtLM16roiSQNBv930Xp54PwR5gSGrzmUli1HMnJlwsDOtb4Pnj24mzz3xRL5bMkmuXxUH/nf0k0mMDu+kasrGkL30xU7U8yJF/270yQdzTRvHVNW2uG+ScPknrd/kD/8e7Z5H4lrHmZKxTz35RKT0Xz1mL7y6ZKyEhfO+40zMoMBFweD69L04tixY6Ze8C233FLh9ieffFJOO+00iYiIMPWF77jjDsnOzpZ77rmnysehk7L/ohsl4+opurTsnfkb5aMlW6TYqQzEyV3bylUjejg66taU+cv+6h6Mq/swtoyrL/B0J2Vveu2118z12LFjTyifdv3113tpq3yXBj1G90ySGb9skc0Hjp9wQrcmGmT6evk2E6B7Y9ZKyS0oki5t614iwhUGdWxlgsHr9x6VAR3ayIge/lcyJKlltOw8nCbr9x0xWXS+kEnntjIR5ZnGdckMbtnAzGDNgs0pKJLeHigRYXfOkG4msPjAe7Pk5RvPktG9kqSopETmrt9tgr3OpQc08/atO841man3vvWDHEjNMvuIJmVoMFizc51peQfNHj57cFfpkRgvL8xcZspMjB/Quc7bt648GNyvmmCwNtrTerP/Wbhevl6+Ve4/52S5bFRv8QV7jqbLfe/MkvScvFpLRNhpqQ59zT5evFEu+sunkpFbIPefM9wrQfAuCXFmdYSWGdH3Bg1on+n02mo/lguH9TDv8eqRi0eb9/y73vzOvF56sWcF96omE95+IupYlgszg7PzTe3lqPAQlz0m4DPB4NqaXmi340mTJpnawU888USF7z322GOO/w8aNEhycnLkueeeqzYYTCdl/0U3SsbVE7Su0yPvzzEZCLr88/fnniwje7Q3WQbOy9lqw/7qHoyr+zC2jKsv8HQnZW+yN1CC65zSO9kEChZs3FNlMPjJTxdISlq2/OOWsyvcro2KdFm6mrGsLNDg6WDwqG4JJqg1pHOCXHlKH49mJXtKcstoc61BHi2/5Qt+ywx2cZmIgvLM4DrMPXUbtClifZe2a4ar0oCZp2g911dvPkvu/tcPcu/bP8jsx6+WTQeOmSDjeUO7V5mpqkHLD++/UB75z0+m9rc9Y7xy7WHNOFZ6n4TyLGItFVGfYLBmm2pJl86tY6sNjn405SL5fOlmE9Se9ukCE6S+75xh9V5t4OnPkyc/XShbDx43QXctqeCcmV2TS0f2NsHgwxk5pozHjacNFG/QzGC1vbxMT1UZ3A9fNMo0iAsKCJBLR/Qy9/vfHy6Vd39aI9+u3G7qB198cs9q3z9dnRms2fe7j6ab7OWmvH8AVQlyd9MLzfDQ5nC6/E1rogWXd2StzvDhw00GsZaDqKpjMp2U/RvdKBlXdzqcni03/WOmmVzqRPL/rhpn6oM1FPurezCu7sPYMq5NHQ050Rh6cleDBAs37TP1L51l5RXI/5ZuNrWBtTyULoW2+2n9bnOdGNtcDqaVBYW71KN5nCvoMv6/TT7Dr/8G7E3kVKdWTb9EhAotD0q5ukxEfpG9ZnDdyjdoEKu+ASwtzaCZzWP6dBBPGtkjSe6dNEye/WKxfL96h2w9WLbSzjnLszItIfL+Pec7vm4dE3FCZrDWHdZ42/Bu7SQyLNj8zdRWN7iktFRy8otMMF0z/rUEzLCu7apsLmanPULuO2e4CTbe+vq38q85q8zJC+emj02NBkKXbj1gtvGV351Vr5/VE2dac1ez1O+YcFKNY+OJJnJaN1hXcVYVDNZA/mcPXmr+bw8Y6/UtZw42l9roPqO/p6tqBuuJDv08uWh4T5c8HuBJAY05+6SBYA3wzp07t8qmF5oRPH78eAkJCZGvvvpKwsJqXwq0evVqiY2NrTIQDAANpUvLJr/ylQkE/+70QfLiDeMbFQgGAABNS1R4qPRoF28+83VpurNFm/aZQLBaX94jwE6XsGvQ7PHLTnXc5unMYCuwZwY3tnmcJ9lr+rq+gVzdy0TYg8HaqEpr7NaFBlK10dopvZJNtqunTRzc1ay6+/rXbTJn7S5T6mJQx7o3jNfM1v2pmY5x14Z7K3cekt7tW5kSAsGBgTK4U1tH3eCq6PNq6YNTHnvPjMfuI+miCzK6JVTf48hZu/hoeerKsjI+uqKwqdL3Na2bq/vq1AtHNugxHjx/hNw1cajXAsHOmcG6367eddiUXtDXuzLdnxuzT+vfkquCwfM37DHX2ngQsEwwWEtDfPDBB6bxhb3phV7y8vIqBIK17MNbb71lvrbfp6R8cvb111/Lv/71L1NiYvv27aZ22v/93/+ZzsoA4Ar7j2eaRgOX/e1/ptnAnWed1OSXegEAgIbRWqAaMNtWno3oHPCt3KVeaeMhrTF8cvf2MqpnexOw1ACd1i6F+4LBvtA8ToW4qWawo4FcPYLB9al1+uOaHbVm47qTbu/J3dvJmj2H5Xh2nlmRV59AowaDNXC7IyXNfK11hYtKSmVE999WIg/u3LZCHeDKQTptNqYlBzRYqhnEWq+67LHrvu/1SWplMk/1+ZsqDXJr1rjWUtYAtq+KbR4u8c3DzWun462BYHc0PtQTE5rNqyUeGmvBxr0SFBhgVqUAlikTUVvTi5UrV8qyZcvMbV27dq1wn127dknHjh1NyYhXX31V7r//fpNprPd7/vnn5eabb27oZgGAI4Pg1e+Wm87gOnnsm9xKHjhvhAztmsgIAQDgp/ont5aPZIOs3XPE0ThLs4QXbtzrWGrvnBm8cNNecz2ubwdzoviV302UjNx8k3kI10qIjTLZfiWlNp/JDP6tTIRrM4Pz7ZnB5ZnHtWkVHWmuj2XkSmJsVK33/3HNThPEHOvhEhHOtDHZ4i37zf/P7F+/oLS90ZwGk/XveMmWsszcET3aOe5j//veuP+Yafzm7PtVZcHwxy8dY+r+bth/VOKahzsCzXWlY6gBYQ0467FFXct6eJJm0qqe7eLF143t28HUutY60DedPsgtz6H7gZahyMwraFQTSw0oazPMsrIlrDaFhYLBtTW90CBxbffRWsJ6AQBX0fedlbtS5ImP5svOI+kms0e74uqkgmxgAAD8PzPYnv17hfQx/1+x45Bk5RfKBcN7mKyz9XuPmvmCzgtW7z5s7jOs/GQx5SHcRwNrCbHN5VBats9kXv/WQM7VmcFFDcoMrkvdYL2PZrvr0nWtseotp/fvJKGfLpCIkGA5qUtCvX7W3mBQywVcObqvLNm635Ry0WZzdvYmkZv2Hz2hTvCCTXvNvnbRyT1N7WJtEpkYF1XvYLDS59RMVX3faIpJJdsOHTfXdS1/0ZQ9ecVYc3EnewBYT/rVFgzWWsy6P517UvcTvqcnEjXcRYkI+CrrtGwGUCP9oNNOrHrGWzsBZ+YWSE5BkelE6836UfWhDWBe+2GFqRWoJo/tbxpY2CfyAADAv3Vs1UKiwkIqlIKYs66sRITOaTSbSw/wtTyELqlev+eIRIeHmKZRcL9bzxwix7NyHc2fmjoNQLqlZnBRsRmDwDo2DHQOBn+2ZJM5aeEcGHWmtXWVtwOXGoj++01nmTHUpfT1kRQfbZo8ahBWTx5oMFeX4jvP6VtGRUibmEjzPWd6gic9J1/OGtTHPG+PxHjZuP+oZOcXmkZycc3rlw06qDwwrdvi7TGtir1BX/dE388M9oQWEeXB4JwCkRNLEleoOf3g+7NN7WstWWE/UagnEr/4ZYv89aul5utTe3sv+x5oDCIkAIwXvl4m7/y05oTR0Kza353hnmU6rqCTPa0DNmPZZpm/ca9Zfqj10a4f218GVjNJBgAA/klPYGtpqKXbDpjgj9b//WH1DhMAGtw5wXR/12Dw+n1HTWBIVxFpkInVQ56hmZq+JDQoyC01g7VMRF2bx6lWMWXB4MVb9pmTG5r1+v2jV1UZZLUHg7XBmreN6pnUoJ/Tv0cNdmvJgA8Xrje3jR94YqkJzQ6et2GPOR6wZ3nam3rZS2T0Tmppyk3sOZphMo7r+7duP55oqnWDtYmenixoTMkDK2kRGWqu03Pzq72PNhyc+p+5EhQQYEpKvPLdr/LCDePN996cvUpe+uYX08ROy5D4SskboDKCwQDk6+VbTSBYz3jeMG6Aqb0VGxkmny7ZaD78tH5T17buWXqUkVsgKenZciQjR45m5IpNyurIaVaPZibnFhSZCXhMeKiZ5MQ2D5O9RzNk9rpdsnTrAdl84JhZoqP0YO6Ri0ZLp3o0hgAAAP5XKmLJ1gNmWbfSbOArRpdlCfYtr0WqNUBjIkIrlJYAqjq5EBwY4IYyEfUMBpdnBtuz3DVbVusCa9OwylbsTJGw4CBHTV1fNbBjGxMM/vf8tSbZ44x+nU64T+/yYLBmB48ob+KlX+vY2ku/aFanXZd6loiw15jt2CrGZBxr0zF3rZjU7POl2w/JxKF138ac/EI5kJpFA7N6iCnPDE7XzOBq/O3rpeY49G+Tz5B/L1hn/tY27jtqmty9/uNKaR0TIR/df5G0adG8Pk8NNCkEgwGL0yDsk58uNEskdSlXh1YxcuHwsu9pw4Q73vxO/vjhT/Lf+y5y6eRn37FME2j+ZuU2RzC3vnTZ2cnd2smwbu3MtR7MkdkDAIC19evQxpEheTgjx/zfHjTT4JEuz5+zbpejU729WRVQFQ2suqOBXH2CwS3LG8gpPYmRlVco78xdLRMHdakw99Vs+C0HjsuQLgk+U4qjOvYyGNoIekT3diYQV5m9brCWgdBgsJZ/2ZGSZkrC2EtKaGawXX3rBTtnB2tpAM0udlfSyWdLN8ufZyyW8IhIOd2p4Z69vnl1WcGKEhENyAzOya+2nrdm4Gt5kbMGdTUnA2549Wu57Y1vTa3z/KJieeLcMQSC4fMIBgMW9+LMZSb79qkrxppAsDPtzHvWwC7y/eodsnbPYZeUXdBaZ2/MWimfLN4oxSWl5qBscOe2ZpLbOjpCbOVLc/KLiiQiNMQcqIUEBZhJb2p2nqRlly0DO61fR9O91dcnugAAwPUZhRrA0z4CwUEB0rZFpAzqWDaH0d4Il4zoZZaevz9vrbmNzGDUJCQ40OVlIjTgFBd1YnCzOrokXefEOme/+ORecigtS75btUM+XbxJxvRJNvu6Zseee1I3KbXZZEjn+jVsa4o0s1mzsjUYPGFQlyrv06s869deN3j5jrISGSeXZwkrXfmoxwuFxSXSqYFL+ruW14vddTTdbcFge2BXfwd7MFhf7wue/cQ04Hv6ynEnJOZsLf8Zf2ge5ykx5eU0qisToatKdCXAuL4dzdeadPTwhSPlxZm/mIaG+vlyzpBuHt1mwB0IBgMWpgHeL3/dajKALxjWo8r7XDi8pwkG64SzocFgXVL17cpt8r9lm2X59kNmkqrLre6ZNEzGD+hMNi8AAHAZzeT61x3nmJqPuhJp4qCuFYIoN58xyDThysovlMTY5qYRFeDJzGAtE6GPWx+aNLH7aIZcOLyHCVbNWrtLpn26QOTT3+5jb6KsiRa+TgO4GnhbtftwlSUilJ7o0dJ2WuJOLd9x0Fxr8NQuODDQZHlqU8mGZgbbE2a0VJ272B977Z7fml/+vHmfKQOhFy1vcMdZQ8y42LOet9szgwkG17+BXG7VZSLmrS8rxaJlEu2uPbW/CQ5/9PMGuXREb45d4RcIBgMWLg/x8Adzzf/1bGd1JSCGd080mbg/rtkhD11Q/f2qevzXZ62UwqIS06xFz9jrCidd8nX+0B4m+FzfzsIAAAB1ofON/z14qcxdt0tOrxRIah0TKZeN7C0fLFhHVjBqFRYSJOnZ1Tebqi9dGafZrvUpE6GuP22AqRVsD2h+/uClpnTBmt2HTaZiswCRaZ8skIBmzUwQ1R88fdU4Sc3Kq7JEhNLyCZrUsmjzPtODRLNqtTFkt0q9Tm44bYAs23pA2sVFNWg7ksuDwVomwl32HMs01xrY1ixmDfrOLQ9MatD7/flrzUWzpT+acpH0bNfSZAbr693QILcVxZSXiciookyEJjBpzemW0RHSx6nWtGofHy0PnDfCY9sJuBvBYMBitO6UTjIefH+2mdDcMWGI6a5dHT2bfmb/TvLpkk2mi67WIKvLJHfKu7McXXc1CHz+0O5y19lDJTG2YZMwAACA+tCl9eee1L3K79185iDThLa6lVGAc43ePUcyaqzdWh9ac1TVNxisGYnOtPzB7887ucJtOs/WWqiRYSHiDzQAp5eanDWoiwkG/2v2KnNsoxmclZNXJgzsYi4NlRQfbY5n3BUM1rIhmkijNBC8ef8xUyZj/oY9khDbXD649wJ59bvlJuC9eMt+U+ZGV1jqiYCuCXHmhAXqxt44tKqawev3HTENR7WUkLsaBQJNBe8agIUs3LRXnv96mWw9eNx8fdv4IXLHWSfV+nNaPF+Dwd+t2l5rMFgnyn//9lcTCJ40pKvcc/YwCQ0KlFYxvzW+AAAA8CYtDfHe3efzIqBOy8qLS0tNc7ao8LJAUmNLRCh3BPBG9UwSqxk/sIv83+c/myX8lUtEuIqWZUho0dxtweC95VnBcZGhkppTIKt3HzZ1qrWUwaQh3aRti+by1JVjTebq2c/8V75dud2cTNDA8ZWj+7hlm/yVJjo1DwuR9CrKRPy0fo+5HtvntxIRgL8iGAz4OQ3Ortp9RL7+crnMWbdbggICTFO4i0f0lBHd29cpw2Fo1wSzXOarX7fK9eMGmDP0+rhaq+ybFdvMBEUnJNtT0mTt7sNyPDtPkltGy+OXjvGbzAQAAABYj5ZLU+k5BS4JBueXB4O1mSFcswJg4qAu8r+lm83XdVnF2NC6wdpcTF8/Vwfy7UHm0/smy6fLtpmMXy0Jok4rb2SmNFv14hG9TAPwDxasl/iocLP6EvXTIjK0yjIRWiIiNDhQTu7ejiGF3yMYDPixRZv2yV+/WuLoTqvNJDRAq8uJ6iMwIEAePH+EPPTvOeZy+4ST5J2fVsvSrQdOuK8uZTpvaHe5bfxgAsEAAADwixqj6bn5kiQ1lyyoa0kAVd8GcqjeRcN7mmBwRGiw9GrX0i1DlVweDN53PFO6ubhh254j6eb6pE6t5aeN+2Xxln1is4lEhYXISV0rBre1tM0r3/5qstWvHdPP0UwOdafN+HaVj7ndwdQss3pWs4I5UQMr4J0D8EPLth2Q139caa61qcCEfh3kmnEDTTOVhtY608YUP2/aJ18t3yq3vv6Nue3UPh1kyjnDJa+o2DSK69ymRbUNHgAAAABfLBNRXY3RxmUGcyjuKgM6tpHT+3WUpJYxbmtQ3bFVC0cWr8uDweWZwe3jokzzv9nrdptSBo9eMtqUNXDWKjrClOLTOsmXj6JEREPrBucWFDka9dmzgtXYvpSIgDXwCQT4mdd+WCGvfPer+f+pvZPl3knDJD5UJC4urtFNL/54yWjJzCswS5J08qHdewEAAAB/LxNR1bLyhsglGOxyeozz8k1niTtpmQi152jFjFJX2HMsQ4ICmknbFhGmMdygzgmm/EN1STZaP7iouJTGcS74m7b3tflp/W5zPbY3wWBYA8FgwI/oh5gGgrXj7fPXn2m60JaWlkpqalmZiMbSM9Sv3jzRJY8FAAAA+FLNYFfIL3JfAzl4Ihjs+iZy+pjt4qNNb5dOLVtIl7ZxtZbwCwxxTwa0pf6mcwtMMDgnv1B+3X7QJDrR9BxWwScQ4AeKS0pNIzftpKtLzl6+aYJ0T4z39mYBAAAAPi3WEThyVZmIsprB4cE0kPMl7eKjJDCgmex1cTA4O79QjmflSe/27ql1jBO1iCirA55R/je9YuchKSopNatqAasgGAz4uLW7D8ujH82THSlppubRn685jUAwAAAA4MIGcmkuKhORZy8TEcqhuC/R2r2JcVGmpIMr2YPLHVqWZR7DMw3knLP97c3WdVUtYBV8AgE+RpcRLdi4x3xoaSOLn9bvEZvY5PKRveWW8YOlbYvm3t5EAAAAwC+4umawPRgcFsyhuK9JiG1uygnoqkxXNaqbs66sVi2BSM9pUX6Cx94UcntKmrnuWkt5DsCf8AkE+ICComL5dPEm+fLXLbJx/7EK3+vYKsY0ERjcOcFr2wcAAAD4o5iIioGjxsorLxNBzWDf0yo6Qmw2kdTsPGld3nisMQqLS+SzpZskOiJUzuzfSXKzM12ynahZjKMOeNnftK6w1ZMz7eKiGDpYBsFgoIlbu+ew/PE/P8nOI+nmDPRpfTvKaf06yqBObSUqPNTUMQsIaObtzQQAAAD8sjyANlHWZlOukF+eGRwRQs1gX9MquiwAfDQz1yXB4LnrdsmxzFy5ftwAc3Ig1wXbiPo0kMuX0lKb7DycJp3btOCYGpZCMBhoojbsOyr/+H65zNuwR5o1E7nu1P5y6/jBjg8vAAAAAJ5ZVu6yzOCi8jIRIRyK+2JmsD0Y7AofLdpgri8b2dslj4e6aVFeMzgzt0AOpmWZ0i2UiIDV8AkENEHfrtwuU/8z19SjGtI5Qe49Z5i5BgAAAOD5hlOaPejSBnIEg31Oy/Jg8JGMnEY/1qG0bPl1xyEZ0b2ddGgVI6WlpS7YQtS39Iu9XnCXtrEMHiyFYDDgARrU1Qlkt4Q4aaZpvjXUEHtj1kp5Y9YqiQ4Pkb9OPlNG9UziNQIAAAC8RFfmaRBXa7yGBAW6pEwEmcG+p3V5MFhLOzTWz5v3mevT+3dq9GOhfqLCQyQwoJmk5xTI9kOp5rauCTSPg7UQDAbcbM/RdJn6wU+yZs9hU+9Xm71VLvWwfMdBWbhxn3y7cpscTMs2xetfvXmiCR4DAAAA8G6ZCHsmYWNrxdqDweHBHIr7cs3gxlpUHgwm8cfzNDlLs4M1w3t7SnkwmMxgWAyfQIAbzV67Sx7+YI7JJEhqGS1z1++Wjc99Js9dd7oM7pwgJaWl8pcvFssHC9ab+4cGB8otZw6WW84cJOE0lQAAAACaTI3RNBcEg3UloGKu73taxdS/ZnBOfqFsO5QqmXkFckqvZBOI1FWjS7fsl+SW0ZLcMsaNW4zqDOvaTr5fvcOU69CSLYmxUQwWLCWgoT84ffp0GTp0qERFRUnr1q3lggsukC1btlS4T35+vtx5550SHx8vzZs3l4svvlgOHz5c4T579+6VSZMmSUREhHmcBx98UIqLy86WAr5KP/TfnLVS7nvnBwlo1kxeuGG8fPvIlXLfpGFyNDNHrn/lK7nvnR/lyhdmmEBw98R4efP2SfLzM9fLvZOGMTkEAAAAmgj7qj5XNJGzN5DTJBD4lsjQYBM41OO52hzPypOnP1soIx95V65+6Qu5/Y3vTKKQWrvnsGTlF8roXske2GpU5Y+XjDYNAYtLS6Vzm1gJCKi+lCPgjxocDJ4/f74J9C5dulRmzZolRUVFMn78eMnJ+e2N8f7775evv/5aPv30U3P/gwcPykUXXeT4fklJiQkEFxYWyuLFi+W9996Td999V/70pz81/jcDvGDP0Qx54uP5cuqf3pcXv/lFWkdHyvv3nC/jB3Q2HzA3nzlY3r3rPLPEaNaanbJh31E5tU8H+fc958vIHkkEgQEAAAB/DgYXFJuAYk19RNA06WumTeRqqxmsmcMXPPux/HfRBmkfHyVXju5jbp/xy5YKJSJG0xvGa+Kah8uz155uErf6Jrfy3oYAvlYm4vvvv6/wtQZxNbN3xYoVMmbMGMnIyJC33npLPvzwQznttNPMfd555x3p1auXCSCffPLJ8uOPP8rGjRtl9uzZ0qZNGxk4cKA89dRT8tBDD8kTTzwhISEhjf8NAQ/QZhLPf71U/rNgvZTabKYkxEXDe8olI3qZDxpnWh7iu0evlNTsPLPkjOYRAABY06uvvirPPfecpKSkyIABA+Tvf/+7DBs2zNubBaCaYHBGTkGjxya/qCwYDN+k2aRrdx+R0lJbtdmkz3+1VFKz8+X2CUPk1vGDJTgw0CQBLdq0Vw6nZ8uctbskODBAhnZN9Pj24zfDu7WTmY9cYQL8gNW47FNIg78qLq6s4ZUGhTVb+IwzznDcp2fPnpKcnCxLliwxwWC97tevnwkE202YMEFuv/122bBhgwwaNOiE5ykoKDAXu8zMTHNdWlpqLp6gz2Oz2Tz2fFbha+Oq9X61A6k2hntrzmpZu+eIdGkTK3dNPMk0irNPDqr6fYICmjm60br79/W1cfUVjCvj6mvYZxlXX2Clz6qPP/5YpkyZIv/85z9l+PDh8uKLL5p5sJZd0wQLAE2wgVxu4zODtYFcGM3jfJa9tIDWj46Pqpj0o1bsOCRfLd8q/ZJbyx0TTnIcE54/tIc5Xrz5n9/IjpQ0uWBYD4kIDfbCbwBnHVpRsxnWFOSqift9990no0aNkr59+5rbNMNBM3tbtGhR4b4a+NXv2e/jHAi2f9/+vepqFU+bNu2E29PS0jxWa1h/36ysLBNgCwhocKUNNPFx3X00U5ZsPyQH03LkgiGdpUubsn1Zz/I+/r+lsm7fMbE53f+s/h3k/omDzOQuPT1NmoqmNq7+gnFlXH0N+yzj6gv088oqnn/+ebn55pvlhhtuMF9rUPibb76Rt99+Wx5++GFvbx6AKhrI2ctE5BQUydx1u+SsQV1M1md95BYWSUz548H3aLk/pXWDKweDtTGc1gnWCiCPXjK6QubwxMFd5c8zfjaB4MTY5vLwhSM9vu0A4NJgsNYOXr9+vSxatEjcberUqSaLwjkzOCkpSWJjYyU6Olo8dUCt9YL0OQmu+ee4asfXm/4123ygqx/W7pH7zx0u7eOi5C9fLpG9xzKlX3IraRvbXHokxsvJ3dpJ/w6tm2Ttr6Y0rv6EcWVcfQ37LOPqC4KCrLF0Wvtl6Co6ndfa6We0rqjTlXNVYXWc/2LlRtMf1+jwsvKFB9OyTN+bRz6YI7PX7ZbAgGZy1sAu9c4MbtsiyGdXQlh9f21ZHgA+kp4j3RPi5PtVO0wm8APnnSyLt+yXrYdS5dIRvaR3+5YVxigqLNj0kflu1Q75v6vHmWZ0zt+3+ri6E2PLuPqCUg//7Td6xn3XXXfJzJkzZcGCBdK+fXvH7W3btjUT3fT09ArZwYcPHzbfs9/nl19+qfB4+n3796oSGhpqLpXpBNqTgS4Nrnn6Oa2gqYzrm7NXm0DwQxeMlDYtIuWJTxbIn2csdnz/jglD5I6zTmqSwd+mPK7+hnFlXH0N+yzj2tRZ5XPq2LFjJqBU1Qq5zZs3V/kzrI7zX6zcaPrjGlRaKu1itQH0LrnrzW9lweYD5vY1Ow7IsOTYOj+ObkteYZEENbNJamqq+CKr76/hgWVrQ3cfOipLNu2S9xeVvWev3XPYHD/GhIfItSO6Vvn63nVGX7nq5C7SvkXoCd+3+ri6E2PLuPqCLA+vjmtwMFjfpO6++26ZMWOGzJs3Tzp16lTh+0OGDJHg4GCZM2eOXHzxxeY2rYG2d+9eGTFihPlar5955hk5cuSIozbarFmzTIZv7969G/ebAQ20+0i6/LB6h/RJaiXXntrPBE/6dWgtP67eaT7guyXEyal9OjC+AADAY1gd579YueEb4/rGbefI5Fe+MoHgqLAQycovlP3peY6eOXVtOl1qE4mKCK/XzzUlVt9fOybkmuuZa/aYpnAdW8XIeUO7yyvfLTeNxJ+4bIx0ap9Q5c/qK/5b+lxFVh9Xd2JsGVdfEOTh1XFBjSkN8eGHH8qXX34pUVFRjhq/MTExEh4ebq5vuukmU9JBP+g0wKvBYw0Aa/M4NX78eBP0vfbaa+Uvf/mLeYxHH33UPHZV2b+AJ7w5e5X5IL/1zMGOzN/E2Ci5ftwAXgAAANBoLVu2lMDAQMeKuKpW0FXG6jj/xsqNpj+uHdvEyr/uOFf+7/NFcuNpA+Wx/84z9V/r89gFxUXmOjw02KcDflbeX9u0aG6uNRAcGhwor982SdrHR0vv9q1k4/6jcvHJvSrUCq4PK4+ruzG2jGtTF+Dhv/sGP9trr70mGRkZMnbsWElISHBctDOy3QsvvCDnnHOOyQweM2aMmdx+/vnnju/rJFhLTOi1BomvueYaue666+TJJ59s/G8GNDAQ/MUvW0z9p3F9OzKGAADA5bTJsq6i0xV0zplL+rV9BR2ApkdXCL5z53lySq9k8/99xzMlt6AswFsXWiJChQdboz66P2oVHeH4/y1nDjaBYHVK72S5dfyQBgeCAcBnykTUJiwsTF599VVzqU6HDh3k22+/behmAC6h+/PzXy+Tt+eulnZxUfLijRP4IAcAAG6jq+cmT54sJ510kgwbNkxefPFFycnJkRtuuIFRB3xA17ZxpmHYjsNp0i+5rORhbeyB4/BQgsG+KiYiVFpEhkmLiFCTIQ4AvohPIVhSTn6hrhWRiJAgyS0slue+WCyfLtkknXX51+2THMt/AAAA3OHyyy+Xo0ePyp/+9CdTKm3gwIHy/fffn9BUDkDTpJnBavuh1DoHg9Nz8s11TESYW7cN7i038OF9F0pUeIiEBAUy1AB8EsFgWMq2Q6ny1pxV8t3KHVJcWioBzZqZ+sBKG8a9fuvZEts83NubCQAALOCuu+4yFwC+p2tCrOP4oq4ycgvMdYtI+uP4sg6tYry9CQDQKASDYQmZuQXy8re/yMc/bzTBX60JnBgXJVl5heasrn6g33HWSdI8LMTbmwoAAACgievSNq7ewWAygwEATQHBYPidTfuPSUp6tlnCo4HevUcz5IWvl8nx7Dzp0jZWHjx/hIzumWS+DwAAAAD1FRkaLO3jo2TLweNSXFIqQYEB9cgMpkwEAMB7CAbDb+QUFMlfv1winyzeeML3wkOC5IHzTpZrTu0nwYHUdgIAAADQOKN6JpmVh+/8tEZuPmNQrffPyC2rGazNxwAA8BaCwfAL21NS5d63fpDdRzOkZ7t4OX9oD7GJzZSB0LIQl43sLW1pCgcAAADARe6bNFx+Wr9bXv3uVxnXp4N0LW8qV52MnLLMYBrIAQC8iWAwfFp+YbHM+GWzPP/1MsktKJIbTxsod589lM6uAAAAANwqOiJUnrx8rNz2xrfy7BeL5c3bz6nx/un2zGAayAEAvIhgMHxGaalNFmzaK+v3HpE9RzMkNTtPthw4Lmk5+aZm10s3TpAz+nfy9mYCAAAAsIhTeieblYmrdqVISWmpBAZUXzs4vTwzOCqcMhEAAO8hGAyfsGTLfvnrV0tk84HjFW5vFR0ht40fIled0lfio8K9tn0AAAAArKlXu5bmOGXv0Uzp1KZFjTWDo8ND6tRsDgAAdyEYjCbtwPFMeenbX+SbFdulWTOR84d2l/OH9ZBOrVtIXPNwJlIAAAAAvKpnu5YiskU2HThWYzBYM4OpFwwA8DaCwWhSikpKZdm2A7Jo835ZuGmv7EhJM7cP6NBGHr98jPRIjPf2JgIAAACAQ6/2GgwW2XzgmJw9uGuNmcFd2sYycgAAryIYjCaTAfzCzGUyb8MeySssNreFhwTJaX07ypkDOss5Q7pJQEAzb28mAAAAAFTQo11Zwsqm/ceqHZnC4hJznNMiIozRAwB4FcFgeFRxiTZVaCbNtOaDiKSkZ8snizfKez+tlfyiYkmOj5KxfTvKqX06yODOCRISFMgrBAAAAKDJah4WIskto01msM1mcxzrOMvIyTfXMZE0jwMAeBfBYHjEtkOp8vac1fLtyu0SGRYsbVs0l5yCQjmYmi2lNptpBPfUFafK0OQWEh8fLwE1dOEFAAAAgKZWN/jHNTvlSEaOtGnR/ITvp+cWmGsygwEA3kYwGI1yLCtX1u05YpZGJcZGnfD9zNwCefnbX+SjnzeIzSbSuXULaRbQTA6kZklUeIgM6ZJgmsKdNaiLhAYFSmpqKq8IAAAAAJ+rG6zB4E0HjlcZDNZ6wSomgsxgAIB3EQxGg6Tn5Muj/50nP63fbb4ODgyQK0b3MbV9tSmCZvz+sHqHfLBgnWTkFkjXtrHywHkjZHSvpCqXTanS0lJeDQAAAAA+p1e7siZyG/cdlbF9Opzw/fSc8szgSGoGAwC8i2Aw6m3FzkPy0L/nyKG0bBnUqa2c3L2dzFyxTf49f525OItrHiYPnHeyXHNqPwkOpP4vAAAAAP/Tv2MbkyCjCTG3TxhyQgKMJtMoMoMBAN5GMBh1UlBULOv3HZUZSzfLjF+2SECzZnLfOcPlptMGSkBAM7nlzMEyd90u+WX7Qdl3LFPaxUVJn+RWJlM4PCSYUQYAAADgtzTIO2FgF5Mks3JXigzpnFB1mQgygwEAXkYwGFXSLrga/J27brf8uv2grN97RIpKyso49G7fUh679BTp36GN4/4hQYFy1qCu5gIAAAAAVnPZyN4mGPzJzxtPCAZTJgIA0FQQDIZDTn6h/LB6p8xdv1tW70qRtPKlTEEBAdI7qaUM7pxgGr6d2jtZAgMCGDkAAAAAKDe4c1vTP0VLRTx84UiJbR7uGBsayAEAmgqCwRZVWFwie49myNHMXDmckSMLN+6V+Rv3SF5hsfl+UstoGde3o5zWr6MM79ZOIkIp9QAAAAAA1dE6wZeO6CV/nrHYJNhcfHIvx/e0qbZqEUEDOQCAdxEMtpidh9Nk+uc/y9KtB6TUZqvwvU6tW8h5Q7vLuSd1l4TY5l7bRgAAAADwRb2TWpnr/cezKtyekZNv+q40Dwvx0pYBAFCGYLCPKy21ye6j6dI+PtrU7a3OjpQ0+e+i9fLpkk1SXFIqAzq0kZ7t4qVNi0izfElrWumSJgAAAABAwyS0KEuqSUnPrnB7em6BaTKnzbcBAPAmgsE+EvBdtTtFflq/WzJzC2Rgx7Ymc1frTv1rzmrZtP+YxDUPM91rE+OiJDYyzHSpPZSaJat2pZhLSnqOeayk+Gh5+KJRMrZPB2//WgAAAADgV1rFRJgM4ENpvwWDD6dny4HULGlbHigGAMAng8ELFiyQ5557TlasWCGHDh2SGTNmyAUXXFChXlJV/vKXv8iDDz5o/t+xY0fZs2dPhe9Pnz5dHn744YZult85mJYlUz+YK8t3HHLc9r+lmx3/12Ee0ztZ1u89Iv9dtKHKx2gVHSHjB3SWc4Z0k7F9O9D8DQAAAADcIDgw0Bx/2TOD8wuL5e63fpDcgiK5ZkxfxhwA4LvB4JycHBkwYIDceOONctFFF53wfQ0QO/vuu+/kpptukosvvrjC7U8++aTcfPPNjq+joqLEqmw2m2w9lCrbD6Waxm4a4F24aZ9k5xfKGf07yaUjekvrmAhZvfuwqTmlAXcNBHdPjDcN4bYePC5p2fmSlqOXPIlrHi6DOrWVdnFR1QbnAQAAAACuo6s4N+w7alZ4vjBzqfn/JSN6yWUjezPMAADfDQZPnDjRXKrTtm3bCl9/+eWXMm7cOOncuXOF2zX4W/m+VgwCf/TzBnln7hqzfMhZm5hIeeSiUaaxmz2gq8HfyrRecN/k1h7bZgAAAADAibQchCbwpObkyfyNe00Zvz9ePJoEHQCAdWoGHz58WL755ht57733Tvjen//8Z3nqqackOTlZrrrqKrn//vslKKj6zSooKDAXu8zMTHNdWlpqLp6gz6MBXFc8X0patvz166Xyw+qdEhUWIucM6WqyeVtGR0j3hDhHVq8+n178mSvHFYyru7G/Mq6+hn2WcfUFzAEA+IO2sc0dTbz3HcuUkT3a19jsGwAAvwsGaxBYM4Arl5O45557ZPDgwRIXFyeLFy+WqVOnmvISzz//fLWPpTWFp02bdsLtaWlpUlxcLJ46UMnKyjKBy4CAgHr9bHpOgazbf0zW7Tsmq/ccky2H0sztAzu0kj9dOFzim4c53bvY/F5W0ZhxBePqaeyvjKuvYZ9lXH2BzgMAwNcllDeKW7hxr7nuUcXKTgAA/DoY/Pbbb8vVV18tYWHOgU6RKVOmOP7fv39/CQkJkVtvvdUEfENDQ6t8LA0YO/+cZgYnJSVJbGysREdHi6cOqDVbV5+zrkFLPSP8+Mfz5VenRnDBgQEyqmeSjO/fyZSBCAq0dgC0IeMKxtVb2F8ZV1/DPsu4+oKaVocBgK9lBs/fUNYsvUc7gsEAgKbD7TPuhQsXypYtW+Tjjz+u9b7Dhw832b27d++WHj16VHkfDRJXFSjW4KEnA4gatKzpObPyCuSbFdtl2bYDUlJaKou37Je8wmI5uXs7Obl7exncua30TWolocEc9NRnXOGe/RWMa1PC/srY+hr2WdfhcwqAvzSQUzuPpFfb8wUAAG9xeyTyrbfekiFDhsiAAQNqve/q1avNQUDr1r7dCG3Gss3yzP8WmeCvnTYNeOaqcTJhYBevbhsAAAAAwL0N5Ox09Wen1i0YbgCA7weDs7OzZfv27Y6vd+3aZYK5Wv9Xm8HZSzh8+umn8re//e2En1+yZIksW7ZMxo0bZ+oJ69faPO6aa64xZQJ8RXpOvmzcf1RiIsLkcHq2fLdqh3y7crvERITK7ROGyKQh3aR5WIiEBQdZvgwEAAAAAPi7uOZhpmFcYXGJdGkTS/M4AIB/BIOXL19uArl29jq+kydPlnfffdf8/6OPPjLNwK688soTfl5LPej3n3jiCSkoKJBOnTqZYLBzPeCmbvWuFLnv3VlyPCuvwu19k1vJC9ePl8S4KK9tGwAAAJoeLYf21FNPydy5cyUlJUUSExNNMsQf//hH0z8DgH+UD2rbIlL2HsukeRwAwH+CwWPHjjWB3prccsst5lKVwYMHy9KlS8UXZecXylvz1suHS7aK2EQmj+0vAc2aSXREqAzq1NZcrN4MDgAAACfavHmzaej4+uuvS9euXWX9+vVy8803S05Ojvz1r39lyAA/kRAbVRYMpnkcAKCJoXtZPS3esk8efH+2pOcUSOuYSHn2mtNkWLd27nl1AAAA4FfOOussc7Hr3Lmzabb82muvEQwG/IhmBqseNI8DADQxBIPrqVPrWAkODJRbT+snvxs/VCLCWM4HAACAhsvIyDB9N2qiZdX0Yqe9OZRmGevFE/R5dGWgp57PKhhX/xzX807qbhqKD+jQ2q/+Zrw9rv6KcWVsfQ37rGt5+j2VYHA9JcQ2lx8evVKyMjMkLIThAwAAQMNpQ+a///3vtWYFT58+XaZNm3bC7WlpaVJcXOyxA5WsrCwTCAoIoCQa49q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",
+                                                            "text/plain": [
+                                                                      "<Figure size 1400x1860 with 12 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        },
+                                        {
+                                                  "data": {
+                                                            "image/png": 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+                                                            "text/plain": [
+                                                                      "<Figure size 1400x310 with 1 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        }
+                              ],
+                              "source": [
+                                        "\n",
+                                        "def plot_series_grid(series_ids: tuple[str, ...], title: str, columns: int = 2) -> None:\n",
+                                        "    rows = int(np.ceil(len(series_ids) / columns))\n",
+                                        "    fig, axes = plt.subplots(rows, columns, figsize=(14, 3.1 * rows), squeeze=False, layout=\"constrained\")\n",
+                                        "    for axis, series_id in zip(axes.flat, series_ids, strict=False):\n",
+                                        "        frame = in_analysis_window(series_frames[series_id])\n",
+                                        "        label = service_summary.loc[service_summary[\"series_id\"] == series_id, \"description\"].iloc[0]\n",
+                                        "        axis.plot(frame[\"timestamp\"], frame[\"value\"], color=\"#2a6f97\", linewidth=1.25)\n",
+                                        "        axis.set_title(f\"{series_id}: {label}\", fontsize=10)\n",
+                                        "        axis.grid(alpha=0.25)\n",
+                                        "    for axis in axes.flat[len(series_ids) :]:\n",
+                                        "        axis.set_visible(False)\n",
+                                        "    fig.suptitle(title, fontsize=14)\n",
+                                        "    plt.show()\n",
+                                        "\n",
+                                        "\n",
+                                        "plot_series_grid(MACRO_FEATURE_SERIES_IDS, \"Active macro and market feature histories\")"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "c442f898",
+                              "metadata": {
+                                        "id": "c442f898",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Build a timestamp-aligned analysis panel\n",
+                                        "\n",
+                                        "This panel is intentionally for descriptive analysis: values are aligned on their reference month, regardless of when they were released. Missing early history remains missing. Forecasting requires the release-aware snapshots shown later."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "38d7b859",
+                              "metadata": {
+                                        "id": "38d7b859",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "Analysis panel reference months: 2000-01-01 to 2026-09-01\n"
+                                                  ]
+                                        },
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>non_missing_months</th>\n",
+                                                                      "      <th>first_available</th>\n",
+                                                                      "      <th>last_available</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>HY_SPREAD</th>\n",
+                                                                      "      <td>37</td>\n",
+                                                                      "      <td>2023-09-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>CPI_YOY</th>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>CPIAUCSL</th>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>UNRATE</th>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>FEDFUNDS</th>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_6m_pct</th>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_3m_pct</th>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_1m_pct</th>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>global_supply_chain_pressure_index</th>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>target</th>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_3m</th>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>YC_SPREAD</th>\n",
+                                                                      "      <td>321</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>SPY_RETURN_3M</th>\n",
+                                                                      "      <td>321</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>SPY_RETURN_12M</th>\n",
+                                                                      "      <td>321</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>VIXCLS</th>\n",
+                                                                      "      <td>321</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ICSA</th>\n",
+                                                                      "      <td>321</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>XLI_RETURN_12M</th>\n",
+                                                                      "      <td>321</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>XLI_RETURN_3M</th>\n",
+                                                                      "      <td>321</td>\n",
+                                                                      "      <td>2000-01-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                                    non_missing_months first_available  \\\n",
+                                                                      "HY_SPREAD                                           37      2023-09-01   \n",
+                                                                      "CPI_YOY                                            319      2000-01-01   \n",
+                                                                      "CPIAUCSL                                           319      2000-01-01   \n",
+                                                                      "UNRATE                                             319      2000-01-01   \n",
+                                                                      "FEDFUNDS                                           320      2000-01-01   \n",
+                                                                      "ipman_change_6m_pct                                320      2000-01-01   \n",
+                                                                      "ipman_change_3m_pct                                320      2000-01-01   \n",
+                                                                      "ipman_change_1m_pct                                320      2000-01-01   \n",
+                                                                      "global_supply_chain_pressure_index                 320      2000-01-01   \n",
+                                                                      "target                                             320      2000-01-01   \n",
+                                                                      "ipman_change_3m                                    320      2000-01-01   \n",
+                                                                      "YC_SPREAD                                          321      2000-01-01   \n",
+                                                                      "SPY_RETURN_3M                                      321      2000-01-01   \n",
+                                                                      "SPY_RETURN_12M                                     321      2000-01-01   \n",
+                                                                      "VIXCLS                                             321      2000-01-01   \n",
+                                                                      "ICSA                                               321      2000-01-01   \n",
+                                                                      "XLI_RETURN_12M                                     321      2000-01-01   \n",
+                                                                      "XLI_RETURN_3M                                      321      2000-01-01   \n",
+                                                                      "\n",
+                                                                      "                                   last_available  \n",
+                                                                      "HY_SPREAD                              2026-09-01  \n",
+                                                                      "CPI_YOY                                2026-08-01  \n",
+                                                                      "CPIAUCSL                               2026-08-01  \n",
+                                                                      "UNRATE                                 2026-08-01  \n",
+                                                                      "FEDFUNDS                               2026-08-01  \n",
+                                                                      "ipman_change_6m_pct                    2026-08-01  \n",
+                                                                      "ipman_change_3m_pct                    2026-08-01  \n",
+                                                                      "ipman_change_1m_pct                    2026-08-01  \n",
+                                                                      "global_supply_chain_pressure_index     2026-08-01  \n",
+                                                                      "target                                 2026-08-01  \n",
+                                                                      "ipman_change_3m                        2026-08-01  \n",
+                                                                      "YC_SPREAD                              2026-09-01  \n",
+                                                                      "SPY_RETURN_3M                          2026-09-01  \n",
+                                                                      "SPY_RETURN_12M                         2026-09-01  \n",
+                                                                      "VIXCLS                                 2026-09-01  \n",
+                                                                      "ICSA                                   2026-09-01  \n",
+                                                                      "XLI_RETURN_12M                         2026-09-01  \n",
+                                                                      "XLI_RETURN_3M                          2026-09-01  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 10,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "analysis_series_ids = FEATURE_SERIES_IDS\n",
+                                        "wide_features = pd.concat(\n",
+                                        "    [\n",
+                                        "        in_analysis_window(series_frames[series_id])[[\"timestamp\", \"value\"]].rename(columns={\"value\": series_id})\n",
+                                        "        .set_index(\"timestamp\")\n",
+                                        "        for series_id in analysis_series_ids\n",
+                                        "    ],\n",
+                                        "    axis=1,\n",
+                                        ").sort_index()\n",
+                                        "analysis_panel = wide_features.join(\n",
+                                        "    target.set_index(\"timestamp\")[[\"registered_target\"]].rename(columns={\"registered_target\": \"target\"}),\n",
+                                        "    how=\"outer\",\n",
+                                        ")\n",
+                                        "analysis_panel = analysis_panel.join(\n",
+                                        "    ipman_change_3m.set_index(\"timestamp\")[[\"value\"]].rename(columns={\"value\": \"ipman_change_3m\"}),\n",
+                                        "    how=\"outer\",\n",
+                                        ")\n",
+                                        "\n",
+                                        "panel_coverage = pd.DataFrame(\n",
+                                        "    {\n",
+                                        "        \"non_missing_months\": analysis_panel.notna().sum(),\n",
+                                        "        \"first_available\": analysis_panel.apply(lambda column: column.first_valid_index()),\n",
+                                        "        \"last_available\": analysis_panel.apply(lambda column: column.last_valid_index()),\n",
+                                        "    }\n",
+                                        ").sort_values(\"non_missing_months\")\n",
+                                        "print(f\"Analysis panel reference months: {analysis_panel.index.min().date()} to {analysis_panel.index.max().date()}\")\n",
+                                        "panel_coverage"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "85096389",
+                              "metadata": {
+                                        "id": "85096389",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Distributions, rolling behavior, and stress regimes\n",
+                                        "\n",
+                                        "The binary target is rare, so compare regime summaries alongside their counts. Rolling statistics are descriptive and may overlap because returns and IPMAN changes themselves use trailing windows."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 12,
+                              "id": "9237d64b",
+                              "metadata": {
+                                        "id": "9237d64b",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>count</th>\n",
+                                                                      "      <th>mean</th>\n",
+                                                                      "      <th>std</th>\n",
+                                                                      "      <th>min</th>\n",
+                                                                      "      <th>5%</th>\n",
+                                                                      "      <th>50%</th>\n",
+                                                                      "      <th>95%</th>\n",
+                                                                      "      <th>max</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_1m_pct</th>\n",
+                                                                      "      <td>320.0</td>\n",
+                                                                      "      <td>0.031235</td>\n",
+                                                                      "      <td>1.289274</td>\n",
+                                                                      "      <td>-15.371556</td>\n",
+                                                                      "      <td>-0.972916</td>\n",
+                                                                      "      <td>0.043089</td>\n",
+                                                                      "      <td>1.163776</td>\n",
+                                                                      "      <td>7.858989e+00</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_3m_pct</th>\n",
+                                                                      "      <td>320.0</td>\n",
+                                                                      "      <td>0.104482</td>\n",
+                                                                      "      <td>2.384910</td>\n",
+                                                                      "      <td>-19.013071</td>\n",
+                                                                      "      <td>-2.179607</td>\n",
+                                                                      "      <td>0.221818</td>\n",
+                                                                      "      <td>2.210450</td>\n",
+                                                                      "      <td>1.681345e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_6m_pct</th>\n",
+                                                                      "      <td>320.0</td>\n",
+                                                                      "      <td>0.217812</td>\n",
+                                                                      "      <td>3.263535</td>\n",
+                                                                      "      <td>-18.585385</td>\n",
+                                                                      "      <td>-4.273020</td>\n",
+                                                                      "      <td>0.475021</td>\n",
+                                                                      "      <td>3.461830</td>\n",
+                                                                      "      <td>1.967475e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>FEDFUNDS</th>\n",
+                                                                      "      <td>320.0</td>\n",
+                                                                      "      <td>2.048469</td>\n",
+                                                                      "      <td>2.022276</td>\n",
+                                                                      "      <td>0.050000</td>\n",
+                                                                      "      <td>0.080000</td>\n",
+                                                                      "      <td>1.375000</td>\n",
+                                                                      "      <td>5.330000</td>\n",
+                                                                      "      <td>6.540000e+00</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>YC_SPREAD</th>\n",
+                                                                      "      <td>321.0</td>\n",
+                                                                      "      <td>1.041059</td>\n",
+                                                                      "      <td>0.962217</td>\n",
+                                                                      "      <td>-1.060000</td>\n",
+                                                                      "      <td>-0.390000</td>\n",
+                                                                      "      <td>0.990000</td>\n",
+                                                                      "      <td>2.540000</td>\n",
+                                                                      "      <td>2.840000e+00</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>CPIAUCSL</th>\n",
+                                                                      "      <td>319.0</td>\n",
+                                                                      "      <td>235.635251</td>\n",
+                                                                      "      <td>43.461518</td>\n",
+                                                                      "      <td>169.300000</td>\n",
+                                                                      "      <td>177.210000</td>\n",
+                                                                      "      <td>232.282000</td>\n",
+                                                                      "      <td>319.836700</td>\n",
+                                                                      "      <td>3.341310e+02</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>CPI_YOY</th>\n",
+                                                                      "      <td>319.0</td>\n",
+                                                                      "      <td>2.608072</td>\n",
+                                                                      "      <td>1.718826</td>\n",
+                                                                      "      <td>-1.958761</td>\n",
+                                                                      "      <td>0.032414</td>\n",
+                                                                      "      <td>2.420261</td>\n",
+                                                                      "      <td>5.985543</td>\n",
+                                                                      "      <td>8.979361e+00</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>UNRATE</th>\n",
+                                                                      "      <td>319.0</td>\n",
+                                                                      "      <td>5.609404</td>\n",
+                                                                      "      <td>1.932388</td>\n",
+                                                                      "      <td>3.400000</td>\n",
+                                                                      "      <td>3.600000</td>\n",
+                                                                      "      <td>5.000000</td>\n",
+                                                                      "      <td>9.500000</td>\n",
+                                                                      "      <td>1.480000e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ICSA</th>\n",
+                                                                      "      <td>321.0</td>\n",
+                                                                      "      <td>371890.965732</td>\n",
+                                                                      "      <td>391182.698620</td>\n",
+                                                                      "      <td>190000.000000</td>\n",
+                                                                      "      <td>207000.000000</td>\n",
+                                                                      "      <td>318000.000000</td>\n",
+                                                                      "      <td>620000.000000</td>\n",
+                                                                      "      <td>5.946000e+06</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>VIXCLS</th>\n",
+                                                                      "      <td>321.0</td>\n",
+                                                                      "      <td>19.789502</td>\n",
+                                                                      "      <td>7.743577</td>\n",
+                                                                      "      <td>9.510000</td>\n",
+                                                                      "      <td>11.570000</td>\n",
+                                                                      "      <td>17.520000</td>\n",
+                                                                      "      <td>33.400000</td>\n",
+                                                                      "      <td>5.989000e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>HY_SPREAD</th>\n",
+                                                                      "      <td>37.0</td>\n",
+                                                                      "      <td>3.124054</td>\n",
+                                                                      "      <td>0.413545</td>\n",
+                                                                      "      <td>2.630000</td>\n",
+                                                                      "      <td>2.720000</td>\n",
+                                                                      "      <td>2.960000</td>\n",
+                                                                      "      <td>3.958000</td>\n",
+                                                                      "      <td>4.420000e+00</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>SPY_RETURN_3M</th>\n",
+                                                                      "      <td>321.0</td>\n",
+                                                                      "      <td>2.312809</td>\n",
+                                                                      "      <td>7.393917</td>\n",
+                                                                      "      <td>-29.644011</td>\n",
+                                                                      "      <td>-11.358055</td>\n",
+                                                                      "      <td>3.029725</td>\n",
+                                                                      "      <td>12.901699</td>\n",
+                                                                      "      <td>2.605481e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>SPY_RETURN_12M</th>\n",
+                                                                      "      <td>321.0</td>\n",
+                                                                      "      <td>9.746382</td>\n",
+                                                                      "      <td>16.502657</td>\n",
+                                                                      "      <td>-43.424427</td>\n",
+                                                                      "      <td>-22.368144</td>\n",
+                                                                      "      <td>12.869267</td>\n",
+                                                                      "      <td>32.307768</td>\n",
+                                                                      "      <td>5.623112e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>XLI_RETURN_3M</th>\n",
+                                                                      "      <td>321.0</td>\n",
+                                                                      "      <td>2.565862</td>\n",
+                                                                      "      <td>8.861559</td>\n",
+                                                                      "      <td>-34.025255</td>\n",
+                                                                      "      <td>-12.823786</td>\n",
+                                                                      "      <td>3.634125</td>\n",
+                                                                      "      <td>15.494997</td>\n",
+                                                                      "      <td>3.278895e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>XLI_RETURN_12M</th>\n",
+                                                                      "      <td>321.0</td>\n",
+                                                                      "      <td>10.634291</td>\n",
+                                                                      "      <td>18.704832</td>\n",
+                                                                      "      <td>-51.670886</td>\n",
+                                                                      "      <td>-21.875522</td>\n",
+                                                                      "      <td>12.571602</td>\n",
+                                                                      "      <td>36.623220</td>\n",
+                                                                      "      <td>7.344748e+01</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>global_supply_chain_pressure_index</th>\n",
+                                                                      "      <td>320.0</td>\n",
+                                                                      "      <td>0.043906</td>\n",
+                                                                      "      <td>1.027559</td>\n",
+                                                                      "      <td>-1.590000</td>\n",
+                                                                      "      <td>-1.040500</td>\n",
+                                                                      "      <td>-0.185000</td>\n",
+                                                                      "      <td>2.606500</td>\n",
+                                                                      "      <td>4.430000e+00</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                                    count           mean            std  \\\n",
+                                                                      "ipman_change_1m_pct                 320.0       0.031235       1.289274   \n",
+                                                                      "ipman_change_3m_pct                 320.0       0.104482       2.384910   \n",
+                                                                      "ipman_change_6m_pct                 320.0       0.217812       3.263535   \n",
+                                                                      "FEDFUNDS                            320.0       2.048469       2.022276   \n",
+                                                                      "YC_SPREAD                           321.0       1.041059       0.962217   \n",
+                                                                      "CPIAUCSL                            319.0     235.635251      43.461518   \n",
+                                                                      "CPI_YOY                             319.0       2.608072       1.718826   \n",
+                                                                      "UNRATE                              319.0       5.609404       1.932388   \n",
+                                                                      "ICSA                                321.0  371890.965732  391182.698620   \n",
+                                                                      "VIXCLS                              321.0      19.789502       7.743577   \n",
+                                                                      "HY_SPREAD                            37.0       3.124054       0.413545   \n",
+                                                                      "SPY_RETURN_3M                       321.0       2.312809       7.393917   \n",
+                                                                      "SPY_RETURN_12M                      321.0       9.746382      16.502657   \n",
+                                                                      "XLI_RETURN_3M                       321.0       2.565862       8.861559   \n",
+                                                                      "XLI_RETURN_12M                      321.0      10.634291      18.704832   \n",
+                                                                      "global_supply_chain_pressure_index  320.0       0.043906       1.027559   \n",
+                                                                      "\n",
+                                                                      "                                              min             5%  \\\n",
+                                                                      "ipman_change_1m_pct                    -15.371556      -0.972916   \n",
+                                                                      "ipman_change_3m_pct                    -19.013071      -2.179607   \n",
+                                                                      "ipman_change_6m_pct                    -18.585385      -4.273020   \n",
+                                                                      "FEDFUNDS                                 0.050000       0.080000   \n",
+                                                                      "YC_SPREAD                               -1.060000      -0.390000   \n",
+                                                                      "CPIAUCSL                               169.300000     177.210000   \n",
+                                                                      "CPI_YOY                                 -1.958761       0.032414   \n",
+                                                                      "UNRATE                                   3.400000       3.600000   \n",
+                                                                      "ICSA                                190000.000000  207000.000000   \n",
+                                                                      "VIXCLS                                   9.510000      11.570000   \n",
+                                                                      "HY_SPREAD                                2.630000       2.720000   \n",
+                                                                      "SPY_RETURN_3M                          -29.644011     -11.358055   \n",
+                                                                      "SPY_RETURN_12M                         -43.424427     -22.368144   \n",
+                                                                      "XLI_RETURN_3M                          -34.025255     -12.823786   \n",
+                                                                      "XLI_RETURN_12M                         -51.670886     -21.875522   \n",
+                                                                      "global_supply_chain_pressure_index      -1.590000      -1.040500   \n",
+                                                                      "\n",
+                                                                      "                                              50%            95%           max  \n",
+                                                                      "ipman_change_1m_pct                      0.043089       1.163776  7.858989e+00  \n",
+                                                                      "ipman_change_3m_pct                      0.221818       2.210450  1.681345e+01  \n",
+                                                                      "ipman_change_6m_pct                      0.475021       3.461830  1.967475e+01  \n",
+                                                                      "FEDFUNDS                                 1.375000       5.330000  6.540000e+00  \n",
+                                                                      "YC_SPREAD                                0.990000       2.540000  2.840000e+00  \n",
+                                                                      "CPIAUCSL                               232.282000     319.836700  3.341310e+02  \n",
+                                                                      "CPI_YOY                                  2.420261       5.985543  8.979361e+00  \n",
+                                                                      "UNRATE                                   5.000000       9.500000  1.480000e+01  \n",
+                                                                      "ICSA                                318000.000000  620000.000000  5.946000e+06  \n",
+                                                                      "VIXCLS                                  17.520000      33.400000  5.989000e+01  \n",
+                                                                      "HY_SPREAD                                2.960000       3.958000  4.420000e+00  \n",
+                                                                      "SPY_RETURN_3M                            3.029725      12.901699  2.605481e+01  \n",
+                                                                      "SPY_RETURN_12M                          12.869267      32.307768  5.623112e+01  \n",
+                                                                      "XLI_RETURN_3M                            3.634125      15.494997  3.278895e+01  \n",
+                                                                      "XLI_RETURN_12M                          12.571602      36.623220  7.344748e+01  \n",
+                                                                      "global_supply_chain_pressure_index      -0.185000       2.606500  4.430000e+00  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 12,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "summary_statistics = analysis_panel[list(analysis_series_ids)].describe(\n",
+                                        "    percentiles=[0.05, 0.25, 0.5, 0.75, 0.95]\n",
+                                        ").T\n",
+                                        "summary_statistics[[\"count\", \"mean\", \"std\", \"min\", \"5%\", \"50%\", \"95%\", \"max\"]]"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "e1ce8a6f",
+                              "metadata": {
+                                        "id": "e1ce8a6f",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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",
+                                                            "text/plain": [
+                                                                      "<Figure size 1400x1450 with 10 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        }
+                              ],
+                              "source": [
+                                        "distribution_ids = IPMAN_FEATURE_SERIES_IDS + tuple(\n",
+                                        "    series_id for series_id in MACRO_FEATURE_SERIES_IDS if series_id in analysis_panel.columns\n",
+                                        ")\n",
+                                        "rows = int(np.ceil(len(distribution_ids) / 2))\n",
+                                        "fig, axes = plt.subplots(rows, 2, figsize=(14, 2.9 * rows), squeeze=False, layout=\"constrained\")\n",
+                                        "for axis, series_id in zip(axes.flat, distribution_ids, strict=False):\n",
+                                        "    values = analysis_panel[series_id].dropna()\n",
+                                        "    axis.hist(values, bins=24, color=\"#2a6f97\", alpha=0.85)\n",
+                                        "    axis.axvline(values.median(), color=\"#b22222\", linestyle=\"--\", label=\"median\")\n",
+                                        "    axis.set(title=series_id, ylabel=\"Months\")\n",
+                                        "    axis.legend()\n",
+                                        "    axis.grid(axis=\"y\", alpha=0.2)\n",
+                                        "for axis in axes.flat[len(distribution_ids) :]:\n",
+                                        "    axis.set_visible(False)\n",
+                                        "plt.show()"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 14,
+                              "id": "6929c646",
+                              "metadata": {
+                                        "id": "6929c646",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead tr th {\n",
+                                                                      "        text-align: left;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead tr:last-of-type th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th colspan=\"2\" halign=\"left\">months</th>\n",
+                                                                      "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
+                                                                      "      <th colspan=\"2\" halign=\"left\">median</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>regime</th>\n",
+                                                                      "      <th>no_stress</th>\n",
+                                                                      "      <th>stress</th>\n",
+                                                                      "      <th>no_stress</th>\n",
+                                                                      "      <th>stress</th>\n",
+                                                                      "      <th>no_stress</th>\n",
+                                                                      "      <th>stress</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th></th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>CPIAUCSL</th>\n",
+                                                                      "      <td>299.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>236.023726</td>\n",
+                                                                      "      <td>2.298276e+02</td>\n",
+                                                                      "      <td>233.456000</td>\n",
+                                                                      "      <td>217.229000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>CPI_YOY</th>\n",
+                                                                      "      <td>299.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>2.659345</td>\n",
+                                                                      "      <td>1.841541e+00</td>\n",
+                                                                      "      <td>2.470996</td>\n",
+                                                                      "      <td>0.908287</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>FEDFUNDS</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>2.125967</td>\n",
+                                                                      "      <td>8.860000e-01</td>\n",
+                                                                      "      <td>1.530000</td>\n",
+                                                                      "      <td>0.215000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>HY_SPREAD</th>\n",
+                                                                      "      <td>36.0</td>\n",
+                                                                      "      <td>0.0</td>\n",
+                                                                      "      <td>3.135000</td>\n",
+                                                                      "      <td>NaN</td>\n",
+                                                                      "      <td>2.995000</td>\n",
+                                                                      "      <td>NaN</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ICSA</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>327880.000000</td>\n",
+                                                                      "      <td>1.040800e+06</td>\n",
+                                                                      "      <td>312000.000000</td>\n",
+                                                                      "      <td>600500.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>SPY_RETURN_12M</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>11.576986</td>\n",
+                                                                      "      <td>-1.802432e+01</td>\n",
+                                                                      "      <td>13.666769</td>\n",
+                                                                      "      <td>-20.271753</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>SPY_RETURN_3M</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>2.752578</td>\n",
+                                                                      "      <td>-4.298742e+00</td>\n",
+                                                                      "      <td>3.502630</td>\n",
+                                                                      "      <td>-7.792887</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>UNRATE</th>\n",
+                                                                      "      <td>299.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>5.466221</td>\n",
+                                                                      "      <td>7.750000e+00</td>\n",
+                                                                      "      <td>4.900000</td>\n",
+                                                                      "      <td>7.050000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>VIXCLS</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>18.774733</td>\n",
+                                                                      "      <td>3.529000e+01</td>\n",
+                                                                      "      <td>17.070000</td>\n",
+                                                                      "      <td>32.290000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>XLI_RETURN_12M</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>12.851540</td>\n",
+                                                                      "      <td>-2.262600e+01</td>\n",
+                                                                      "      <td>13.493396</td>\n",
+                                                                      "      <td>-21.568725</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>XLI_RETURN_3M</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>3.221800</td>\n",
+                                                                      "      <td>-6.712314e+00</td>\n",
+                                                                      "      <td>3.880771</td>\n",
+                                                                      "      <td>-8.919493</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>YC_SPREAD</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>1.015633</td>\n",
+                                                                      "      <td>1.462000e+00</td>\n",
+                                                                      "      <td>0.945000</td>\n",
+                                                                      "      <td>1.485000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>global_supply_chain_pressure_index</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>0.007633</td>\n",
+                                                                      "      <td>5.880000e-01</td>\n",
+                                                                      "      <td>-0.200000</td>\n",
+                                                                      "      <td>0.270000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_1m_pct</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>0.148638</td>\n",
+                                                                      "      <td>-1.729804e+00</td>\n",
+                                                                      "      <td>0.068005</td>\n",
+                                                                      "      <td>-1.181206</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_3m_pct</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>0.479947</td>\n",
+                                                                      "      <td>-5.527494e+00</td>\n",
+                                                                      "      <td>0.298977</td>\n",
+                                                                      "      <td>-4.645436</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>ipman_change_6m_pct</th>\n",
+                                                                      "      <td>300.0</td>\n",
+                                                                      "      <td>20.0</td>\n",
+                                                                      "      <td>0.789186</td>\n",
+                                                                      "      <td>-8.352793e+00</td>\n",
+                                                                      "      <td>0.622155</td>\n",
+                                                                      "      <td>-8.435298</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                                      months                  mean  \\\n",
+                                                                      "regime                             no_stress stress      no_stress   \n",
+                                                                      "series_id                                                            \n",
+                                                                      "CPIAUCSL                               299.0   20.0     236.023726   \n",
+                                                                      "CPI_YOY                                299.0   20.0       2.659345   \n",
+                                                                      "FEDFUNDS                               300.0   20.0       2.125967   \n",
+                                                                      "HY_SPREAD                               36.0    0.0       3.135000   \n",
+                                                                      "ICSA                                   300.0   20.0  327880.000000   \n",
+                                                                      "SPY_RETURN_12M                         300.0   20.0      11.576986   \n",
+                                                                      "SPY_RETURN_3M                          300.0   20.0       2.752578   \n",
+                                                                      "UNRATE                                 299.0   20.0       5.466221   \n",
+                                                                      "VIXCLS                                 300.0   20.0      18.774733   \n",
+                                                                      "XLI_RETURN_12M                         300.0   20.0      12.851540   \n",
+                                                                      "XLI_RETURN_3M                          300.0   20.0       3.221800   \n",
+                                                                      "YC_SPREAD                              300.0   20.0       1.015633   \n",
+                                                                      "global_supply_chain_pressure_index     300.0   20.0       0.007633   \n",
+                                                                      "ipman_change_1m_pct                    300.0   20.0       0.148638   \n",
+                                                                      "ipman_change_3m_pct                    300.0   20.0       0.479947   \n",
+                                                                      "ipman_change_6m_pct                    300.0   20.0       0.789186   \n",
+                                                                      "\n",
+                                                                      "                                                         median                 \n",
+                                                                      "regime                                    stress      no_stress         stress  \n",
+                                                                      "series_id                                                                       \n",
+                                                                      "CPIAUCSL                            2.298276e+02     233.456000     217.229000  \n",
+                                                                      "CPI_YOY                             1.841541e+00       2.470996       0.908287  \n",
+                                                                      "FEDFUNDS                            8.860000e-01       1.530000       0.215000  \n",
+                                                                      "HY_SPREAD                                    NaN       2.995000            NaN  \n",
+                                                                      "ICSA                                1.040800e+06  312000.000000  600500.000000  \n",
+                                                                      "SPY_RETURN_12M                     -1.802432e+01      13.666769     -20.271753  \n",
+                                                                      "SPY_RETURN_3M                      -4.298742e+00       3.502630      -7.792887  \n",
+                                                                      "UNRATE                              7.750000e+00       4.900000       7.050000  \n",
+                                                                      "VIXCLS                              3.529000e+01      17.070000      32.290000  \n",
+                                                                      "XLI_RETURN_12M                     -2.262600e+01      13.493396     -21.568725  \n",
+                                                                      "XLI_RETURN_3M                      -6.712314e+00       3.880771      -8.919493  \n",
+                                                                      "YC_SPREAD                           1.462000e+00       0.945000       1.485000  \n",
+                                                                      "global_supply_chain_pressure_index  5.880000e-01      -0.200000       0.270000  \n",
+                                                                      "ipman_change_1m_pct                -1.729804e+00       0.068005      -1.181206  \n",
+                                                                      "ipman_change_3m_pct                -5.527494e+00       0.298977      -4.645436  \n",
+                                                                      "ipman_change_6m_pct                -8.352793e+00       0.622155      -8.435298  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 14,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "regime_rows = []\n",
+                                        "for series_id in analysis_series_ids:\n",
+                                        "    paired = analysis_panel[[series_id, \"target\"]].dropna()\n",
+                                        "    for regime_value, regime_name in ((0, \"no_stress\"), (1, \"stress\")):\n",
+                                        "        values = paired.loc[paired[\"target\"] == regime_value, series_id]\n",
+                                        "        regime_rows.append(\n",
+                                        "            {\n",
+                                        "                \"series_id\": series_id,\n",
+                                        "                \"regime\": regime_name,\n",
+                                        "                \"months\": len(values),\n",
+                                        "                \"mean\": values.mean(),\n",
+                                        "                \"median\": values.median(),\n",
+                                        "                \"std\": values.std(),\n",
+                                        "            }\n",
+                                        "        )\n",
+                                        "regime_summary = pd.DataFrame(regime_rows)\n",
+                                        "regime_summary.pivot(index=\"series_id\", columns=\"regime\", values=[\"months\", \"mean\", \"median\"])"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "660e7fb1",
+                              "metadata": {
+                                        "id": "660e7fb1",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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+                                                            "text/plain": [
+                                                                      "<Figure size 1400x700 with 2 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        }
+                              ],
+                              "source": [
+                                        "rolling_ids = (IPMAN_CHANGE_3M_SERIES_ID,) + tuple(\n",
+                                        "    series_id for series_id in MACRO_FEATURE_SERIES_IDS if series_id in analysis_panel.columns\n",
+                                        ")\n",
+                                        "fig, axes = plt.subplots(2, 1, figsize=(14, 7), sharex=True, layout=\"constrained\")\n",
+                                        "for series_id in rolling_ids:\n",
+                                        "    axes[0].plot(\n",
+                                        "        analysis_panel.index,\n",
+                                        "        analysis_panel[series_id].rolling(ROLLING_WINDOW_MONTHS, min_periods=12).mean(),\n",
+                                        "        linewidth=1.25,\n",
+                                        "        label=series_id,\n",
+                                        "    )\n",
+                                        "axes[0].set(title=f\"{ROLLING_WINDOW_MONTHS}-month rolling means\", ylabel=\"Native units\")\n",
+                                        "axes[0].legend(ncol=3)\n",
+                                        "axes[0].grid(alpha=0.25)\n",
+                                        "axes[1].plot(\n",
+                                        "    analysis_panel.index,\n",
+                                        "    analysis_panel[\"target\"].rolling(ROLLING_WINDOW_MONTHS, min_periods=12).mean(),\n",
+                                        "    color=\"#b22222\",\n",
+                                        ")\n",
+                                        "axes[1].set(title=f\"{ROLLING_WINDOW_MONTHS}-month manufacturing-stress rate\", xlabel=\"Reference month\", ylabel=\"Share of months\")\n",
+                                        "axes[1].set_ylim(bottom=0)\n",
+                                        "axes[1].grid(alpha=0.25)\n",
+                                        "plt.show()"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "279b8b15",
+                              "metadata": {
+                                        "id": "279b8b15",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Contemporaneous correlations\n",
+                                        "\n",
+                                        "Pearson summarizes linear co-movement; Spearman summarizes rank co-movement. Both require overlapping observed months and neither identifies causality. The target association is equivalent to a point-biserial correlation when using Pearson because the target is binary."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 16,
+                              "id": "f6f8a763",
+                              "metadata": {
+                                        "id": "f6f8a763",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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+                                                            "text/plain": [
+                                                                      "<Figure size 2000x800 with 4 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        }
+                              ],
+                              "source": [
+                                        "correlation_data = analysis_panel[list(analysis_series_ids)]\n",
+                                        "pearson_correlation = correlation_data.corr(method=\"pearson\", min_periods=36)\n",
+                                        "spearman_correlation = correlation_data.corr(method=\"spearman\", min_periods=36)\n",
+                                        "\n",
+                                        "fig, axes = plt.subplots(1, 2, figsize=(20, 8), layout=\"constrained\")\n",
+                                        "for axis, matrix, title in zip(\n",
+                                        "    axes,\n",
+                                        "    (pearson_correlation, spearman_correlation),\n",
+                                        "    (\"Pearson correlation\", \"Spearman correlation\"),\n",
+                                        "    strict=False,\n",
+                                        "):\n",
+                                        "    image = axis.imshow(matrix, vmin=-1, vmax=1, cmap=\"RdBu_r\")\n",
+                                        "    axis.set_xticks(range(len(matrix.columns)), matrix.columns, rotation=90, fontsize=8)\n",
+                                        "    axis.set_yticks(range(len(matrix.index)), matrix.index, fontsize=8)\n",
+                                        "    axis.set_title(title)\n",
+                                        "    fig.colorbar(image, ax=axis, shrink=0.8)\n",
+                                        "plt.show()"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 17,
+                              "id": "5362e697",
+                              "metadata": {
+                                        "id": "5362e697",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th>overlapping_months</th>\n",
+                                                                      "      <th>stress_months</th>\n",
+                                                                      "      <th>pearson_with_target</th>\n",
+                                                                      "      <th>spearman_with_target</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>2</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.679137</td>\n",
+                                                                      "      <td>-0.408783</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.610693</td>\n",
+                                                                      "      <td>-0.419265</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>9</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>0.516682</td>\n",
+                                                                      "      <td>0.344217</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>14</th>\n",
+                                                                      "      <td>XLI_RETURN_12M</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.459119</td>\n",
+                                                                      "      <td>-0.342819</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>8</th>\n",
+                                                                      "      <td>ICSA</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>0.441289</td>\n",
+                                                                      "      <td>0.333253</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>12</th>\n",
+                                                                      "      <td>SPY_RETURN_12M</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.434290</td>\n",
+                                                                      "      <td>-0.314309</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.353230</td>\n",
+                                                                      "      <td>-0.297538</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>7</th>\n",
+                                                                      "      <td>UNRATE</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>0.286947</td>\n",
+                                                                      "      <td>0.227658</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>13</th>\n",
+                                                                      "      <td>XLI_RETURN_3M</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.272043</td>\n",
+                                                                      "      <td>-0.169383</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>11</th>\n",
+                                                                      "      <td>SPY_RETURN_3M</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.230846</td>\n",
+                                                                      "      <td>-0.148979</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>3</th>\n",
+                                                                      "      <td>FEDFUNDS</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.148653</td>\n",
+                                                                      "      <td>-0.129382</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>15</th>\n",
+                                                                      "      <td>global_supply_chain_pressure_index</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>0.136931</td>\n",
+                                                                      "      <td>0.110899</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>6</th>\n",
+                                                                      "      <td>CPI_YOY</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.115520</td>\n",
+                                                                      "      <td>-0.118240</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>4</th>\n",
+                                                                      "      <td>YC_SPREAD</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>0.112410</td>\n",
+                                                                      "      <td>0.116137</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>5</th>\n",
+                                                                      "      <td>CPIAUCSL</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>20</td>\n",
+                                                                      "      <td>-0.034615</td>\n",
+                                                                      "      <td>-0.023943</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>10</th>\n",
+                                                                      "      <td>HY_SPREAD</td>\n",
+                                                                      "      <td>36</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>NaN</td>\n",
+                                                                      "      <td>NaN</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                             series_id  overlapping_months  stress_months  \\\n",
+                                                                      "2                  ipman_change_6m_pct                 320             20   \n",
+                                                                      "1                  ipman_change_3m_pct                 320             20   \n",
+                                                                      "9                               VIXCLS                 320             20   \n",
+                                                                      "14                      XLI_RETURN_12M                 320             20   \n",
+                                                                      "8                                 ICSA                 320             20   \n",
+                                                                      "12                      SPY_RETURN_12M                 320             20   \n",
+                                                                      "0                  ipman_change_1m_pct                 320             20   \n",
+                                                                      "7                               UNRATE                 319             20   \n",
+                                                                      "13                       XLI_RETURN_3M                 320             20   \n",
+                                                                      "11                       SPY_RETURN_3M                 320             20   \n",
+                                                                      "3                             FEDFUNDS                 320             20   \n",
+                                                                      "15  global_supply_chain_pressure_index                 320             20   \n",
+                                                                      "6                              CPI_YOY                 319             20   \n",
+                                                                      "4                            YC_SPREAD                 320             20   \n",
+                                                                      "5                             CPIAUCSL                 319             20   \n",
+                                                                      "10                           HY_SPREAD                  36              0   \n",
+                                                                      "\n",
+                                                                      "    pearson_with_target  spearman_with_target  \n",
+                                                                      "2             -0.679137             -0.408783  \n",
+                                                                      "1             -0.610693             -0.419265  \n",
+                                                                      "9              0.516682              0.344217  \n",
+                                                                      "14            -0.459119             -0.342819  \n",
+                                                                      "8              0.441289              0.333253  \n",
+                                                                      "12            -0.434290             -0.314309  \n",
+                                                                      "0             -0.353230             -0.297538  \n",
+                                                                      "7              0.286947              0.227658  \n",
+                                                                      "13            -0.272043             -0.169383  \n",
+                                                                      "11            -0.230846             -0.148979  \n",
+                                                                      "3             -0.148653             -0.129382  \n",
+                                                                      "15             0.136931              0.110899  \n",
+                                                                      "6             -0.115520             -0.118240  \n",
+                                                                      "4              0.112410              0.116137  \n",
+                                                                      "5             -0.034615             -0.023943  \n",
+                                                                      "10                  NaN                   NaN  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 17,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "target_associations = []\n",
+                                        "for series_id in analysis_series_ids:\n",
+                                        "    paired = analysis_panel[[series_id, \"target\"]].dropna()\n",
+                                        "    target_associations.append(\n",
+                                        "        {\n",
+                                        "            \"series_id\": series_id,\n",
+                                        "            \"overlapping_months\": len(paired),\n",
+                                        "            \"stress_months\": int(paired[\"target\"].sum()),\n",
+                                        "            \"pearson_with_target\": paired.corr(method=\"pearson\").iloc[0, 1],\n",
+                                        "            \"spearman_with_target\": paired.corr(method=\"spearman\").iloc[0, 1],\n",
+                                        "        }\n",
+                                        "    )\n",
+                                        "target_associations = pd.DataFrame(target_associations).sort_values(\"pearson_with_target\", key=np.abs, ascending=False)\n",
+                                        "target_associations"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "e4ef704b",
+                              "metadata": {
+                                        "id": "e4ef704b",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Lagged cross-correlations\n",
+                                        "\n",
+                                        "For a lag $k$, this section correlates the feature observed in month $t$ with the outcome at $t+k$. Positive lags are the forecast-relevant direction: for example, `lag_months = 3` compares a month-$t$ feature with manufacturing stress or IPMAN momentum three months later. Negative lags are included as diagnostic context only."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 18,
+                              "id": "acf3aaf0",
+                              "metadata": {
+                                        "id": "acf3aaf0",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th>outcome</th>\n",
+                                                                      "      <th>lag_months</th>\n",
+                                                                      "      <th>overlapping_months</th>\n",
+                                                                      "      <th>pearson_correlation</th>\n",
+                                                                      "      <th>spearman_correlation</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>111</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>1.000000</td>\n",
+                                                                      "      <td>1.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>39</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>1</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>0.713129</td>\n",
+                                                                      "      <td>0.551374</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>184</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>-0.679137</td>\n",
+                                                                      "      <td>-0.408783</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>113</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>1</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>0.677362</td>\n",
+                                                                      "      <td>0.703910</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>185</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>0.642045</td>\n",
+                                                                      "      <td>0.757187</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>110</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>-0.610693</td>\n",
+                                                                      "      <td>-0.419265</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>186</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>1</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>-0.598950</td>\n",
+                                                                      "      <td>-0.357949</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>41</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>2</td>\n",
+                                                                      "      <td>318</td>\n",
+                                                                      "      <td>0.588770</td>\n",
+                                                                      "      <td>0.635415</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>112</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>1</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>-0.545659</td>\n",
+                                                                      "      <td>-0.355000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>37</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>0.533766</td>\n",
+                                                                      "      <td>0.603456</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>704</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>1</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>0.533295</td>\n",
+                                                                      "      <td>0.340467</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>637</th>\n",
+                                                                      "      <td>ICSA</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>4</td>\n",
+                                                                      "      <td>316</td>\n",
+                                                                      "      <td>0.524044</td>\n",
+                                                                      "      <td>0.266029</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>702</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>0.516682</td>\n",
+                                                                      "      <td>0.344217</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>706</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>2</td>\n",
+                                                                      "      <td>318</td>\n",
+                                                                      "      <td>0.498223</td>\n",
+                                                                      "      <td>0.316217</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>188</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>2</td>\n",
+                                                                      "      <td>318</td>\n",
+                                                                      "      <td>-0.490890</td>\n",
+                                                                      "      <td>-0.331738</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>793</th>\n",
+                                                                      "      <td>HY_SPREAD</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>8</td>\n",
+                                                                      "      <td>28</td>\n",
+                                                                      "      <td>-0.486318</td>\n",
+                                                                      "      <td>-0.511017</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1072</th>\n",
+                                                                      "      <td>XLI_RETURN_12M</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>0</td>\n",
+                                                                      "      <td>320</td>\n",
+                                                                      "      <td>-0.459119</td>\n",
+                                                                      "      <td>-0.342819</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>187</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>1</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>0.458312</td>\n",
+                                                                      "      <td>0.598340</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1001</th>\n",
+                                                                      "      <td>XLI_RETURN_3M</td>\n",
+                                                                      "      <td>ipman_change_3m</td>\n",
+                                                                      "      <td>1</td>\n",
+                                                                      "      <td>319</td>\n",
+                                                                      "      <td>0.458129</td>\n",
+                                                                      "      <td>0.272851</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>708</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>target</td>\n",
+                                                                      "      <td>3</td>\n",
+                                                                      "      <td>317</td>\n",
+                                                                      "      <td>0.451206</td>\n",
+                                                                      "      <td>0.277764</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "                series_id          outcome  lag_months  overlapping_months  \\\n",
+                                                                      "111   ipman_change_3m_pct  ipman_change_3m           0                 320   \n",
+                                                                      "39    ipman_change_1m_pct  ipman_change_3m           1                 319   \n",
+                                                                      "184   ipman_change_6m_pct           target           0                 320   \n",
+                                                                      "113   ipman_change_3m_pct  ipman_change_3m           1                 319   \n",
+                                                                      "185   ipman_change_6m_pct  ipman_change_3m           0                 320   \n",
+                                                                      "110   ipman_change_3m_pct           target           0                 320   \n",
+                                                                      "186   ipman_change_6m_pct           target           1                 319   \n",
+                                                                      "41    ipman_change_1m_pct  ipman_change_3m           2                 318   \n",
+                                                                      "112   ipman_change_3m_pct           target           1                 319   \n",
+                                                                      "37    ipman_change_1m_pct  ipman_change_3m           0                 320   \n",
+                                                                      "704                VIXCLS           target           1                 319   \n",
+                                                                      "637                  ICSA  ipman_change_3m           4                 316   \n",
+                                                                      "702                VIXCLS           target           0                 320   \n",
+                                                                      "706                VIXCLS           target           2                 318   \n",
+                                                                      "188   ipman_change_6m_pct           target           2                 318   \n",
+                                                                      "793             HY_SPREAD  ipman_change_3m           8                  28   \n",
+                                                                      "1072       XLI_RETURN_12M           target           0                 320   \n",
+                                                                      "187   ipman_change_6m_pct  ipman_change_3m           1                 319   \n",
+                                                                      "1001        XLI_RETURN_3M  ipman_change_3m           1                 319   \n",
+                                                                      "708                VIXCLS           target           3                 317   \n",
+                                                                      "\n",
+                                                                      "      pearson_correlation  spearman_correlation  \n",
+                                                                      "111              1.000000              1.000000  \n",
+                                                                      "39               0.713129              0.551374  \n",
+                                                                      "184             -0.679137             -0.408783  \n",
+                                                                      "113              0.677362              0.703910  \n",
+                                                                      "185              0.642045              0.757187  \n",
+                                                                      "110             -0.610693             -0.419265  \n",
+                                                                      "186             -0.598950             -0.357949  \n",
+                                                                      "41               0.588770              0.635415  \n",
+                                                                      "112             -0.545659             -0.355000  \n",
+                                                                      "37               0.533766              0.603456  \n",
+                                                                      "704              0.533295              0.340467  \n",
+                                                                      "637              0.524044              0.266029  \n",
+                                                                      "702              0.516682              0.344217  \n",
+                                                                      "706              0.498223              0.316217  \n",
+                                                                      "188             -0.490890             -0.331738  \n",
+                                                                      "793             -0.486318             -0.511017  \n",
+                                                                      "1072            -0.459119             -0.342819  \n",
+                                                                      "187              0.458312              0.598340  \n",
+                                                                      "1001             0.458129              0.272851  \n",
+                                                                      "708              0.451206              0.277764  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 18,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "lag_rows = []\n",
+                                        "for series_id in analysis_series_ids:\n",
+                                        "    feature = analysis_panel[series_id]\n",
+                                        "    for lag in range(-CORRELATION_LAG_MONTHS, CORRELATION_LAG_MONTHS + 1):\n",
+                                        "        for outcome_name in (\"target\", \"ipman_change_3m\"):\n",
+                                        "            paired = pd.concat(\n",
+                                        "                [feature.rename(\"feature\"), analysis_panel[outcome_name].shift(-lag).rename(\"outcome\")],\n",
+                                        "                axis=1,\n",
+                                        "            ).dropna()\n",
+                                        "            lag_rows.append(\n",
+                                        "                {\n",
+                                        "                    \"series_id\": series_id,\n",
+                                        "                    \"outcome\": outcome_name,\n",
+                                        "                    \"lag_months\": lag,\n",
+                                        "                    \"overlapping_months\": len(paired),\n",
+                                        "                    \"pearson_correlation\": paired.corr(method=\"pearson\").iloc[0, 1] if len(paired) >= 12 else np.nan,\n",
+                                        "                    \"spearman_correlation\": paired.corr(method=\"spearman\").iloc[0, 1] if len(paired) >= 12 else np.nan,\n",
+                                        "                }\n",
+                                        "            )\n",
+                                        "lag_correlations = pd.DataFrame(lag_rows)\n",
+                                        "forecast_relevant_lags = lag_correlations.query(\"lag_months >= 0\").sort_values(\n",
+                                        "    \"pearson_correlation\", key=np.abs, ascending=False\n",
+                                        ")\n",
+                                        "forecast_relevant_lags.head(20)"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 19,
+                              "id": "7ef695cd",
+                              "metadata": {
+                                        "id": "7ef695cd",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "image/png": 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",
+                                                            "text/plain": [
+                                                                      "<Figure size 1700x1300 with 16 Axes>"
+                                                            ]
+                                                  },
+                                                  "metadata": {},
+                                                  "output_type": "display_data"
+                                        }
+                              ],
+                              "source": [
+                                        "fig, axes = plt.subplots(4, 4, figsize=(17, 13), sharex=True, sharey=True, layout=\"constrained\")\n",
+                                        "for axis, series_id in zip(axes.flat, analysis_series_ids, strict=False):\n",
+                                        "    subset = lag_correlations.query(\"series_id == @series_id and outcome == 'target'\")\n",
+                                        "    axis.plot(subset[\"lag_months\"], subset[\"pearson_correlation\"], color=\"#2a6f97\")\n",
+                                        "    axis.axvline(0, color=\"black\", linewidth=0.8)\n",
+                                        "    axis.axvline(3, color=\"#b22222\", linestyle=\"--\", linewidth=0.8)\n",
+                                        "    axis.axhline(0, color=\"black\", linewidth=0.6)\n",
+                                        "    axis.set_title(series_id, fontsize=9)\n",
+                                        "    axis.grid(alpha=0.2)\n",
+                                        "for axis in axes.flat[len(analysis_series_ids) :]:\n",
+                                        "    axis.set_visible(False)\n",
+                                        "fig.supxlabel(\"Lag in months: feature at t versus target at t + lag\")\n",
+                                        "fig.supylabel(\"Pearson correlation\")\n",
+                                        "fig.suptitle(\"Feature-to-future-stress cross-correlations; dashed line marks +3 months\")\n",
+                                        "plt.show()"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "4b797351",
+                              "metadata": {
+                                        "id": "4b797351",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Small-sample robustness check\n",
+                                        "\n",
+                                        "Only 20 of 320 observed months are labeled stress, so a target correlation can be dominated by a handful of episodes. For the top non-mechanical candidates (excluding the IPMAN-derived features, which are related to the target by construction), this section reports a bootstrap confidence interval on the point-biserial correlation and a leave-one-stress-month-out sensitivity range. A wide bootstrap interval, a sign flip under leave-one-out, or a large leave-one-out swing all indicate the descriptive correlation is fragile and should not be treated as a strong feature-selection signal on its own."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 29,
+                              "id": "78aaceab",
+                              "metadata": {
+                                        "id": "78aaceab",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "data": {
+                                                            "text/html": [
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+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th>point_estimate</th>\n",
+                                                                      "      <th>bootstrap_ci_low</th>\n",
+                                                                      "      <th>bootstrap_ci_high</th>\n",
+                                                                      "      <th>loo_min</th>\n",
+                                                                      "      <th>loo_max</th>\n",
+                                                                      "      <th>loo_swing</th>\n",
+                                                                      "      <th>sign_stable_under_loo</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>5</th>\n",
+                                                                      "      <td>XLI_RETURN_3M</td>\n",
+                                                                      "      <td>-0.272043</td>\n",
+                                                                      "      <td>-0.471106</td>\n",
+                                                                      "      <td>-0.053204</td>\n",
+                                                                      "      <td>-0.327186</td>\n",
+                                                                      "      <td>-0.233512</td>\n",
+                                                                      "      <td>0.093674</td>\n",
+                                                                      "      <td>True</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>4</th>\n",
+                                                                      "      <td>UNRATE</td>\n",
+                                                                      "      <td>0.286947</td>\n",
+                                                                      "      <td>0.140489</td>\n",
+                                                                      "      <td>0.425702</td>\n",
+                                                                      "      <td>0.243455</td>\n",
+                                                                      "      <td>0.308137</td>\n",
+                                                                      "      <td>0.064681</td>\n",
+                                                                      "      <td>True</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>2</th>\n",
+                                                                      "      <td>ICSA</td>\n",
+                                                                      "      <td>0.441289</td>\n",
+                                                                      "      <td>0.330613</td>\n",
+                                                                      "      <td>0.579483</td>\n",
+                                                                      "      <td>0.394551</td>\n",
+                                                                      "      <td>0.457501</td>\n",
+                                                                      "      <td>0.062950</td>\n",
+                                                                      "      <td>True</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>3</th>\n",
+                                                                      "      <td>SPY_RETURN_12M</td>\n",
+                                                                      "      <td>-0.434290</td>\n",
+                                                                      "      <td>-0.556678</td>\n",
+                                                                      "      <td>-0.282467</td>\n",
+                                                                      "      <td>-0.461930</td>\n",
+                                                                      "      <td>-0.411560</td>\n",
+                                                                      "      <td>0.050370</td>\n",
+                                                                      "      <td>True</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1</th>\n",
+                                                                      "      <td>XLI_RETURN_12M</td>\n",
+                                                                      "      <td>-0.459119</td>\n",
+                                                                      "      <td>-0.573176</td>\n",
+                                                                      "      <td>-0.325573</td>\n",
+                                                                      "      <td>-0.481412</td>\n",
+                                                                      "      <td>-0.436542</td>\n",
+                                                                      "      <td>0.044871</td>\n",
+                                                                      "      <td>True</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>0.516682</td>\n",
+                                                                      "      <td>0.366911</td>\n",
+                                                                      "      <td>0.637856</td>\n",
+                                                                      "      <td>0.485727</td>\n",
+                                                                      "      <td>0.527930</td>\n",
+                                                                      "      <td>0.042203</td>\n",
+                                                                      "      <td>True</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "        series_id  point_estimate  bootstrap_ci_low  bootstrap_ci_high  \\\n",
+                                                                      "5   XLI_RETURN_3M       -0.272043         -0.471106          -0.053204   \n",
+                                                                      "4          UNRATE        0.286947          0.140489           0.425702   \n",
+                                                                      "2            ICSA        0.441289          0.330613           0.579483   \n",
+                                                                      "3  SPY_RETURN_12M       -0.434290         -0.556678          -0.282467   \n",
+                                                                      "1  XLI_RETURN_12M       -0.459119         -0.573176          -0.325573   \n",
+                                                                      "0          VIXCLS        0.516682          0.366911           0.637856   \n",
+                                                                      "\n",
+                                                                      "    loo_min   loo_max  loo_swing  sign_stable_under_loo  \n",
+                                                                      "5 -0.327186 -0.233512   0.093674                   True  \n",
+                                                                      "4  0.243455  0.308137   0.064681                   True  \n",
+                                                                      "2  0.394551  0.457501   0.062950                   True  \n",
+                                                                      "3 -0.461930 -0.411560   0.050370                   True  \n",
+                                                                      "1 -0.481412 -0.436542   0.044871                   True  \n",
+                                                                      "0  0.485727  0.527930   0.042203                   True  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 29,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "N_BOOTSTRAP = 2000\n",
+                                        "robustness_rng = np.random.default_rng(0)\n",
+                                        "\n",
+                                        "\n",
+                                        "def point_biserial(paired: pd.DataFrame) -> float:\n",
+                                        "    return paired.corr(method=\"pearson\").iloc[0, 1]\n",
+                                        "\n",
+                                        "\n",
+                                        "robustness_candidates = (\n",
+                                        "    target_associations.query(\"series_id not in @IPMAN_FEATURE_SERIES_IDS and stress_months > 0\")\n",
+                                        "    .head(6)[\"series_id\"]\n",
+                                        "    .tolist()\n",
+                                        ")\n",
+                                        "\n",
+                                        "robustness_rows = []\n",
+                                        "for series_id in robustness_candidates:\n",
+                                        "    paired = analysis_panel[[series_id, \"target\"]].dropna()\n",
+                                        "    point_estimate = point_biserial(paired)\n",
+                                        "\n",
+                                        "    bootstrap_estimates = np.array(\n",
+                                        "        [\n",
+                                        "            point_biserial(paired.sample(n=len(paired), replace=True, random_state=robustness_rng.integers(0, 1_000_000)))\n",
+                                        "            for _ in range(N_BOOTSTRAP)\n",
+                                        "        ]\n",
+                                        "    )\n",
+                                        "\n",
+                                        "    stress_timestamps = paired.index[paired[\"target\"] == 1]\n",
+                                        "    loo_estimates = np.array(\n",
+                                        "        [point_biserial(paired.drop(index=stress_timestamp)) for stress_timestamp in stress_timestamps]\n",
+                                        "    )\n",
+                                        "\n",
+                                        "    robustness_rows.append(\n",
+                                        "        {\n",
+                                        "            \"series_id\": series_id,\n",
+                                        "            \"point_estimate\": point_estimate,\n",
+                                        "            \"bootstrap_ci_low\": np.percentile(bootstrap_estimates, 2.5),\n",
+                                        "            \"bootstrap_ci_high\": np.percentile(bootstrap_estimates, 97.5),\n",
+                                        "            \"loo_min\": loo_estimates.min(),\n",
+                                        "            \"loo_max\": loo_estimates.max(),\n",
+                                        "            \"loo_swing\": loo_estimates.max() - loo_estimates.min(),\n",
+                                        "            \"sign_stable_under_loo\": bool(np.all(np.sign(loo_estimates) == np.sign(point_estimate))),\n",
+                                        "        }\n",
+                                        "    )\n",
+                                        "\n",
+                                        "robustness_summary = pd.DataFrame(robustness_rows).sort_values(\"loo_swing\", ascending=False)\n",
+                                        "robustness_summary\n"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "3e9eceec",
+                              "metadata": {
+                                        "id": "3e9eceec",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Point-in-time feature availability\n",
+                                        "\n",
+                                        "The preceding descriptive panel aligns reference months. A forecasting origin must instead see only records whose `released_at` is on or before that origin. This reproduces the fit-at-origin feature lookup used by the statistical predictors."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 21,
+                              "id": "ce25236d",
+                              "metadata": {
+                                        "id": "ce25236d",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "Latest complete active-feature snapshot: 2026-09-01\n"
+                                                  ]
+                                        },
+                                        {
+                                                  "data": {
+                                                            "text/html": [
+                                                                      "<div>\n",
+                                                                      "<style scoped>\n",
+                                                                      "    .dataframe tbody tr th:only-of-type {\n",
+                                                                      "        vertical-align: middle;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe tbody tr th {\n",
+                                                                      "        vertical-align: top;\n",
+                                                                      "    }\n",
+                                                                      "\n",
+                                                                      "    .dataframe thead th {\n",
+                                                                      "        text-align: right;\n",
+                                                                      "    }\n",
+                                                                      "</style>\n",
+                                                                      "<table border=\"1\" class=\"dataframe\">\n",
+                                                                      "  <thead>\n",
+                                                                      "    <tr style=\"text-align: right;\">\n",
+                                                                      "      <th></th>\n",
+                                                                      "      <th>series_id</th>\n",
+                                                                      "      <th>origin</th>\n",
+                                                                      "      <th>latest_visible_reference_month</th>\n",
+                                                                      "      <th>latest_visible_release_date</th>\n",
+                                                                      "      <th>snapshot_value</th>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </thead>\n",
+                                                                      "  <tbody>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>5</th>\n",
+                                                                      "      <td>CPIAUCSL</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "      <td>334.131000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>6</th>\n",
+                                                                      "      <td>CPI_YOY</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "      <td>3.712958</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>3</th>\n",
+                                                                      "      <td>FEDFUNDS</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "      <td>3.630000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>10</th>\n",
+                                                                      "      <td>HY_SPREAD</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2.630000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>8</th>\n",
+                                                                      "      <td>ICSA</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-08-31</td>\n",
+                                                                      "      <td>207000.000000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>12</th>\n",
+                                                                      "      <td>SPY_RETURN_12M</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>20.230850</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>11</th>\n",
+                                                                      "      <td>SPY_RETURN_3M</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>1.658482</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>7</th>\n",
+                                                                      "      <td>UNRATE</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-08-03</td>\n",
+                                                                      "      <td>4.100000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>9</th>\n",
+                                                                      "      <td>VIXCLS</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>14.920000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>14</th>\n",
+                                                                      "      <td>XLI_RETURN_12M</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>16.694316</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>13</th>\n",
+                                                                      "      <td>XLI_RETURN_3M</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>1.404076</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>4</th>\n",
+                                                                      "      <td>YC_SPREAD</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>0.410000</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>0</th>\n",
+                                                                      "      <td>ipman_change_1m_pct</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>-0.300618</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>1</th>\n",
+                                                                      "      <td>ipman_change_3m_pct</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>0.118004</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "    <tr>\n",
+                                                                      "      <th>2</th>\n",
+                                                                      "      <td>ipman_change_6m_pct</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>2026-08-01</td>\n",
+                                                                      "      <td>2026-09-01</td>\n",
+                                                                      "      <td>1.395025</td>\n",
+                                                                      "    </tr>\n",
+                                                                      "  </tbody>\n",
+                                                                      "</table>\n",
+                                                                      "</div>"
+                                                            ],
+                                                            "text/plain": [
+                                                                      "              series_id     origin latest_visible_reference_month  \\\n",
+                                                                      "5              CPIAUCSL 2026-09-01                     2026-08-01   \n",
+                                                                      "6               CPI_YOY 2026-09-01                     2026-08-01   \n",
+                                                                      "3              FEDFUNDS 2026-09-01                     2026-08-01   \n",
+                                                                      "10            HY_SPREAD 2026-09-01                     2026-08-01   \n",
+                                                                      "8                  ICSA 2026-09-01                     2026-08-01   \n",
+                                                                      "12       SPY_RETURN_12M 2026-09-01                     2026-08-01   \n",
+                                                                      "11        SPY_RETURN_3M 2026-09-01                     2026-08-01   \n",
+                                                                      "7                UNRATE 2026-09-01                     2026-08-01   \n",
+                                                                      "9                VIXCLS 2026-09-01                     2026-08-01   \n",
+                                                                      "14       XLI_RETURN_12M 2026-09-01                     2026-08-01   \n",
+                                                                      "13        XLI_RETURN_3M 2026-09-01                     2026-08-01   \n",
+                                                                      "4             YC_SPREAD 2026-09-01                     2026-08-01   \n",
+                                                                      "0   ipman_change_1m_pct 2026-09-01                     2026-08-01   \n",
+                                                                      "1   ipman_change_3m_pct 2026-09-01                     2026-08-01   \n",
+                                                                      "2   ipman_change_6m_pct 2026-09-01                     2026-08-01   \n",
+                                                                      "\n",
+                                                                      "   latest_visible_release_date  snapshot_value  \n",
+                                                                      "5                   2026-08-03      334.131000  \n",
+                                                                      "6                   2026-08-03        3.712958  \n",
+                                                                      "3                   2026-08-03        3.630000  \n",
+                                                                      "10                  2026-09-01        2.630000  \n",
+                                                                      "8                   2026-08-31   207000.000000  \n",
+                                                                      "12                  2026-09-01       20.230850  \n",
+                                                                      "11                  2026-09-01        1.658482  \n",
+                                                                      "7                   2026-08-03        4.100000  \n",
+                                                                      "9                   2026-09-01       14.920000  \n",
+                                                                      "14                  2026-09-01       16.694316  \n",
+                                                                      "13                  2026-09-01        1.404076  \n",
+                                                                      "4                   2026-09-01        0.410000  \n",
+                                                                      "0                   2026-09-01       -0.300618  \n",
+                                                                      "1                   2026-09-01        0.118004  \n",
+                                                                      "2                   2026-09-01        1.395025  "
+                                                            ]
+                                                  },
+                                                  "execution_count": 21,
+                                                  "metadata": {},
+                                                  "output_type": "execute_result"
+                                        }
+                              ],
+                              "source": [
+                                        "feature_frames = {series_id: series_frames[series_id] for series_id in FEATURE_SERIES_IDS}\n",
+                                        "candidate_origins = analysis_panel.index[analysis_panel.index >= ANALYSIS_START_TS]\n",
+                                        "example_origin = None\n",
+                                        "snapshot = None\n",
+                                        "for candidate_origin in reversed(candidate_origins):\n",
+                                        "    candidate_snapshot = build_feature_snapshot(candidate_origin, feature_frames)\n",
+                                        "    if candidate_snapshot is not None:\n",
+                                        "        example_origin = candidate_origin\n",
+                                        "        snapshot = candidate_snapshot\n",
+                                        "        break\n",
+                                        "if example_origin is None or snapshot is None:\n",
+                                        "    raise RuntimeError(\"No complete cutoff-safe feature snapshot is available in the analysis window.\")\n",
+                                        "\n",
+                                        "print(f\"Latest complete active-feature snapshot: {example_origin.date()}\")\n",
+                                        "availability_audit = []\n",
+                                        "for series_id in FEATURE_SERIES_IDS:\n",
+                                        "    visible = feature_frames[series_id].query(\"released_at <= @example_origin\").sort_values(\"timestamp\")\n",
+                                        "    latest = visible.iloc[-1]\n",
+                                        "    availability_audit.append(\n",
+                                        "        {\n",
+                                        "            \"series_id\": series_id,\n",
+                                        "            \"origin\": example_origin,\n",
+                                        "            \"latest_visible_reference_month\": latest[\"timestamp\"],\n",
+                                        "            \"latest_visible_release_date\": latest[\"released_at\"],\n",
+                                        "            \"snapshot_value\": snapshot[series_id],\n",
+                                        "        }\n",
+                                        "    )\n",
+                                        "pd.DataFrame(availability_audit).sort_values(\"series_id\")"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "bc38a96b",
+                              "metadata": {
+                                        "id": "bc38a96b",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Interpretation checklist\n",
+                                        "\n",
+                                        "Use the executed tables and charts to record observations here. Keep the distinction between descriptive evidence and forecast evidence intact.\n",
+                                        "\n",
+                                        "- Coverage and quality: identify sparse starts, gaps, and unusual release delays.\n",
+                                        "- Target: decide whether the $-2\\%$ three-month IPMAN threshold visibly captures the intended stress episodes.\n",
+                                        "- Contemporaneous relationships: note co-movements that are economically plausible, but do not call them causal.\n",
+                                        "- Leading candidates: inspect positive lags, especially $+3$ months, then validate any candidate with a cutoff-safe backtest.\n",
+                                        "- Small-sample robustness: the bootstrap/leave-one-stress-month-out check shows VIXCLS, ICSA, SPY_RETURN_12M, XLI_RETURN_12M, and UNRATE keep a stable sign and a reasonably tight range; XLI_RETURN_3M has the widest bootstrap interval (crossing near zero) and the largest leave-one-out swing, so treat it as the least reliable of the six.\n",
+                                        "- Caveats: FRED uses current revisions rather than full ALFRED vintages; GSCPI uses the latest revision-table column; overlapping trailing returns and small stress-event counts can exaggerate apparent patterns."
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "2b6682d5",
+                              "metadata": {
+                                        "id": "2b6682d5",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Current model-input inventory\n",
+                                        "\n",
+                                        "This section mirrors the variables used by the current manufacturing-stress predictors. The model consumes 15 monthly, cutoff-aware features: three trailing IPMAN changes and twelve macro/market inputs. The target is the binary manufacturing-stress label derived from the trailing three-month IPMAN change. Feature observations use the conservative one-month release lag configured by the service builder."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "8923f77d",
+                              "metadata": {
+                                        "id": "8923f77d",
+                                        "language": "python"
+                              },
+                              "outputs": [],
+                              "source": [
+                                        "from manufacturing_stress_forecasting.features import (\n",
+                                        "    CREDIT_SPREAD_SERIES_ID,\n",
+                                        "    FEATURE_SERIES_IDS,\n",
+                                        "    IPMAN_FEATURE_SERIES_IDS,\n",
+                                        "    MACRO_FEATURE_SERIES_IDS,\n",
+                                        "    FEATURE_PERIODS,\n",
+                                        ")\n",
+                                        "from manufacturing_stress_forecasting.targets import (\n",
+                                        "    DEFAULT_LOOKBACK_MONTHS,\n",
+                                        "    DEFAULT_STRESS_THRESHOLD_PCT,\n",
+                                        ")\n",
+                                        "\n",
+                                        "model_input_rows = []\n",
+                                        "for series_id in IPMAN_FEATURE_SERIES_IDS:\n",
+                                        "    model_input_rows.append({\n",
+                                        "        \"series_id\": series_id,\n",
+                                        "        \"family\": \"IPMAN momentum\",\n",
+                                        "        \"transformation\": f\"trailing {FEATURE_PERIODS[series_id]}-month percent change\",\n",
+                                        "    })\n",
+                                        "for series_id in MACRO_FEATURE_SERIES_IDS:\n",
+                                        "    model_input_rows.append({\n",
+                                        "        \"series_id\": series_id,\n",
+                                        "        \"family\": \"macro/market\",\n",
+                                        "        \"transformation\": \"monthly level or trailing return from data service\",\n",
+                                        "    })\n",
+                                        "\n",
+                                        "model_input_inventory = pd.DataFrame(model_input_rows)\n",
+                                        "print(f\"Model features: {len(FEATURE_SERIES_IDS)}\")\n",
+                                        "print(f\"Target lookback: {DEFAULT_LOOKBACK_MONTHS} months\")\n",
+                                        "print(f\"Target threshold: {DEFAULT_STRESS_THRESHOLD_PCT}%\")\n",
+                                        "model_input_inventory"
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "b19910cd",
+                              "metadata": {},
+                              "outputs": [],
+                              "source": [
+                                        "from pathlib import Path\n",
+                                        "\n",
+                                        "REPORT_DIR = REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"reports\" / \"data_understanding\"\n",
+                                        "REPORT_DIR.mkdir(parents=True, exist_ok=True)\n",
+                                        "\n",
+                                        "report_tables = {\n",
+                                        "    \"service_summary\": service_summary,\n",
+                                        "    \"variable_dictionary\": variable_dictionary,\n",
+                                        "    \"coverage\": coverage,\n",
+                                        "    \"summary_statistics\": summary_statistics,\n",
+                                        "    \"target_summary\": target_summary,\n",
+                                        "    \"annual_target_rate\": annual_target_rate,\n",
+                                        "    \"pearson_correlation\": pearson_correlation,\n",
+                                        "    \"spearman_correlation\": spearman_correlation,\n",
+                                        "    \"lag_correlations\": lag_correlations,\n",
+                                        "    \"regime_summary\": regime_summary,\n",
+                                        "    \"robustness_summary\": robustness_summary,\n",
+                                        "    \"release_gap\": release_gap,\n",
+                                        "    \"availability_audit\": pd.DataFrame(availability_audit),\n",
+                                        "    \"model_input_inventory\": model_input_inventory,\n",
+                                        "}\n",
+                                        "for table_name, table in report_tables.items():\n",
+                                        "    table.to_csv(REPORT_DIR / f\"{table_name}.csv\", index=True)\n",
+                                        "\n",
+                                        "feature_columns = [series_id for series_id in FEATURE_SERIES_IDS if series_id in analysis_panel.columns]\n",
+                                        "fig, axes = plt.subplots(2, 1, figsize=(14, 9), sharex=True)\n",
+                                        "analysis_panel[feature_columns].plot(ax=axes[0], linewidth=0.8)\n",
+                                        "axes[0].set_title(\"Manufacturing Stress Model Features\")\n",
+                                        "axes[0].set_ylabel(\"Feature value\")\n",
+                                        "axes[0].legend(loc=\"upper left\", ncol=3, fontsize=8)\n",
+                                        "analysis_panel[\"target\"].plot(ax=axes[1], color=\"firebrick\", drawstyle=\"steps-post\")\n",
+                                        "axes[1].set_title(\"Manufacturing Stress Target\")\n",
+                                        "axes[1].set_ylabel(\"Stress label\")\n",
+                                        "axes[1].set_xlabel(\"Reference month\")\n",
+                                        "fig.tight_layout()\n",
+                                        "fig.savefig(REPORT_DIR / \"feature_and_target_history.png\", dpi=180, bbox_inches=\"tight\")\n",
+                                        "plt.close(fig)\n",
+                                        "\n",
+                                        "correlation_columns = feature_columns + [column for column in [\"target\"] if column in analysis_panel.columns]\n",
+                                        "correlation_matrix = analysis_panel[correlation_columns].corr(method=\"spearman\")\n",
+                                        "fig, axis = plt.subplots(figsize=(13, 11))\n",
+                                        "image = axis.imshow(correlation_matrix, cmap=\"RdBu_r\", vmin=-1, vmax=1, aspect=\"auto\")\n",
+                                        "axis.set_xticks(range(len(correlation_matrix.columns)), correlation_matrix.columns, rotation=90)\n",
+                                        "axis.set_yticks(range(len(correlation_matrix.index)), correlation_matrix.index)\n",
+                                        "axis.set_title(\"Spearman Correlation: Active Features and Target\")\n",
+                                        "fig.colorbar(image, ax=axis, label=\"Correlation\")\n",
+                                        "fig.tight_layout()\n",
+                                        "fig.savefig(REPORT_DIR / \"active_feature_target_correlation.png\", dpi=180, bbox_inches=\"tight\")\n",
+                                        "plt.close(fig)\n",
+                                        "\n",
+                                        "coverage_plot = coverage.set_index(\"series_id\")\n",
+                                        "fig, axis = plt.subplots(figsize=(13, 7))\n",
+                                        "coverage_plot[\"observations\"].sort_values().plot.barh(ax=axis, color=\"steelblue\")\n",
+                                        "axis.set_title(\"Registered Series Observation Counts\")\n",
+                                        "axis.set_xlabel(\"Observations\")\n",
+                                        "fig.tight_layout()\n",
+                                        "fig.savefig(REPORT_DIR / \"feature_coverage.png\", dpi=180, bbox_inches=\"tight\")\n",
+                                        "plt.close(fig)\n",
+                                        "\n",
+                                        "availability_plot = pd.DataFrame(availability_audit).sort_values(\"latest_visible_release_date\")\n",
+                                        "fig, axis = plt.subplots(figsize=(12, 7))\n",
+                                        "axis.barh(availability_plot[\"series_id\"], availability_plot[\"latest_visible_release_date\"])\n",
+                                        "axis.axvline(pd.Timestamp(example_origin), color=\"firebrick\", linestyle=\"--\", label=\"Forecast origin\")\n",
+                                        "axis.set_title(\"Latest Visible Release Dates at Example Forecast Origin\")\n",
+                                        "axis.set_xlabel(\"Release date\")\n",
+                                        "axis.legend()\n",
+                                        "fig.tight_layout()\n",
+                                        "fig.savefig(REPORT_DIR / \"cutoff_safe_release_availability.png\", dpi=180, bbox_inches=\"tight\")\n",
+                                        "plt.close(fig)\n",
+                                        "\n",
+                                        "print(f\"Wrote {len(report_tables)} CSV reports and 4 PNG graphs to {REPORT_DIR}\")"
+                              ]
+                    }
+          ],
+          "metadata": {
+                    "kernelspec": {
+                              "display_name": "agentic-forecasting-bootcamp (3.12.14.final.0)",
+                              "language": "python",
+                              "name": "python3"
+                    },
+                    "language_info": {
+                              "codemirror_mode": {
+                                        "name": "ipython",
+                                        "version": 3
+                              },
+                              "file_extension": ".py",
+                              "mimetype": "text/x-python",
+                              "name": "python",
+                              "nbconvert_exporter": "python",
+                              "pygments_lexer": "ipython3",
+                              "version": "3.12.14"
+                    }
+          },
+          "nbformat": 4,
+          "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_data_understanding_1_5.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_data_understanding_1_5.ipynb
new file mode 100644
index 000000000..b564bfef6
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_data_understanding_1_5.ipynb
@@ -0,0 +1,4438 @@
+{
+    "cells":  [
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "5f83072e",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "5f83072e"
+                                   },
+                      "source":  [
+                                     "# Manufacturing Stress Data Understanding\n",
+                                     "\n",
+                                     "This manufacturing-only notebook inspects the registered data, transformations, target definition, and descriptive relationships before interpreting any forecast-model result. It does not run a backtest, fit a model, or call an LLM.\n",
+                                     "\n",
+                                     "The correlation sections align values by their reference month. The final point-in-time section separately demonstrates the stricter release-date cutoff required for forecasting. Correlation is descriptive, not causal evidence or evidence of predictive performance."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  36,
+                      "id":  "ac0c7417",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "ac0c7417"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "name":  "stdout",
+                                          "output_type":  "stream",
+                                          "text":  [
+                                                       "Running: uv sync --all-extras --dev --all-packages\n",
+                                                       "\n",
+                                                       "Environment synchronized successfully.\n",
+                                                       "Restart the kernel before continuing if package versions changed.\n"
+                                                   ]
+                                      }
+                                  ],
+                      "source":  [
+                                     "# Synchronize the repository environment before running the analysis.\n",
+                                     "import subprocess\n",
+                                     "from pathlib import Path\n",
+                                     "\n",
+                                     "\n",
+                                     "def find_repo_root(start: Path) -\u003e Path:\n",
+                                     "    \"\"\"Find the workspace root without confusing member pyproject files for it.\"\"\"\n",
+                                     "    for candidate in (start, *start.parents):\n",
+                                     "        if (\n",
+                                     "            (candidate / \"uv.lock\").exists()\n",
+                                     "            and (candidate / \"implementations\").is_dir()\n",
+                                     "            and (candidate / \"aieng-forecasting\").is_dir()\n",
+                                     "        ):\n",
+                                     "            return candidate\n",
+                                     "    raise FileNotFoundError(\"Could not find the agentic-forecasting repository root.\")\n",
+                                     "\n",
+                                     "\n",
+                                     "REPO_ROOT = find_repo_root(Path.cwd().resolve())\n",
+                                     "print(\"Running: uv sync --all-extras --dev --all-packages\")\n",
+                                     "setup_result = subprocess.run(\n",
+                                     "    [\"uv\", \"sync\", \"--all-extras\", \"--dev\", \"--all-packages\"],\n",
+                                     "    cwd=REPO_ROOT,\n",
+                                     "    check=False,\n",
+                                     "    text=True,\n",
+                                     "    capture_output=True,\n",
+                                     ")\n",
+                                     "print(setup_result.stdout)\n",
+                                     "if setup_result.returncode != 0:\n",
+                                     "    print(setup_result.stderr)\n",
+                                     "    raise RuntimeError(\"uv sync failed. Install uv or restart with the repository environment selected.\")\n",
+                                     "print(\"Environment synchronized successfully.\")\n",
+                                     "print(\"Restart the kernel before continuing if package versions changed.\")"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "b663959b",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "b663959b"
+                                   },
+                      "source":  [
+                                     "## Analysis controls\n",
+                                     "\n",
+                                     "Use cached data by default. Refreshing contacts FRED, Yahoo Finance, and the New York Fed. The date range affects descriptive charts and tables only; it does not alter the registered service or target construction."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  37,
+                      "id":  "cc148e73",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "cc148e73"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "name":  "stdout",
+                                          "output_type":  "stream",
+                                          "text":  [
+                                                       "{\u0027refresh_input_data\u0027: False, \u0027analysis_start\u0027: \u00272000-01-01\u0027, \u0027analysis_end\u0027: None, \u0027correlation_lag_months\u0027: 18, \u0027rolling_window_months\u0027: 24}\n"
+                                                   ]
+                                      }
+                                  ],
+                      "source":  [
+                                     "REFRESH_INPUT_DATA = False\n",
+                                     "ANALYSIS_START = \"2000-01-01\"\n",
+                                     "ANALYSIS_END = None\n",
+                                     "CORRELATION_LAG_MONTHS = 18\n",
+                                     "ROLLING_WINDOW_MONTHS = 24\n",
+                                     "\n",
+                                     "print(\n",
+                                     "    {\n",
+                                     "        \"refresh_input_data\": REFRESH_INPUT_DATA,\n",
+                                     "        \"analysis_start\": ANALYSIS_START,\n",
+                                     "        \"analysis_end\": ANALYSIS_END,\n",
+                                     "        \"correlation_lag_months\": CORRELATION_LAG_MONTHS,\n",
+                                     "        \"rolling_window_months\": ROLLING_WINDOW_MONTHS,\n",
+                                     "    }\n",
+                                     ")"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "deed9473",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "deed9473"
+                                   },
+                      "source":  [
+                                     "## Imports, paths, and registered manufacturing data\n",
+                                     "\n",
+                                     "The analysis reads through `build_manufacturing_stress_service()`. This preserves the same transformations and release-date metadata used by the forecasting workflow."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  38,
+                      "id":  "dbf5c1bf",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "dbf5c1bf"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "name":  "stdout",
+                                          "output_type":  "stream",
+                                          "text":  [
+                                                       "Registered manufacturing series: 21\n",
+                                                       "Analysis target: manufacturing_stress_3m_decline_1_5pct (threshold=-1.5%)\n"
+                                                   ]
+                                      },
+                                      {
+                                          "data":  {
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+                                                                         "      \u003ctd\u003e1998-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e7\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eUNRATE\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eU.S. unemployment rate\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (UNRATE)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e943\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1948-01-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e8\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eVIXCLS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eCBOE volatility index\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (VIXCLS)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIndex\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e441\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1990-01-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e9\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eXLI trailing 12-month adjusted-close return\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eYahoo Finance (XLI), derived\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e322\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1999-12-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e10\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_3M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eXLI trailing 3-month adjusted-close return\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eYahoo Finance (XLI), derived\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e331\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1999-03-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e11\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eYC_SPREAD\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e10-year minus 2-year Treasury yield spread\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (DGS10 minus DGS2)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercentage points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e604\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-06-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e12\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003efed_funds_rate_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMonth-end effective federal funds rate\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (FEDFUNDS), derived monthly\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e866\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1954-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e13\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eglobal_supply_chain_pressure_index\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eNew York Fed Global Supply Chain Pressure Index\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFederal Reserve Bank of New York (GSCPI)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eStandard deviations from historical average\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e348\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1997-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e14\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_1m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eTrailing 1-month percentage change in IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e655\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-02-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e15\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_3m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eTrailing 3-month percentage change in IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e16\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_6m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eTrailing 6-month percentage change in IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e650\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e17\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_us_manufacturing_production\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eU.S. manufacturing industrial production index...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (IPMAN)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIndex\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e656\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-01-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e18\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003emanufacturing_stress\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary U.S. manufacturing stress label: 1 when...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary event (0=no stress, 1=stress)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e19\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003emanufacturing_stress_3m_decline_1_5pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary U.S. manufacturing material-contraction...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary event (0=no material contraction, 1=mat...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e20\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003etreasury_10y_minus_2y_pct_points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMonth-end 10-year minus 2-year Treasury yield ...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (DGS10 minus DGS2), derived monthly\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercentage points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e604\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-06-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                                 series_id  \\\n",
+                                                                          "0                                 CPIAUCSL   \n",
+                                                                          "1                                  CPI_YOY   \n",
+                                                                          "2                                 FEDFUNDS   \n",
+                                                                          "3                                HY_SPREAD   \n",
+                                                                          "4                                     ICSA   \n",
+                                                                          "5                           SPY_RETURN_12M   \n",
+                                                                          "6                            SPY_RETURN_3M   \n",
+                                                                          "7                                   UNRATE   \n",
+                                                                          "8                                   VIXCLS   \n",
+                                                                          "9                           XLI_RETURN_12M   \n",
+                                                                          "10                           XLI_RETURN_3M   \n",
+                                                                          "11                               YC_SPREAD   \n",
+                                                                          "12                      fed_funds_rate_pct   \n",
+                                                                          "13      global_supply_chain_pressure_index   \n",
+                                                                          "14                     ipman_change_1m_pct   \n",
+                                                                          "15                     ipman_change_3m_pct   \n",
+                                                                          "16                     ipman_change_6m_pct   \n",
+                                                                          "17       ipman_us_manufacturing_production   \n",
+                                                                          "18                    manufacturing_stress   \n",
+                                                                          "19  manufacturing_stress_3m_decline_1_5pct   \n",
+                                                                          "20        treasury_10y_minus_2y_pct_points   \n",
+                                                                          "\n",
+                                                                          "                                          description  \\\n",
+                                                                          "0        Consumer Price Index for All Urban Consumers   \n",
+                                                                          "1          Consumer Price Index year-over-year change   \n",
+                                                                          "2                        Effective federal funds rate   \n",
+                                                                          "3              U.S. high-yield option-adjusted spread   \n",
+                                                                          "4           Initial claims for unemployment insurance   \n",
+                                                                          "5         SPY trailing 12-month adjusted-close return   \n",
+                                                                          "6          SPY trailing 3-month adjusted-close return   \n",
+                                                                          "7                              U.S. unemployment rate   \n",
+                                                                          "8                               CBOE volatility index   \n",
+                                                                          "9         XLI trailing 12-month adjusted-close return   \n",
+                                                                          "10         XLI trailing 3-month adjusted-close return   \n",
+                                                                          "11         10-year minus 2-year Treasury yield spread   \n",
+                                                                          "12             Month-end effective federal funds rate   \n",
+                                                                          "13    New York Fed Global Supply Chain Pressure Index   \n",
+                                                                          "14        Trailing 1-month percentage change in IPMAN   \n",
+                                                                          "15        Trailing 3-month percentage change in IPMAN   \n",
+                                                                          "16        Trailing 6-month percentage change in IPMAN   \n",
+                                                                          "17  U.S. manufacturing industrial production index...   \n",
+                                                                          "18  Binary U.S. manufacturing stress label: 1 when...   \n",
+                                                                          "19  Binary U.S. manufacturing material-contraction...   \n",
+                                                                          "20  Month-end 10-year minus 2-year Treasury yield ...   \n",
+                                                                          "\n",
+                                                                          "                                      source  \\\n",
+                                                                          "0                            FRED (CPIAUCSL)   \n",
+                                                                          "1               Derived from FRED (CPIAUCSL)   \n",
+                                                                          "2                            FRED (FEDFUNDS)   \n",
+                                                                          "3                        FRED (BAMLH0A0HYM2)   \n",
+                                                                          "4                                FRED (ICSA)   \n",
+                                                                          "5               Yahoo Finance (SPY), derived   \n",
+                                                                          "6               Yahoo Finance (SPY), derived   \n",
+                                                                          "7                              FRED (UNRATE)   \n",
+                                                                          "8                              FRED (VIXCLS)   \n",
+                                                                          "9               Yahoo Finance (XLI), derived   \n",
+                                                                          "10              Yahoo Finance (XLI), derived   \n",
+                                                                          "11                   FRED (DGS10 minus DGS2)   \n",
+                                                                          "12          FRED (FEDFUNDS), derived monthly   \n",
+                                                                          "13  Federal Reserve Bank of New York (GSCPI)   \n",
+                                                                          "14                   Derived from FRED IPMAN   \n",
+                                                                          "15                   Derived from FRED IPMAN   \n",
+                                                                          "16                   Derived from FRED IPMAN   \n",
+                                                                          "17                              FRED (IPMAN)   \n",
+                                                                          "18                   Derived from FRED IPMAN   \n",
+                                                                          "19                   Derived from FRED IPMAN   \n",
+                                                                          "20  FRED (DGS10 minus DGS2), derived monthly   \n",
+                                                                          "\n",
+                                                                          "                                                units frequency  n_obs  \\\n",
+                                                                          "0                                               Index        MS    955   \n",
+                                                                          "1                                             Percent        MS    943   \n",
+                                                                          "2                                             Percent        MS    866   \n",
+                                                                          "3                                   Percentage points        MS     37   \n",
+                                                                          "4                                              Number        MS    717   \n",
+                                                                          "5                                             Percent        MS    333   \n",
+                                                                          "6                                             Percent        MS    342   \n",
+                                                                          "7                                             Percent        MS    943   \n",
+                                                                          "8                                               Index        MS    441   \n",
+                                                                          "9                                             Percent        MS    322   \n",
+                                                                          "10                                            Percent        MS    331   \n",
+                                                                          "11                                  Percentage points        MS    604   \n",
+                                                                          "12                                            Percent        MS    866   \n",
+                                                                          "13        Standard deviations from historical average        MS    348   \n",
+                                                                          "14                                            Percent        MS    655   \n",
+                                                                          "15                                            Percent        MS    653   \n",
+                                                                          "16                                            Percent        MS    650   \n",
+                                                                          "17                                              Index        MS    656   \n",
+                                                                          "18               Binary event (0=no stress, 1=stress)        MS    653   \n",
+                                                                          "19  Binary event (0=no material contraction, 1=mat...        MS    653   \n",
+                                                                          "20                                  Percentage points        MS    604   \n",
+                                                                          "\n",
+                                                                          "        start        end  \n",
+                                                                          "0  1947-01-01 2026-08-01  \n",
+                                                                          "1  1948-01-01 2026-08-01  \n",
+                                                                          "2  1954-07-01 2026-08-01  \n",
+                                                                          "3  2023-09-01 2026-09-01  \n",
+                                                                          "4  1967-01-01 2026-09-01  \n",
+                                                                          "5  1999-01-01 2026-09-01  \n",
+                                                                          "6  1998-04-01 2026-09-01  \n",
+                                                                          "7  1948-01-01 2026-08-01  \n",
+                                                                          "8  1990-01-01 2026-09-01  \n",
+                                                                          "9  1999-12-01 2026-09-01  \n",
+                                                                          "10 1999-03-01 2026-09-01  \n",
+                                                                          "11 1976-06-01 2026-09-01  \n",
+                                                                          "12 1954-07-01 2026-08-01  \n",
+                                                                          "13 1997-09-01 2026-08-01  \n",
+                                                                          "14 1972-02-01 2026-08-01  \n",
+                                                                          "15 1972-04-01 2026-08-01  \n",
+                                                                          "16 1972-07-01 2026-08-01  \n",
+                                                                          "17 1972-01-01 2026-08-01  \n",
+                                                                          "18 1972-04-01 2026-08-01  \n",
+                                                                          "19 1972-04-01 2026-08-01  \n",
+                                                                          "20 1976-06-01 2026-09-01  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  38,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "from datetime import datetime\n",
+                                     "\n",
+                                     "import matplotlib.pyplot as plt\n",
+                                     "import numpy as np\n",
+                                     "import pandas as pd\n",
+                                     "from dotenv import load_dotenv\n",
+                                     "\n",
+                                     "from manufacturing_stress_forecasting.data import (\n",
+                                     "    IPMAN_SERIES_ID,\n",
+                                     "    MATERIAL_STRESS_1_5PCT_SERIES_ID,\n",
+                                     "    build_manufacturing_stress_service,\n",
+                                     ")\n",
+                                     "from manufacturing_stress_forecasting.features import (\n",
+                                     "    FEATURE_SERIES_IDS,\n",
+                                     "    FED_FUNDS_SERIES_ID,\n",
+                                     "    GSCPI_SERIES_ID,\n",
+                                     "    IPMAN_CHANGE_3M_SERIES_ID,\n",
+                                     "    IPMAN_FEATURE_SERIES_IDS,\n",
+                                     "    MACRO_FEATURE_SERIES_IDS,\n",
+                                     "    YIELD_CURVE_SERIES_ID,\n",
+                                     "    build_feature_snapshot,\n",
+                                     ")\n",
+                                     "from manufacturing_stress_forecasting.targets import (\n",
+                                     "    DEFAULT_LOOKBACK_MONTHS,\n",
+                                     "    derive_manufacturing_stress_labels,\n",
+                                     ")\n",
+                                     "\n",
+                                     "\n",
+                                     "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+                                     "FULL_HISTORY_AS_OF = datetime(2100, 1, 1)\n",
+                                     "ANALYSIS_START_TS = pd.Timestamp(ANALYSIS_START)\n",
+                                     "ANALYSIS_END_TS = pd.Timestamp(ANALYSIS_END) if ANALYSIS_END else None\n",
+                                     "TARGET_SERIES_ID = MATERIAL_STRESS_1_5PCT_SERIES_ID\n",
+                                     "TARGET_THRESHOLD_PCT = -1.5\n",
+                                     "\n",
+                                     "service = build_manufacturing_stress_service(\n",
+                                     "    cache_dir=REPO_ROOT / \"data\" / \"fred\",\n",
+                                     "    yahoo_cache_dir=REPO_ROOT / \"data\" / \"yfinance\",\n",
+                                     "    gscpi_cache_path=REPO_ROOT / \"data\" / \"new_york_fed\" / \"gscpi_interactive_data.csv\",\n",
+                                     "    refresh=REFRESH_INPUT_DATA,\n",
+                                     ")\n",
+                                     "service_summary = service.summary().sort_values(\"series_id\").reset_index(drop=True)\n",
+                                     "print(f\"Registered manufacturing series: {len(service_summary)}\")\n",
+                                     "print(f\"Analysis target: {TARGET_SERIES_ID} (threshold={TARGET_THRESHOLD_PCT}%)\")\n",
+                                     "service_summary"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "3fefe669",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "3fefe669"
+                                   },
+                      "source":  [
+                                     "## Variable inventory and availability\n",
+                                     "\n",
+                                     "All registered series are monthly. `timestamp` is the reference month represented by a value; `released_at` is the date when that value becomes visible to a forecast origin. The tables below audit both. GSCPI is included for exploration but is not one of the active model features."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  39,
+                      "id":  "e1c52683",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "e1c52683"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "text/html":  [
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+                                                                         "      \u003ctd\u003eActive macro or market feature\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1999-12-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2000-01-03\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-25\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e10\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_3M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eXLI trailing 3-month adjusted-close return\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eYahoo Finance (XLI), derived\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e331\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1999-03-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eActive macro or market feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e331\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1999-03-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1999-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-25\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e11\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eYC_SPREAD\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e10-year minus 2-year Treasury yield spread\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (DGS10 minus DGS2)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercentage points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e604\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-06-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eActive macro or market feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e604\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-06-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-23\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e19\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003emanufacturing_stress_3m_decline_1_5pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary U.S. manufacturing material-contraction...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary event (0=no material contraction, 1=mat...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary target\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-05-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e13\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eglobal_supply_chain_pressure_index\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eNew York Fed Global Supply Chain Pressure Index\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFederal Reserve Bank of New York (GSCPI)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eStandard deviations from historical average\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e348\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1997-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eExploratory GSCPI challenger\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e348\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1997-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1997-10-06\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-04\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e17\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_us_manufacturing_production\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eU.S. manufacturing industrial production index...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (IPMAN)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIndex\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e656\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-01-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN level\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e656\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-01-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-02-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e14\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_1m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eTrailing 1-month percentage change in IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e655\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-02-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN momentum feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e655\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-02-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-03-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e15\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_3m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eTrailing 3-month percentage change in IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN momentum feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-05-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e16\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_6m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eTrailing 6-month percentage change in IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e650\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN momentum feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e650\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e12\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003efed_funds_rate_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMonth-end effective federal funds rate\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (FEDFUNDS), derived monthly\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercent\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e866\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1954-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eLegacy macro alias\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e866\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1954-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1954-07-02\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-03\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e20\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003etreasury_10y_minus_2y_pct_points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMonth-end 10-year minus 2-year Treasury yield ...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (DGS10 minus DGS2), derived monthly\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercentage points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e604\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-06-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eLegacy macro alias\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e604\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-06-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-23\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e3\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eHY_SPREAD\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eU.S. high-yield option-adjusted spread\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eFRED (BAMLH0A0HYM2)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003ePercentage points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e37\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2023-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eRegistered series\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e37\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2023-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2023-10-02\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-24\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e18\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003emanufacturing_stress\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary U.S. manufacturing stress label: 1 when...\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eDerived from FRED IPMAN\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary event (0=no stress, 1=stress)\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eMS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eRegistered series\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-05-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                                 series_id  \\\n",
+                                                                          "0                                 CPIAUCSL   \n",
+                                                                          "1                                  CPI_YOY   \n",
+                                                                          "2                                 FEDFUNDS   \n",
+                                                                          "4                                     ICSA   \n",
+                                                                          "5                           SPY_RETURN_12M   \n",
+                                                                          "6                            SPY_RETURN_3M   \n",
+                                                                          "7                                   UNRATE   \n",
+                                                                          "8                                   VIXCLS   \n",
+                                                                          "9                           XLI_RETURN_12M   \n",
+                                                                          "10                           XLI_RETURN_3M   \n",
+                                                                          "11                               YC_SPREAD   \n",
+                                                                          "19  manufacturing_stress_3m_decline_1_5pct   \n",
+                                                                          "13      global_supply_chain_pressure_index   \n",
+                                                                          "17       ipman_us_manufacturing_production   \n",
+                                                                          "14                     ipman_change_1m_pct   \n",
+                                                                          "15                     ipman_change_3m_pct   \n",
+                                                                          "16                     ipman_change_6m_pct   \n",
+                                                                          "12                      fed_funds_rate_pct   \n",
+                                                                          "20        treasury_10y_minus_2y_pct_points   \n",
+                                                                          "3                                HY_SPREAD   \n",
+                                                                          "18                    manufacturing_stress   \n",
+                                                                          "\n",
+                                                                          "                                          description  \\\n",
+                                                                          "0        Consumer Price Index for All Urban Consumers   \n",
+                                                                          "1          Consumer Price Index year-over-year change   \n",
+                                                                          "2                        Effective federal funds rate   \n",
+                                                                          "4           Initial claims for unemployment insurance   \n",
+                                                                          "5         SPY trailing 12-month adjusted-close return   \n",
+                                                                          "6          SPY trailing 3-month adjusted-close return   \n",
+                                                                          "7                              U.S. unemployment rate   \n",
+                                                                          "8                               CBOE volatility index   \n",
+                                                                          "9         XLI trailing 12-month adjusted-close return   \n",
+                                                                          "10         XLI trailing 3-month adjusted-close return   \n",
+                                                                          "11         10-year minus 2-year Treasury yield spread   \n",
+                                                                          "19  Binary U.S. manufacturing material-contraction...   \n",
+                                                                          "13    New York Fed Global Supply Chain Pressure Index   \n",
+                                                                          "17  U.S. manufacturing industrial production index...   \n",
+                                                                          "14        Trailing 1-month percentage change in IPMAN   \n",
+                                                                          "15        Trailing 3-month percentage change in IPMAN   \n",
+                                                                          "16        Trailing 6-month percentage change in IPMAN   \n",
+                                                                          "12             Month-end effective federal funds rate   \n",
+                                                                          "20  Month-end 10-year minus 2-year Treasury yield ...   \n",
+                                                                          "3              U.S. high-yield option-adjusted spread   \n",
+                                                                          "18  Binary U.S. manufacturing stress label: 1 when...   \n",
+                                                                          "\n",
+                                                                          "                                      source  \\\n",
+                                                                          "0                            FRED (CPIAUCSL)   \n",
+                                                                          "1               Derived from FRED (CPIAUCSL)   \n",
+                                                                          "2                            FRED (FEDFUNDS)   \n",
+                                                                          "4                                FRED (ICSA)   \n",
+                                                                          "5               Yahoo Finance (SPY), derived   \n",
+                                                                          "6               Yahoo Finance (SPY), derived   \n",
+                                                                          "7                              FRED (UNRATE)   \n",
+                                                                          "8                              FRED (VIXCLS)   \n",
+                                                                          "9               Yahoo Finance (XLI), derived   \n",
+                                                                          "10              Yahoo Finance (XLI), derived   \n",
+                                                                          "11                   FRED (DGS10 minus DGS2)   \n",
+                                                                          "19                   Derived from FRED IPMAN   \n",
+                                                                          "13  Federal Reserve Bank of New York (GSCPI)   \n",
+                                                                          "17                              FRED (IPMAN)   \n",
+                                                                          "14                   Derived from FRED IPMAN   \n",
+                                                                          "15                   Derived from FRED IPMAN   \n",
+                                                                          "16                   Derived from FRED IPMAN   \n",
+                                                                          "12          FRED (FEDFUNDS), derived monthly   \n",
+                                                                          "20  FRED (DGS10 minus DGS2), derived monthly   \n",
+                                                                          "3                        FRED (BAMLH0A0HYM2)   \n",
+                                                                          "18                   Derived from FRED IPMAN   \n",
+                                                                          "\n",
+                                                                          "                                                units frequency  n_obs  \\\n",
+                                                                          "0                                               Index        MS    955   \n",
+                                                                          "1                                             Percent        MS    943   \n",
+                                                                          "2                                             Percent        MS    866   \n",
+                                                                          "4                                              Number        MS    717   \n",
+                                                                          "5                                             Percent        MS    333   \n",
+                                                                          "6                                             Percent        MS    342   \n",
+                                                                          "7                                             Percent        MS    943   \n",
+                                                                          "8                                               Index        MS    441   \n",
+                                                                          "9                                             Percent        MS    322   \n",
+                                                                          "10                                            Percent        MS    331   \n",
+                                                                          "11                                  Percentage points        MS    604   \n",
+                                                                          "19  Binary event (0=no material contraction, 1=mat...        MS    653   \n",
+                                                                          "13        Standard deviations from historical average        MS    348   \n",
+                                                                          "17                                              Index        MS    656   \n",
+                                                                          "14                                            Percent        MS    655   \n",
+                                                                          "15                                            Percent        MS    653   \n",
+                                                                          "16                                            Percent        MS    650   \n",
+                                                                          "12                                            Percent        MS    866   \n",
+                                                                          "20                                  Percentage points        MS    604   \n",
+                                                                          "3                                   Percentage points        MS     37   \n",
+                                                                          "18               Binary event (0=no stress, 1=stress)        MS    653   \n",
+                                                                          "\n",
+                                                                          "        start        end                        category  observations  \\\n",
+                                                                          "0  1947-01-01 2026-08-01  Active macro or market feature           955   \n",
+                                                                          "1  1948-01-01 2026-08-01  Active macro or market feature           943   \n",
+                                                                          "2  1954-07-01 2026-08-01  Active macro or market feature           866   \n",
+                                                                          "4  1967-01-01 2026-09-01  Active macro or market feature           717   \n",
+                                                                          "5  1999-01-01 2026-09-01  Active macro or market feature           333   \n",
+                                                                          "6  1998-04-01 2026-09-01  Active macro or market feature           342   \n",
+                                                                          "7  1948-01-01 2026-08-01  Active macro or market feature           943   \n",
+                                                                          "8  1990-01-01 2026-09-01  Active macro or market feature           441   \n",
+                                                                          "9  1999-12-01 2026-09-01  Active macro or market feature           322   \n",
+                                                                          "10 1999-03-01 2026-09-01  Active macro or market feature           331   \n",
+                                                                          "11 1976-06-01 2026-09-01  Active macro or market feature           604   \n",
+                                                                          "19 1972-04-01 2026-08-01                   Binary target           653   \n",
+                                                                          "13 1997-09-01 2026-08-01    Exploratory GSCPI challenger           348   \n",
+                                                                          "17 1972-01-01 2026-08-01                     IPMAN level           656   \n",
+                                                                          "14 1972-02-01 2026-08-01          IPMAN momentum feature           655   \n",
+                                                                          "15 1972-04-01 2026-08-01          IPMAN momentum feature           653   \n",
+                                                                          "16 1972-07-01 2026-08-01          IPMAN momentum feature           650   \n",
+                                                                          "12 1954-07-01 2026-08-01              Legacy macro alias           866   \n",
+                                                                          "20 1976-06-01 2026-09-01              Legacy macro alias           604   \n",
+                                                                          "3  2023-09-01 2026-09-01               Registered series            37   \n",
+                                                                          "18 1972-04-01 2026-08-01               Registered series           653   \n",
+                                                                          "\n",
+                                                                          "    missing_values  missing_rate reference_start reference_end first_release  \\\n",
+                                                                          "0                0           0.0      1947-01-01    2026-08-01    1947-01-02   \n",
+                                                                          "1                0           0.0      1948-01-01    2026-08-01    1948-01-02   \n",
+                                                                          "2                0           0.0      1954-07-01    2026-08-01    1954-07-02   \n",
+                                                                          "4                0           0.0      1967-01-01    2026-09-01    1967-01-30   \n",
+                                                                          "5                0           0.0      1999-01-01    2026-09-01    1999-02-01   \n",
+                                                                          "6                0           0.0      1998-04-01    2026-09-01    1998-05-01   \n",
+                                                                          "7                0           0.0      1948-01-01    2026-08-01    1948-01-02   \n",
+                                                                          "8                0           0.0      1990-01-01    2026-09-01    1990-02-01   \n",
+                                                                          "9                0           0.0      1999-12-01    2026-09-01    2000-01-03   \n",
+                                                                          "10               0           0.0      1999-03-01    2026-09-01    1999-04-01   \n",
+                                                                          "11               0           0.0      1976-06-01    2026-09-01    1976-07-01   \n",
+                                                                          "19               0           0.0      1972-04-01    2026-08-01    1972-05-01   \n",
+                                                                          "13               0           0.0      1997-09-01    2026-08-01    1997-10-06   \n",
+                                                                          "17               0           0.0      1972-01-01    2026-08-01    1972-02-01   \n",
+                                                                          "14               0           0.0      1972-02-01    2026-08-01    1972-03-01   \n",
+                                                                          "15               0           0.0      1972-04-01    2026-08-01    1972-05-01   \n",
+                                                                          "16               0           0.0      1972-07-01    2026-08-01    1972-08-01   \n",
+                                                                          "12               0           0.0      1954-07-01    2026-08-01    1954-07-02   \n",
+                                                                          "20               0           0.0      1976-06-01    2026-09-01    1976-07-01   \n",
+                                                                          "3                0           0.0      2023-09-01    2026-09-01    2023-10-02   \n",
+                                                                          "18               0           0.0      1972-04-01    2026-08-01    1972-05-01   \n",
+                                                                          "\n",
+                                                                          "   last_release  \n",
+                                                                          "0    2026-08-03  \n",
+                                                                          "1    2026-08-03  \n",
+                                                                          "2    2026-08-03  \n",
+                                                                          "4    2026-09-21  \n",
+                                                                          "5    2026-09-25  \n",
+                                                                          "6    2026-09-25  \n",
+                                                                          "7    2026-08-03  \n",
+                                                                          "8    2026-09-23  \n",
+                                                                          "9    2026-09-25  \n",
+                                                                          "10   2026-09-25  \n",
+                                                                          "11   2026-09-23  \n",
+                                                                          "19   2026-09-01  \n",
+                                                                          "13   2026-09-04  \n",
+                                                                          "17   2026-09-01  \n",
+                                                                          "14   2026-09-01  \n",
+                                                                          "15   2026-09-01  \n",
+                                                                          "16   2026-09-01  \n",
+                                                                          "12   2026-08-03  \n",
+                                                                          "20   2026-09-23  \n",
+                                                                          "3    2026-09-24  \n",
+                                                                          "18   2026-09-01  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  39,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "CATEGORY_BY_SERIES = {\n",
+                                     "    IPMAN_SERIES_ID: \"IPMAN level\",\n",
+                                     "    **{series_id: \"IPMAN momentum feature\" for series_id in IPMAN_FEATURE_SERIES_IDS},\n",
+                                     "    **{series_id: \"Active macro or market feature\" for series_id in MACRO_FEATURE_SERIES_IDS},\n",
+                                     "    FED_FUNDS_SERIES_ID: \"Legacy macro alias\",\n",
+                                     "    YIELD_CURVE_SERIES_ID: \"Legacy macro alias\",\n",
+                                     "    GSCPI_SERIES_ID: \"Exploratory GSCPI challenger\",\n",
+                                     "    TARGET_SERIES_ID: \"Binary target\",\n",
+                                     "}\n",
+                                     "\n",
+                                     "\n",
+                                     "def full_series(series_id: str) -\u003e pd.DataFrame:\n",
+                                     "    frame = service.get_series(series_id, as_of=FULL_HISTORY_AS_OF).copy()\n",
+                                     "    frame[\"timestamp\"] = pd.to_datetime(frame[\"timestamp\"])\n",
+                                     "    frame[\"released_at\"] = pd.to_datetime(frame[\"released_at\"])\n",
+                                     "    return frame.sort_values(\"timestamp\").reset_index(drop=True)\n",
+                                     "\n",
+                                     "\n",
+                                     "series_frames = {series_id: full_series(series_id) for series_id in service_summary[\"series_id\"]}\n",
+                                     "coverage_rows = []\n",
+                                     "for series_id, frame in series_frames.items():\n",
+                                     "    coverage_rows.append(\n",
+                                     "        {\n",
+                                     "            \"series_id\": series_id,\n",
+                                     "            \"category\": CATEGORY_BY_SERIES.get(series_id, \"Registered series\"),\n",
+                                     "            \"observations\": len(frame),\n",
+                                     "            \"missing_values\": int(frame[\"value\"].isna().sum()),\n",
+                                     "            \"missing_rate\": frame[\"value\"].isna().mean(),\n",
+                                     "            \"reference_start\": frame[\"timestamp\"].min(),\n",
+                                     "            \"reference_end\": frame[\"timestamp\"].max(),\n",
+                                     "            \"first_release\": frame[\"released_at\"].min(),\n",
+                                     "            \"last_release\": frame[\"released_at\"].max(),\n",
+                                     "        }\n",
+                                     "    )\n",
+                                     "\n",
+                                     "coverage = pd.DataFrame(coverage_rows).sort_values([\"category\", \"series_id\"])\n",
+                                     "variable_dictionary = (\n",
+                                     "    service_summary.assign(category=service_summary[\"series_id\"].map(CATEGORY_BY_SERIES).fillna(\"Registered series\"))\n",
+                                     "    .merge(coverage, on=[\"series_id\", \"category\"], how=\"left\")\n",
+                                     "    .sort_values([\"category\", \"series_id\"])\n",
+                                     ")\n",
+                                     "variable_dictionary"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  19,
+                      "id":  "86aeb5b8",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "86aeb5b8"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e19\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003emanufacturing_stress_3m_decline_1_5pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eBinary target\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e13\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eglobal_supply_chain_pressure_index\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eExploratory GSCPI challenger\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e348\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1997-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e35.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e17\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_us_manufacturing_production\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN level\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e656\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-01-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e14\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_1m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN momentum feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e655\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-02-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e15\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_3m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN momentum feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e16\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_6m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eIPMAN momentum feature\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e650\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e12\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003efed_funds_rate_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eLegacy macro alias\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e866\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1954-07-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e20\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003etreasury_10y_minus_2y_pct_points\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eLegacy macro alias\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e604\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1976-06-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e3\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eHY_SPREAD\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eRegistered series\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e37\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2023-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e18\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003emanufacturing_stress\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eRegistered series\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e653\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1972-04-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.0\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                                 series_id                        category  \\\n",
+                                                                          "0                                 CPIAUCSL  Active macro or market feature   \n",
+                                                                          "1                                  CPI_YOY  Active macro or market feature   \n",
+                                                                          "2                                 FEDFUNDS  Active macro or market feature   \n",
+                                                                          "4                                     ICSA  Active macro or market feature   \n",
+                                                                          "5                           SPY_RETURN_12M  Active macro or market feature   \n",
+                                                                          "6                            SPY_RETURN_3M  Active macro or market feature   \n",
+                                                                          "7                                   UNRATE  Active macro or market feature   \n",
+                                                                          "8                                   VIXCLS  Active macro or market feature   \n",
+                                                                          "9                           XLI_RETURN_12M  Active macro or market feature   \n",
+                                                                          "10                           XLI_RETURN_3M  Active macro or market feature   \n",
+                                                                          "11                               YC_SPREAD  Active macro or market feature   \n",
+                                                                          "19  manufacturing_stress_3m_decline_1_5pct                   Binary target   \n",
+                                                                          "13      global_supply_chain_pressure_index    Exploratory GSCPI challenger   \n",
+                                                                          "17       ipman_us_manufacturing_production                     IPMAN level   \n",
+                                                                          "14                     ipman_change_1m_pct          IPMAN momentum feature   \n",
+                                                                          "15                     ipman_change_3m_pct          IPMAN momentum feature   \n",
+                                                                          "16                     ipman_change_6m_pct          IPMAN momentum feature   \n",
+                                                                          "12                      fed_funds_rate_pct              Legacy macro alias   \n",
+                                                                          "20        treasury_10y_minus_2y_pct_points              Legacy macro alias   \n",
+                                                                          "3                                HY_SPREAD               Registered series   \n",
+                                                                          "18                    manufacturing_stress               Registered series   \n",
+                                                                          "\n",
+                                                                          "    observations  missing_values  missing_rate reference_start reference_end  \\\n",
+                                                                          "0            955               0           0.0      1947-01-01    2026-08-01   \n",
+                                                                          "1            943               0           0.0      1948-01-01    2026-08-01   \n",
+                                                                          "2            866               0           0.0      1954-07-01    2026-08-01   \n",
+                                                                          "4            717               0           0.0      1967-01-01    2026-09-01   \n",
+                                                                          "5            333               0           0.0      1999-01-01    2026-09-01   \n",
+                                                                          "6            342               0           0.0      1998-04-01    2026-09-01   \n",
+                                                                          "7            943               0           0.0      1948-01-01    2026-08-01   \n",
+                                                                          "8            441               0           0.0      1990-01-01    2026-09-01   \n",
+                                                                          "9            322               0           0.0      1999-12-01    2026-09-01   \n",
+                                                                          "10           331               0           0.0      1999-03-01    2026-09-01   \n",
+                                                                          "11           604               0           0.0      1976-06-01    2026-09-01   \n",
+                                                                          "19           653               0           0.0      1972-04-01    2026-08-01   \n",
+                                                                          "13           348               0           0.0      1997-09-01    2026-08-01   \n",
+                                                                          "17           656               0           0.0      1972-01-01    2026-08-01   \n",
+                                                                          "14           655               0           0.0      1972-02-01    2026-08-01   \n",
+                                                                          "15           653               0           0.0      1972-04-01    2026-08-01   \n",
+                                                                          "16           650               0           0.0      1972-07-01    2026-08-01   \n",
+                                                                          "12           866               0           0.0      1954-07-01    2026-08-01   \n",
+                                                                          "20           604               0           0.0      1976-06-01    2026-09-01   \n",
+                                                                          "3             37               0           0.0      2023-09-01    2026-09-01   \n",
+                                                                          "18           653               0           0.0      1972-04-01    2026-08-01   \n",
+                                                                          "\n",
+                                                                          "    median_release_delay_days  \n",
+                                                                          "0                         1.0  \n",
+                                                                          "1                         1.0  \n",
+                                                                          "2                         1.0  \n",
+                                                                          "4                        28.0  \n",
+                                                                          "5                        31.0  \n",
+                                                                          "6                        31.0  \n",
+                                                                          "7                         1.0  \n",
+                                                                          "8                        31.0  \n",
+                                                                          "9                        31.0  \n",
+                                                                          "10                       31.0  \n",
+                                                                          "11                       31.0  \n",
+                                                                          "19                       31.0  \n",
+                                                                          "13                       35.0  \n",
+                                                                          "17                       31.0  \n",
+                                                                          "14                       31.0  \n",
+                                                                          "15                       31.0  \n",
+                                                                          "16                       31.0  \n",
+                                                                          "12                        1.0  \n",
+                                                                          "20                       31.0  \n",
+                                                                          "3                        31.0  \n",
+                                                                          "18                       31.0  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  19,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "release_gap = coverage.assign(\n",
+                                     "    median_release_delay_days=[\n",
+                                     "        (frame[\"released_at\"] - frame[\"timestamp\"]).dt.days.median()\n",
+                                     "        for frame in (series_frames[series_id] for series_id in coverage[\"series_id\"])\n",
+                                     "    ]\n",
+                                     ")[\n",
+                                     "    [\n",
+                                     "        \"series_id\",\n",
+                                     "        \"category\",\n",
+                                     "        \"observations\",\n",
+                                     "        \"missing_values\",\n",
+                                     "        \"missing_rate\",\n",
+                                     "        \"reference_start\",\n",
+                                     "        \"reference_end\",\n",
+                                     "        \"median_release_delay_days\",\n",
+                                     "    ]\n",
+                                     "]\n",
+                                     "release_gap"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "50cab4a1",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "50cab4a1"
+                                   },
+                      "source":  [
+                                     "## IPMAN level and manufacturing momentum\n",
+                                     "\n",
+                                     "The target is derived from the trailing three-month IPMAN change, so inspect the level and momentum features before considering any correlation or model result."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  20,
+                      "id":  "6844dd75",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "6844dd75"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "image/png":  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+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1400x800 with 2 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      }
+                                  ],
+                      "source":  [
+                                     "\n",
+                                     "def in_analysis_window(frame: pd.DataFrame) -\u003e pd.DataFrame:\n",
+                                     "    mask = frame[\"timestamp\"] \u003e= ANALYSIS_START_TS\n",
+                                     "    if ANALYSIS_END_TS is not None:\n",
+                                     "        mask \u0026= frame[\"timestamp\"] \u003c= ANALYSIS_END_TS\n",
+                                     "    return frame.loc[mask].copy()\n",
+                                     "\n",
+                                     "\n",
+                                     "ipman = in_analysis_window(series_frames[IPMAN_SERIES_ID])\n",
+                                     "ipman_momentum = {series_id: in_analysis_window(series_frames[series_id]) for series_id in IPMAN_FEATURE_SERIES_IDS}\n",
+                                     "\n",
+                                     "fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True, layout=\"constrained\")\n",
+                                     "axes[0].plot(ipman[\"timestamp\"], ipman[\"value\"], color=\"#1f4e79\", linewidth=1.8)\n",
+                                     "axes[0].set(title=\"U.S. manufacturing industrial production (IPMAN)\", ylabel=\"Index\")\n",
+                                     "axes[0].grid(alpha=0.25)\n",
+                                     "for series_id, frame in ipman_momentum.items():\n",
+                                     "    axes[1].plot(frame[\"timestamp\"], frame[\"value\"], linewidth=1.4, label=series_id)\n",
+                                     "axes[1].axhline(0, color=\"black\", linewidth=0.8)\n",
+                                     "axes[1].axhline(TARGET_THRESHOLD_PCT, color=\"#b22222\", linestyle=\"--\", linewidth=1.0, label=\"1.5% stress threshold\")\n",
+                                     "axes[1].set(title=\"Trailing IPMAN percentage changes\", xlabel=\"Reference month\", ylabel=\"Percent\")\n",
+                                     "axes[1].legend(ncol=2)\n",
+                                     "axes[1].grid(alpha=0.25)\n",
+                                     "plt.show()"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "dfcea780",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "dfcea780"
+                                   },
+                      "source":  [
+                                     "## Target construction and event frequency\n",
+                                     "\n",
+                                     "A material-contraction month has a trailing three-month IPMAN change at or below $-1.5\\%$. The target is known only once the current IPMAN observation is released, and the forecasting task predicts the target three months ahead."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  40,
+                      "id":  "b921baea",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "b921baea"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "image/png":  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+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1400x450 with 1 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      },
+                                      {
+                                          "data":  {
+                                                       "text/html":  [
+                                                                         "\u003cdiv\u003e\n",
+                                                                         "\u003cstyle scoped\u003e\n",
+                                                                         "    .dataframe tbody tr th:only-of-type {\n",
+                                                                         "        vertical-align: middle;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe tbody tr th {\n",
+                                                                         "        vertical-align: top;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe thead th {\n",
+                                                                         "        text-align: right;\n",
+                                                                         "    }\n",
+                                                                         "\u003c/style\u003e\n",
+                                                                         "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
+                                                                         "  \u003cthead\u003e\n",
+                                                                         "    \u003ctr style=\"text-align: right;\"\u003e\n",
+                                                                         "      \u003cth\u003e\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003etarget_series_id\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003estress_months\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eobserved_months\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003estress_base_rate\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003elookback_months\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003ethreshold_pct\u003c/th\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/thead\u003e\n",
+                                                                         "  \u003ctbody\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e0\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003emanufacturing_stress_3m_decline_1_5pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e320\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.08125\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-1.5\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                         target_series_id  stress_months  observed_months  \\\n",
+                                                                          "0  manufacturing_stress_3m_decline_1_5pct             26              320   \n",
+                                                                          "\n",
+                                                                          "   stress_base_rate  lookback_months  threshold_pct  \n",
+                                                                          "0           0.08125                3           -1.5  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  40,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "reconstructed_target = derive_manufacturing_stress_labels(\n",
+                                     "    series_frames[IPMAN_SERIES_ID],\n",
+                                     "    lookback_months=DEFAULT_LOOKBACK_MONTHS,\n",
+                                     "    threshold_pct=TARGET_THRESHOLD_PCT,\n",
+                                     ").rename(columns={\"value\": \"reconstructed_target\"})\n",
+                                     "registered_target = series_frames[TARGET_SERIES_ID].rename(columns={\"value\": \"registered_target\"})\n",
+                                     "target_check = reconstructed_target.merge(\n",
+                                     "    registered_target[[\"timestamp\", \"registered_target\"]], on=\"timestamp\", how=\"inner\"\n",
+                                     ")\n",
+                                     "if not target_check[\"reconstructed_target\"].equals(target_check[\"registered_target\"]):\n",
+                                     "    raise AssertionError(\"The registered 1.5% target does not match the IPMAN-derived target.\")\n",
+                                     "\n",
+                                     "target = in_analysis_window(registered_target).dropna(subset=[\"registered_target\"]).copy()\n",
+                                     "ipman_change_3m = in_analysis_window(series_frames[IPMAN_CHANGE_3M_SERIES_ID])\n",
+                                     "stress_months = target.loc[target[\"registered_target\"] == 1, \"timestamp\"]\n",
+                                     "\n",
+                                     "fig, ax = plt.subplots(figsize=(14, 4.5), layout=\"constrained\")\n",
+                                     "ax.plot(ipman_change_3m[\"timestamp\"], ipman_change_3m[\"value\"], color=\"#1f4e79\", label=\"IPMAN 3-month change\")\n",
+                                     "ax.axhline(TARGET_THRESHOLD_PCT, color=\"#b22222\", linestyle=\"--\", label=\"1.5% stress threshold\")\n",
+                                     "for timestamp in stress_months:\n",
+                                     "    ax.axvspan(timestamp, timestamp + pd.offsets.MonthEnd(1), color=\"#b22222\", alpha=0.18)\n",
+                                     "ax.set(title=\"Material-contraction target over the IPMAN three-month change\", xlabel=\"Reference month\", ylabel=\"Percent\")\n",
+                                     "ax.legend()\n",
+                                     "ax.grid(alpha=0.25)\n",
+                                     "plt.show()\n",
+                                     "\n",
+                                     "target_summary = pd.DataFrame(\n",
+                                     "    {\n",
+                                     "        \"target_series_id\": [TARGET_SERIES_ID],\n",
+                                     "        \"stress_months\": [int(target[\"registered_target\"].sum())],\n",
+                                     "        \"observed_months\": [len(target)],\n",
+                                     "        \"stress_base_rate\": [target[\"registered_target\"].mean()],\n",
+                                     "        \"lookback_months\": [DEFAULT_LOOKBACK_MONTHS],\n",
+                                     "        \"threshold_pct\": [TARGET_THRESHOLD_PCT],\n",
+                                     "    }\n",
+                                     ")\n",
+                                     "target_summary"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  8,
+                      "id":  "626aaeb5",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "626aaeb5"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "image/png":  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",
+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1400x400 with 1 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      },
+                                      {
+                                          "data":  {
+                                                       "text/html":  [
+                                                                         "\u003cdiv\u003e\n",
+                                                                         "\u003cstyle scoped\u003e\n",
+                                                                         "    .dataframe tbody tr th:only-of-type {\n",
+                                                                         "        vertical-align: middle;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe tbody tr th {\n",
+                                                                         "        vertical-align: top;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe thead th {\n",
+                                                                         "        text-align: right;\n",
+                                                                         "    }\n",
+                                                                         "\u003c/style\u003e\n",
+                                                                         "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
+                                                                         "  \u003cthead\u003e\n",
+                                                                         "    \u003ctr style=\"text-align: right;\"\u003e\n",
+                                                                         "      \u003cth\u003e\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eyear\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003estress_months\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eobserved_months\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003estress_rate\u003c/th\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/thead\u003e\n",
+                                                                         "  \u003ctbody\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e0\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e1\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2001\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e2\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2002\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e3\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2003\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e4\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2004\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e5\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2005\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e6\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2006\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e7\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2007\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e8\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2008\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e8.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.666667\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e9\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2009\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e6.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.500000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e10\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2010\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e11\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2011\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e12\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2012\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e13\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2013\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e14\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2014\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e15\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2015\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e16\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2016\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e17\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2017\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e18\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2018\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e19\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2019\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e20\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2020\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e4.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.333333\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e21\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2021\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.083333\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e22\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2022\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.083333\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e23\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2023\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e24\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2024\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e25\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2025\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e26\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e2026\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e8\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "    year  stress_months  observed_months  stress_rate\n",
+                                                                          "0   2000            0.0               12     0.000000\n",
+                                                                          "1   2001            0.0               12     0.000000\n",
+                                                                          "2   2002            0.0               12     0.000000\n",
+                                                                          "3   2003            0.0               12     0.000000\n",
+                                                                          "4   2004            0.0               12     0.000000\n",
+                                                                          "5   2005            0.0               12     0.000000\n",
+                                                                          "6   2006            0.0               12     0.000000\n",
+                                                                          "7   2007            0.0               12     0.000000\n",
+                                                                          "8   2008            8.0               12     0.666667\n",
+                                                                          "9   2009            6.0               12     0.500000\n",
+                                                                          "10  2010            0.0               12     0.000000\n",
+                                                                          "11  2011            0.0               12     0.000000\n",
+                                                                          "12  2012            0.0               12     0.000000\n",
+                                                                          "13  2013            0.0               12     0.000000\n",
+                                                                          "14  2014            0.0               12     0.000000\n",
+                                                                          "15  2015            0.0               12     0.000000\n",
+                                                                          "16  2016            0.0               12     0.000000\n",
+                                                                          "17  2017            0.0               12     0.000000\n",
+                                                                          "18  2018            0.0               12     0.000000\n",
+                                                                          "19  2019            0.0               12     0.000000\n",
+                                                                          "20  2020            4.0               12     0.333333\n",
+                                                                          "21  2021            1.0               12     0.083333\n",
+                                                                          "22  2022            1.0               12     0.083333\n",
+                                                                          "23  2023            0.0               12     0.000000\n",
+                                                                          "24  2024            0.0               12     0.000000\n",
+                                                                          "25  2025            0.0               12     0.000000\n",
+                                                                          "26  2026            0.0                8     0.000000"
+                                                                      ]
+                                                   },
+                                          "execution_count":  8,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "annual_target_rate = (\n",
+                                     "    target.assign(year=target[\"timestamp\"].dt.year)\n",
+                                     "    .groupby(\"year\", as_index=False)[\"registered_target\"]\n",
+                                     "    .agg(stress_months=\"sum\", observed_months=\"count\", stress_rate=\"mean\")\n",
+                                     ")\n",
+                                     "\n",
+                                     "fig, ax = plt.subplots(figsize=(14, 4), layout=\"constrained\")\n",
+                                     "ax.bar(annual_target_rate[\"year\"], annual_target_rate[\"stress_rate\"], color=\"#b22222\")\n",
+                                     "ax.set(title=\"Annual manufacturing-stress event rate\", xlabel=\"Year\", ylabel=\"Share of observed months\")\n",
+                                     "ax.set_ylim(bottom=0)\n",
+                                     "ax.grid(axis=\"y\", alpha=0.25)\n",
+                                     "plt.show()\n",
+                                     "annual_target_rate"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "a1e31076",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "a1e31076"
+                                   },
+                      "source":  [
+                                     "## Macro, risk, market, and supply-chain trends\n",
+                                     "\n",
+                                     "The source data have different units, so each feature receives its own axis. Daily/weekly source series have already been reduced to monthly observations; SPY and XLI are trailing monthly-origin returns."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  21,
+                      "id":  "06a20c78",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "06a20c78"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "image/png":  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+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1400x1860 with 12 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      },
+                                      {
+                                          "data":  {
+                                                       "image/png":  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WUR5imytCN6K4YRfxzz//aBTu/PnzNSrVVpR2hmEfAoHd2+B4R0ZGFhGC0XZhSVLaYFshTi9evNgn29quXbsiQjBEdlh+eAraK353586dRWwpvEWXLl30GZMktqBd2dKnTx99dtWegxBCCCHEF1AMJoQQQkipg8hSpIfDhgCWBYiSQ4QmfFt/+uknu9/57rvvZMuWLZa/EZUJ71uIqxAnnQGh+JprrlERd+LEiYXeg8/x7Nmz1WfWns+nPSAAwxICwpy1uIzUdEQ42wPiniHu2oLIW0S0wscW/sfW2/j4449rxG1x22gPpKqPGzdOt/GTTz7RdHb87SpLliyxK17DdxhAYAcQYdu0aaP+tvv27bN8DtHEiEx2BiKsraOH8X9E7SIa8qabbiry+Xnz5unxso2kheB34403uhy5Ck9l7Fe0Qexnd0Vy+F1D4H/66aeLWBng99zxjLbnhY22ZN0WcBxgS5KcnCyljTFJ8+CDD1qsDgywDx1Fa7u6rWgz1r8Bf++77767iE+yu8CDHOcnjq09O4gDBw6oxYynXHfddfr84osvFpoAQhQ1JrnsieoQhH/++WeZPHlykfcxWeMLwZ0QQgghxBraRBBCCCHEK0DQsVcUyeCJJ55Q8RDetxCVUKwLUaYGiFzt3LmzikCIuLUteoXITbyOomZIyUY0J6JwUdzMkQBrDYpIQWiB0IioPYgyEIIgSsOjFEKsq0IiPnfPPfeoby6iA8eMGaP+ofCghbiFolW2wAP2zTff1Ijo8ePHqz0GPnvDDTeomArLAgjWWC94qGIbIXwifR4i0mOPPSaeANES2wix7ZFHHnGrIBfENIiaiFCFbQIEWgi+KCiG/W4duYrfxrbhGEFghbCF/dGrVy+ny2jdurUWy8I+AVOnTpUjR47Iww8/rNYWtmDSAPv78ssv13WCbcLChQulRYsWKsq5CqKcsf8h5NavX19/0x1Q0Ave0NhHiEyHyI7fgxAIER2Rx/YK0LkC2jMijrF/r7zySj1vEDWOyGsUGbQXQe5LUNwRQjTaLyJ44fWNCRGsD85DvPfQQw95vK14IAocxxTiMoo5QlDHuVUSSwwI/mgf8M1G9O7w4cP13MS5AIsTRLZj8gntyBPweyisiN9Ae0AbQAT1r7/+qucx7Fls+xQIwfDcRj+G9tG9e3edWEKWBCKGIfZDDCeEEEII8RkmQgghhJAScODAARirFvtIS0sz5ebmmrp3726qWLGiafHixUV+a86cOaYKFSqY+vbtazp79qy+9txzz+n3Fy5caPriiy9MHTp0MFWtWtUUExNjevDBB02ZmZlFfgefHzx4cJHXk5OTTQ888ICpSZMmpsqVK5uio6NNl19+uWnr1q1FPnvTTTfp72D77HHmzBnTK6+8YmrVqpWuT+PGjU2PPPKIKSsrS38fD1veeOMN/TyWbW8dlyxZYho1apSpdu3apipVqphat25teuaZZ0zZ2dkub6Mt+fn5um74/M6dO03u8Msvv5iuvPJKU4sWLUw1atQwhYeHm7p06WJ6/fXXdTtt+eijjyzbh2U+++yzup/srSv+xuunTp0y/fe//zXFxsbqNrdp08b0/vvv63pb88033+jn8Txt2jRTr169TNWrVzdFRUWZbr75ZlNCQkKR9XF0HAyefvpp/c0nnnjC5ClolxdffLEpMjJS179Ro0am8ePHm5YvX275jHUbtsV6u6yZMmWKnivY72inOA779++32y7xu3gNy3F1+4397w5Tp041nX/++doO0OabNm1quuGGG0zbtm1z6byxtx9wnD/99FM9r6tVq2aqX7++6bbbbjMlJSXZXUdH+8vZtk6ePNk0fPhwU0REhLbNhg0bmoYMGWJ66623tE9wtn7FLRf91EsvvWRq1qyZHv/mzZubXn31VdPq1av18+ijbElNTdW217FjR23DtWrV0vPm2muvNf3+++8ubxchhBBCiCdUwD++k5oJIYQQQkoGoo1feOEFjf5EVCRxD3ivwgsXEbuIWA0UcCwRqe3qrei3336rHsiI4PbEMsMeiDKGbzCi1WETQoi3gE837Ck+/vhjzXYghBBCCAkU6BlMCCGEEFKOQSo6Uu8pSBVmx44dKgSPGDGCQjDxGNiC2E5owD4DdjTwlMaEAyGEEEJIIEHPYEIIIYSQcgaKsKFgHPxwEaHYvn179Z4lov6uu3fv1oKE4LnnnuNuIR7z2muvyYwZM2TgwIHqowzv3+nTp2vxRGQ1wBudEEIIISSQoBhMCCGEEFLOSEtLkwkTJmjhMRQh+/TTTzVKkYgWLVy6dKkWe/vqq6/kvPPO424hHoNChIgyhyCM8w7nHAphosAkissRQgghhAQa9AwmhBBCCCGEEEIIIYSQIICewYQQQgghhBBCCCGEEBIEUAwmhBBCCCGEEEIIIYSQIIBiMCGEEEIIIYQQQgghhAQBFIMJIYQQQgghhBBCCCEkCKAYTAghhBBCCCGEEEIIIUEAxWBCCCGEEEIIIYQQQggJAigGE0IIIYQQQgghhBBCSBBAMZgQQkiZoWnTpvLuu++6/Pnnn39eunbtWuLlVqhQQaZNmyZlBW9ttysMGTJEHnrooRL9xrfffiu1a9f22joRz88ZeyxatEjPgfT0dO7aMniOlsZx9eS3vNE2y1rf4Y1tdoWbb75Zxo0bV6Lf4HkfOJS1exBCCCGBD8VgQgghhPiVq666Svbs2cOj4APWrl0rd955Z5nct5hogAjyyy+/FHodYhpEtdJg8eLFUrlyZVm2bFmh13NycqR58+by6KOPSmmBbcb+sH40atTI58vduHGjnqMxMTFStWpVadKkiVx88cXy999/i8lkkkAHx3Do0KESGRkpNWrUkFatWslNN90kZ86ckbII9vnnn38uffr0kVq1aqkY3rNnTz0vTp486bXlnHfeeZKQkCDh4eEe/0Z8fHyh9hoVFSUXXHCBtqlgwBuiPCGEEOILKAYTQgghxK9Ur15d6tat69d1CERhyBvrVKdOHRXAyirVqlWTp59+Ws6ePeuX5Q8ePFjuv/9+FXUgABv897//1Xb78ssvuy3knTt3zuP1efHFF1WgMx6+FtX+/PNP6du3r2RnZ8ukSZNk586dMmvWLLn00kv1uGRkZEggs2PHDhk5cqSKpUuWLJGtW7fKBx98IFWqVJG8vDwpi9xwww2ajTF27FhZuHChbNq0SZ555hk9VnPmzPHacrCP6tevryJuSZk3b56219mzZ2tbGjVqlMMocn+d684IxHUihBBCSgLFYEIIIQFBVlaWXHfddVKzZk2NQHvnnXeKtSA4dOiQDogRHRUWFiZXXnmlHD9+vMjnPvvsM4mNjVVRDJ+xFjAQOTlixAiJjo7WCCiIPxs2bPBYvLvvvvt0/SFiIYJu4sSJhSKkMHA3wGAYryEd1zotd8aMGdK5c2f9DQgx27ZtK5IWjZRRRLjhMxdeeKEcPnzY7jpBAEFkY2JiYqHXsV8HDhzo0nYtX75cjwX2X0REhC4vLS3N8n5+fr6KY4i8g3iAFHhr3n77benUqZMeWxyHe+65RwUB222yTaH//vvvNRoSx+Xqq6/WNuIKWFccBzzwXRxbiCXWUYz43ZdeekluvPFGbTtG9CwiQLFfIPRhXR944IFCIuDHH39s2e/16tWTyy+/3PLelClTdDvxXUTADR8+3PJde20ZEWMQGUu6Tu6kpaN9ffnllyrmGVGSf/31V6Hv/PPPP9K6dWtd3vnnn69t1xZn6/Tdd9/pObl3717L53HM27Zt63bk4jXXXKPnyRdffOH0cxDCunfvrscFEbsvvPCCRXRF9C4iWQ2wP7AfIGoatGzZUveLPV599VUVxh5//HH9GwIcPovtxO9g2zGZgWUPGDBA+xQD45yeOXOm9OjRQyNrbaOMwf79+3W90WadRduGhobqOWY8IPYb5yD6mmbNmukx6dKli7ZHd4+rNTiet912m4wePVr7JER0Yh3btWunr2/evNlp1OjUqVOlQ4cOus1oh2+99VaRz+CcxjFG39CwYUP56KOP3Oo7igPiKPbTG2+8IR07dpQWLVqoOIz2hP3gyLLDNvrciPBEu8I+x/l51113FZqwcaXfsebWW28t1C4N0RFt6auvvrL7nV9//VV+/PFH+fnnn+XJJ5+UXr166XriOrhgwQI9rta8+eabej1Cf3TvvfcWEjXRv0IkN9rUtddeK0lJSQ5tIox+GoIu2gDOcexLiLzFgeVjGVge1gnX6dWrV1uui5MnT9ZrL84hbB/AOYbl4DX0Heh7XbnWYn/jmDZu3FjbXoMGDfQcdWa5gO3C9gFP18kV0EawLs6ul+g3Bw0apMto3769zJ07t8jv4HqPexmsN34Hx984n3ft2qV9+08//VSo3aC9Y3KEEEIIMS6YhBBCiN+5/fbbTU2aNDHNmzfPtHXrVtOll15qCg0NNT344IOWz+D9d955R/+fl5dn6tq1q2nAgAGmdevWmVatWmXq0aOHafDgwZbPP/fcc6aaNWuahg4datq4caNp8eLFppYtW5quvfZay2fmz59v+v777007d+407dixw3TbbbeZ6tWrZ8rMzLR8BpfLP/74o9ht+N///meKjY01LVmyxBQfH29aunSp6aefftL3Dhw4oL+D9TBIS0vT1xYuXKh/4xl/t2vXzjRnzhzTli1bTBdffLGpadOmpjNnzuhnvvnmG1PlypVNPXv2NK1YsUK3vXfv3qbzzjuv0HZ36dLF8nfr1q1Nb7zxhuVv/FZ0dLTp66+/LnabsL5Vq1Y13X333aZNmzaZtm3bZvrggw9MycnJ+j72d1hYmOn555837dmzxzRp0iRThQoVdP0NcMwWLFig+wD7u02bNvp7Btim8PDwQutfq1Yt02WXXaZtAfuzfv36pieffLLY9TXWCd9H29m1a5fphx9+MNWoUcP0+eefF2pLWO8333zTtG/fPssD7QXri21Zvny5qVu3bqabb75Zv7N27VpTpUqV9Jji+G7YsMH03nvv6XvHjh0zhYSEmN5++23dThy7jz76yJSVlWVZJ+u2DMaOHWu66aabSrROxWF9zgC0r0aNGuk27N271/TAAw/ovjpx4oS+f+jQIT3eDz/8sGXf4XzA99BegSvrdMUVV5h69eplOnv2rGn69OnaZtFWrY8x1q2444h9hn2KdcjOztbXsVzr76J9YL99++23pv3792vbwzmDNgn++usvbV/nzp3Tv8eNG6ft//HHH9e/jxw5otuH/eEIHHtsw7Rp0wr9NvZfgwYNTP/8849p+/btejwjIiIs+9M4pzt37qzrhX2H96zP0c2bN2v7fuqpp9w6lta8/PLLprZt25pmzZql+wDnFI7jokWLXD6utvz+++/6/sqVK52ul/V2Gr+FY12xYkXTiy++aNq9e7euT/Xq1fXZenvQx0+cOFE/8/777+v5VZK+w5aff/5Ztxt9vyNs+0t7bQzHFefJVVddpX0g2nSdOnUK9Umu9jvGMcR5g+1F32G9z3FuGf2GLZdcconug+LA+uKcuOuuu/Ta9vfffxdZl6+++krbLdoLjnG/fv1Mo0aNcnhMjWvP8OHD9XxYv369Xqusr6e22Lvuod/EazgvjfdxTk2dOtUUFxen+wP7LiYmxvIaniMjI/UcL+5a+9tvv+m2Y9sOHjxoWr16daHttnc9Rxsy2qan6+ToOKCfNyjueon7mo4dO5qGDRum11u0W/St1uuM6zf2+6233qrXGdy34BigXeTm5upncO3BNmH7Dx8+rH2Sca0ihBBC9HrI3UAIIcTfQHjFIBODOIP09HQdvDoSgzF4wkAaIocBxBgMmtasWWMZ5OMzEHsMZs6cqSJFQkKC3XXBYAwCBQbP7orB999/vwrP+fn5Rd5zRwz+5ZdfLJ+BcAQRZfLkyfo3Bqz4DMRvAwz28RoGvfbEjddff10HjwYYxEK0MMQ1Z1xzzTWm/v37O3wfg1sI8tZABDSENnvgOEdFRTkVg3HsrQX5xx57zNSnT59i19dYJ2yv9XHA+ljvA7QliILWYCLgzjvvLPQaRAa0l1OnTul+w0Deer0MIIzgGECYcLROrojB7q6TJ2Lw008/bfkbbQCv4bwAEyZMMLVv377Qb2DfWYtCrqxTamqqis4Q7iA6vvLKK4U+jwkFnCvOMPbZ6dOndTsgLNoT6iCcvPrqq4W+iwkeCDcA6411g4CFNgEBBwKk0Z4g8jRs2LDYffnss8/q72DSCSI39h36rR9//NHyGQg1EIeNyRfjnIaIbI1xjkIQhFCDCYDiwDZXqVJFxULjAYEH+wfnCyaHrMFxwvnr6nG15bXXXtP3cSwN0LdaL9/oJ22FQ4hTI0aMKPR7OIet1wHbM3LkyEKfgdhqLUi623fYggkATFJg3SC44/xC28vIyHBbDEa7ycnJsbz2ySefaD+Ka4Y7/Y71+Yj9gf7ZYMyYMU4nevBbEISLA+uLZRkTIMYEDfavI3B+YD8ZQrQ9MRh/Y0LDAKIjzm9H2F738FuY6MV+S0xMtLz/7rvvFvpeixYtLOKuwUsvvaSCdXHX2rfeeksnQI0JVFtcFYPdXSdXxWBn18vZs2frpOLRo0ct76Nvtl5n9G0Qfq23HSIw7hPwfYPRo0ebBg4cqP3jBRdcYHdfEUIICV5oE0EIIcTvxMXFafpq7969La8hzbZNmzYOvwPvSqQN42GAlEqkTeI9A6SKIv3YoF+/fppSvXv3bv0b6ap33HGHpstjmUj/RRoyLCjcBanEsIHAeiMV1FP/RqyjAVJA8XvW2xQSEqLpwQZIV7Xdbtv12rdvn6xatUr/RjosUkyRel0c2J5hw4Y5/QwsLaxB6q51ujH8IvEbOA5ISYbn5YkTJ5xaBiD1GZ919JvFAXsNa69L7FOk31r7hCJl2RqkvWPfIP3ZeMASA+3lwIEDaieCdGSkymMbkDpsbAPS8rGNSGm/4oorNA3d2krDVdxdJ0+wPl5oA2jzxr5FG0JhKkft0dV1gp0IUt0/+eQTTc1/4oknCv0GUrznz5/v0voi1RteuUgvT0lJKfI+1gfvW68Pzmmkr+P44NzA8UHaOzxjYfkACw747eJcR4ExpIMXB1L+sY3YFpyDsHZAv9W/f3/LZ2DJgn7M9ly0Pa4AfQza1LPPPiuPPPKIS/viscce03PSeMBSBOc2thO/Zb0PYGOBdXT1uLradoxlw0bCkf8xlme9XwD+tj0HbdcBf1vvO0/6DmsqVaok33zzjRw5ckStIvA7sP2AfYUr9gbWoA1Z+29jXdF+rC16XOl3rLn99tt1/YxrEexEYB/hCHcK9mEbsf2O+tD169fLmDFj9BqJfWucA86ufdh+nM+OftNZMTq0SfQLOF9hwQCbHXvnB9oV2i2sSKzbM/y5jfbs7FqL/vfUqVPaT6Mf+OOPPzzy6XZ3nVzF2fXSuK+BtYWz/hfnPI6ZsR64Tzh9+nShdfn6669ly5YtanuF/tob3s+EEELKDyH+XgFCCCHEn6CqPMSF9957T4U+CE8YfHlSvAuepRDDMKCHiAHBFb6x8O6sWLFikcF8aRWlgQclBv0QHeApivUzfIqLw/DVdAYEMGsw6IRoBuBjCF/Mu+++W1555RUdtMIzFYNq7GNHxc2c/aa3sBXDIez85z//KeQvaQDBBCIiBtbYdxAfIOLB7xEesRAc4e24YsUKfQ9Fqp566in1xcQ+x/G3FXLsHX9318kTSrpvXV0n+FVDjILoBjHFWtx3l+uvv17FYIgv1l6uxvrAy/Wyyy4r8j34bhpenThuOL8heqEdwvsTbRFisCtiLARg62d3sDfxAu9ZiD7wf4UACFG+OOBDC39ja4yJLfj6Wk98AWyvp2CCzPh9iJzG79ku31d42nfYA/sFQjIe8OWGd/Knn36q7cbVc9MXQMzH5MLKlSu170Bf4czLHesNT9iSnuc4HzGBgwcmtdAWIQLjb2fXPnu/6YpADfEXk7XwDrb2h7d3fhie0JhQs53AMMRtZ9daiKlos3gdfTJ8pv/3v//peY71t7fOxfXFrqxTafa/8B83fIytMTzEDdEYxxntG30wRGdCCCHEgJHBhBBC/A4ieDBAsi68hCJve/bscfgdCDmIyLKOykJxFBS7waDTAAPcY8eOWf5GdCwGR0bUMYqjQdS66KKLLMWO7EUfugoEnauuukoHjRgAo4hSamqqZZBmHY1mXUzOGiOCFyC6FPsB22uAKKd169ZZ/sbAF9tt/Rl7EWhYn88//1wju2yj9pxFMbkawWkPRJ9hoIviURCUIGZYHw9fARHWdp9C3HI2cIfAgDYEscv2ASHYEAIhOiDKEFFXEKxQuMkY1GO/QmBC1Cm+g6g0gONvfewRKWhdGLAk6+RN0IbWrFnjsD26uk4Qtl5//XX5+++/NXINkcAlAecsCkQh0ti28BnWB+eAvfUxJmEgAENIRFuGMAzwDCEW55fxmjvgPML2og+xFpXQj1n3Qc4mWqZPn24pAulqgURbsCz0W+jrbLffyJxw5bjagoJxEGBxHN0Fy7PeLwB/4/y3Pgdt1wF/G/2Yr/oORKdCGDMKHuLcRIFNa4HQXt8McQ0Rp9brirZtnZ3ibr8DcRSF6TBRh+jNW265xem6o8gb2isKJtqC9bcujuoMCMqYBH3ttddUfEZ2iTuZF+6CfYTzxZ4QbAsihjFJgowh2/YMsby4a61xbmEC9P3339dJIIjtyAqw1xcjcru4SHNX16mkGPc11utnr//FOmOS13ZdjIKO2A+InsaEJJ5RnNe67RJCCCEUgwkhhPgdRAwiQhcp0AsXLpTt27dr9BeEHEepjRDkkJKPQQ6iNSF0IMoKoo91eieEFvw2BvJLly5V4RdRRKjiDTBQR1V1pGdiII/fcyUa1h6ofA9xCQNtDNh/++03XQ4GwPhNCBoYfGNZiFJ6+umn7f4OUt4hWkEsxEAO0YAQDAwgnN9///26vhBM8Bn8trXNhi0QmzB4RmRlcYKDNRMmTFBxC9FVED+xbRDkXBXMMUCFQIZIWQyksa8RkedrIIw9/PDDKhLimGD5Dz74oNPvPP744ypiQriEGIQBN0QXQ8iEcAdxAe8dPHhQ0/AhVmFiAccC6ecQ6bHs33//XZKTky3C1tChQzVyEw/sQ0Q7QsAvjuLWydvcddddugyci9h3qEgPkcqddYKoiQhMnGujRo3SCDaINYjaM/jwww+LtR+xZfTo0RqV99lnnxV6HRHaOBYQ4dF34Pz65ZdfCp1fgwYN0vXCMbQWg7FuEAYhNLoLIgdxHLGvZs2apQI50tIhLKH/cvU30CYwyYB9ZUQgutt/Pvroo/J///d/MmnSJE0VR5+INo+/XT2utkDo/PLLL3X9sO9nz56t5zD6AUyGAEciJyKt0YchChd9IdYDxxzraSsQ47fwmY8++kj7TOM89UbfgbaCY4RofewXtA+0XzxDLDTaAc5VrAc+g/VAxKktiJjFccVx/ueff+S5557TNm9MOHja72CiDvsH7RbXKmfg2gUB9JprrrH0N+iL0K5xTcT10xWMTAdj3/711196rAIFnMuY/EF/i7YBIReCOa6xxV1r0a5hUYPrJ7bthx9+0OsvMn+MvhhtERN22H84N2yjdT1ZJ2+AY4i+yPqeBYKuNbhHwT3B2LFj9X1ESEPwRn8LOxSAbYIAjz4Q64fJR9tzjxBCSHBDMZgQQkhAgAEL7BmQFowBESIsIaQZad62QCSGAIUoLwg9+A4ijCE6WQNBAenjiPxFpBsiXT/++GPL+xg0IvoW0TaGgIWIG0+AKANBAWI0PH0RwQjRwBAL4OGHqF6keD700EMqzNoDgjEEBHwOEWuIrrSOAkV6NAQNRIlhP0G0sd1uW7AOEI0xKIRo7ioYmEJIwcAUYjOOEfa7q6ny8NnEsUV0YceOHVV8w4Da12AbEQmFdb733nt1f8In1hloGxDpMdBHtFy3bt1UaDT8GyE0QOSFmIC2CWEKggQiyiG0wxYB7Qz7DINwRDRC4AOwAcAA35iwQFs9//zzi92O4tbJ20AkQoTdtGnT9NhhGyE6ubNO2NcQOY3vYdIG/4e1xNGjR/U1TCa467UJ0I7gjWk70QExDO0U5x0mRt555x2L+APQT2A9EBWIKEiAfgNivit+wY7AuTp+/HjtO9CHwMsToimW5yo4fyE+IrIToqsRseoOEPLgaYxzC21z5MiRKuIaUYuuHFd7XHrppSr8o89B28XEB9o/ouEhuKO/tgf2xa+//qqfwXmP9oFJLvRBtqIxBDm0IfSH6CtwPL3Vd+D8h8AOcQznKY41Ii2xH4zjjv2FawJEYCwTE4v2hDNMXmDyEO0Gguwll1yiNjEl7Xdw7cKEBLa7uPMa1z0I+dgvxjbgfMR6QBw09l1x4DyAaAoRFZHlaMewYQkUIJBjIgJiK85bbCfW12jPzq616KcRLYxrI/YN7CJwDUUUNkC/DKEUfReuoTjWrliOFLdO3gDrj2wSow1hmbBIsQbrimsNzmnc26D9YpIC/SKuQ5gYw77A5Amu0+iLIYhjn9ib5CCEEBKcVEAVOX+vBCGEEGILBBH4PGLg5mqUXVkH0T0QCCFOO0qnxeATQrIrUaW2YD8iAg5RYOUZRPp17dpV3n33XX+vCiGkHAARG30uBFhv9zsQq3Gtg8hoz/eaEEIIIcTbsIAcIYSQgAApm0j5RDQMfA8RRQYQ7URKBvYnUloRUVbehWBCCCkLICodEfKY8MTkHyKNCSGEEEJKA9pEEEIICRiQpoo0XaTNIjIYfnjwxgsUkFaNlG57D8MOIBCBoA6LDKRKjxgxotB7WG9H2+RKGnlpA09OR+uLB94PFnB+ONsXhJDABX0VCpNhkg4WQq5a7xBCCCGElBTaRBBCCCEuggrdRrVyW1CgBqm+ZQ14uDqqMh4ZGamPQAKey/CHdETTpk2DRlTBcTM8eO0Bv2xCCCGEEEIIsYZiMCGEEEIIIYQQQgghhAQBtIkghBBCCCGEEEIIIYSQIIBiMCGEEEIIIYQQQgghhAQBIYFeZffYsWMSGhoqFSpU8PfqEEIIIYQQQgghhBBCSMBhMpkkKytLGjRoIBUrViybYjCE4NjYWH+vBiGEEEIIIYQQQgghhAQ8hw8flkaNGpVNMRgRwcZGhIWFSbCAiOi0tDSJiIhwquQTUpZhOyfBAts6CQbYzkkwwHZOggG2cxIssK2T8khmZqYG1Rp6apkUgw1rCAjBwSYGnzt3TreZYjApr7Cdk2CBbZ0EA2znJBhgOyfBANs5CRbY1kl5pjirXYadEkIIIYQQQgghhBBCSBBAMZgQQgghhBBCCCGEEEKCAIrBhBBCCCGEEEIIIYQQEgRQDCaEEEIIIYQQQgghhJAggGIwIYQQQgghhBBCCCGEBAEUgwkhhBBCCCGEEEIIISQIoBhMCCGEEEIIIYQQUgqczcuTlbuPSO7Zc9zfhBC/QDGYEEIIIYQQQgghxMccPZEpN3/wl9z+yXT5Y/Vu7m9CiF8I8c9iCSGEEEIIIYQQQoKD2Zv2y3O/LJas02f0793HTvh7lQghQQojgwkhhBBCCCGEEEJ8gMlkkpenLJWHv50r5/Lz5cWrB0ulihXkwPE07m9CiF+gGEwIIYQQQgghhBDiA7YeSpKfl22X1g2iZOpjl8v4vu0kNipM4pLSub8JIX6BYjAhhBBCCCGEEEKID1i156g+33NhD2lSp7b+v1m9CDmRdUoyTuZynxNCSh2KwYQQQgghhBBCCCE+YOWeI1KhgkjvVg0trzWraxaFaRVBCPEHFIMJIYQQQgghhBBCvMzpM+dk04Hj0r5RHQmvUdXyevN6BWIwrSIIIX6AYjAhhBBCCCGEEEKIl9l4IFHOnMuTfq0bFXrdiAyOO07fYEJI6UMxmBBCCCGEEEIIIcQHFhGgb+t/LSIMz2DAyGBCiD+gGEwIIYQQQgghhBDig+JxVUIqSbdm9Qu9DsuIqNDq9AwmhPgFisGEEEIIIYQQQgghXiQ957TsOJKsQnC1KiFF3m9et7YcPpGpNhKEEFKaUAwmhBBCCCGEEEII8SJr9x0Tk6moRYS1VURevkkOpWRwvxNCyqcY/Nprr0mFChXkoYceKq1FEkIIIYQQQgghhJQ6q/Ye1ee+rRyIwQVF5A6wiBwhpDyKwWvXrpXPPvtMOnfuXBqLI4QQQgghhBBCCPEbq3YfkdBqVaR9bB277zdnETlCSHkVg7Ozs+W6666TL774QiIizBUzCSGEEEIIIYQQQsojCWnZEp+cIb1aNpCQSvZll+b1zJHBccfTSnntCCHBTlEXcy9z7733yujRo2X48OHy8ssvO/1sbm6uPgwyMzP1OT8/Xx/BArbVZDIF1TaT4IPtnAQLbOskGGA7J8EA2zkJBtjOvcPK3Yf1uXerBg7H9XXDaki1ypUk7ng6x/5+gG2dlEdc1RF9Kgb/8ssvsmHDBrWJcIWJEyfKCy+8UOT1tLQ0OXfunATTwcvKylJBuGJF1vgj5RO2cxIssK2TYIDtnAQDbOckGGA79w5Lth3Q53b1aklqaqrDzzWKDNXI4BMnTmiNJVJ6sK2T8gi0RL+KwYcPH5YHH3xQ5s6dK9WqVXPpOxMmTJCHH364UGRwbGys2kuEhYVJMHVKuBBguykGk/IK2zkJFtjWSTDAdk6CAbZzEgywnZccBHVtOpQidcJqSLdWTZyKvK0aRMm+4+lyrlI1qVe7pheWTlyFbZ2UR0JCQvwrBq9fv16SkpKke/fultfy8vJkyZIl8uGHH6odRKVKlQp9p2rVqvqwBYJosImiuGAE43aT4ILtnAQLbOskGGA7J8EA2zkJBtjOS8bB5AxJzjwpo3u0LKJ5OCoidzAlQ2IiQ0u4ZOIubOukvOGqhugzMXjYsGGydevWQq/dcsst0rZtW3n88ceL7RQJIYQQQgghhBBCyhLbDyfrc+fG9Yr9rFFEbv/xdOnbupHP140QQnwqBoeGhkrHjh0LvVazZk2Jiooq8johhBBCCCGEEEJIeRGDOzSuU+xnmxVEBh84nubz9SKEEAN6EBBCCCGEEEIIIYR4SQyuWKGCtG0YVexnm0SHCyyFDySlc98TQsp+ZLA9Fi1aVJqLI4QQQgghhBBCCCkV8vNNsuNwsrSsHyHVq1Qu9vPVqoRIw8hQOXCcYjAhpPRgZDAhhBBCCCGEEEKIF4rH5eSelQ6xxVtEWBeRO56RI9mnz3D/E0JKBYrBhBBCCCGEEEIIISVk++EkfW7vhhjcrK65iBytIgghpQXFYEIIIYQQQgghhJASsq2geFxHd8TgegViMK0iCCGlBMVgQgghhBBCCCGEEC8UjwupWFFaNyi+eJxBbFSYPh9NzeL+J4SUChSDCSGEEEIIIYQQQkpAXn6+7DySIi1jIrQwnKs0iAzV52MUgwkhpQTFYEIIIYQQQgghhJASAJuHU2fOuVU8DtSvXUsqVGBkMCGk9KAYTAghhBBCCCGEEOIVv+C6bn2vSkglqRdek5HBhJBSg2IwIYQQQgghhBBCSAn9gkGHxu5FBhtWEQlp2Wo1QQghvoZiMCGEEEIIIYQQQkgJ2HE4WSpXqiitYiLd/m6DiFA5l58vyZkni/3s0p2HZOXuIx6uJSGEUAwmhBBCCCGEEEII8Zhzefmy82iKtG4QpbYP7mIUkTtaTBE5k8kkj38/X57+eZHH60oIIYwMJoQQQgghhBBCCPGQ/cfTJPdsntvF4wwaFojBx4oRgxE5nHEyVxLTs+X0mXMeLYsQQigGE0IIIYQQQgghhHjI9kMFfsElFIOLiwzel5hq+f+R1EyPlkUIIRSDCSGEEEIIIYQQQjxk2+GkEonBDVyMDN6b8K8YfCiFYjAhxDMoBhNCCCGEEEIIIYR4QGr2KVm795h6BbeMifBoH8ZE1JIKFYqPDN6fmGb5/+GUDI+WRQghIdwFhBBCCCGEEEIIcZfj6dkqgkbUqh50Oy/n9BmZtGiLfLNws5zMPSsjujSXypXcLx4HsA/rhNUoNjJ4X8K/YjAjgwkhnkIxmBBCCCGEEEIIIW5hMpnk2nenSbO64fLlPWOCau/N2rhPXp6yTNJyTkt0WA15ZExfuaxv2xL9Jqwith1Klvx8k1SsWMHu/kahusbRYSoEH6ZNBCHEQygGE0IIIYQQQgghxC1Ssk5JYnq2nMw9o0JlBfgcBAFnzuXJs78sFpOIPDS6t1w3qJPUqFq5xL+LInKbDhyX5MwcqVe7VpH3E9NzJPv0GRnUvrGcPntODtEmghDiIfQMJoQQQgghhBBCiFvEJ6Xrc+apM5JxMjdo9t6qPUckJ/esXN2/g9wxortXhGBDDAaOfIP3FRSPa1k/QhpHh0tCaraczcvzyrIJIcEFxWBCCCGEEEIIIYS4xYECMRgcSg6eYmbzt8br87BOTb36u7CJAI58g/clGmJwpMRGhcm5/HxJSMv26joQQoIDisGEEEIIIYQQQgjxKDIYHAwSy4K8/HxZuDVefYI7N6nn1d9uEFFMZHCiuXhcy5gIiY0O0//TN5gQ4gkUgwkhhBBCCCGEEOIWwRgZvDn+uJzIPiVDOza1W+TNGzYRxxxE+yIyuGrlStIoKkxtIgB9gwkhnkAxmBBCCCGEEEIIIW6LwVVCKgVVZPC/FhHNvP7bMRG1HNpE5OebZH9imjSvFyGVKlZkZDAhpERQDCaEEEIIIYQQQojLnDmXJ0dPZEmXJnU1WvVQcma533smk0nmbz0gtapVkd6tGnj996tWDpE6YTXs2kQcS8uSU2fOafE4EGuJDC7/+50Q4n0oBhNCCCGEEEIIIcRlYE+QbzJJs3oRWswsGOwK9iakqkfvoPaNLRHR3gZWEYgMRiSwPb/gFvUj9Tm8RlV9HA6C/U4IKWNi8CeffCKdO3eWsLAwffTr109mzpzpy0USQgghhBBCCCHEh8QnmUXIpnXD1b8242SupOecLtOF4bJO5bpoEdHUZ+vRIDJUzublS0rWyUKv70tI1edWMWYxGGC/Hz6RWUQ4JoQQv4rBjRo1ktdee03Wr18v69atk6FDh8rYsWNl+/btvlwsIYQQQgghhBBCfFw8rlnd2tKkTtkvZvb+jLVy/nPfy5ETjm0XYBFRuVJFGdiusc/Wo0GEuYicrVUE/IKBYRMBYqPDJPdsniRl5vhsfQgh5ZMQX/74mDFjCv39yiuvaLTwqlWrpEOHDkU+n5ubqw+DzExzR5yfn6+PYAHbCj+iYNpmEnywnZNggW2dBANs5yQYYDsnwYCr7fzAcbM42SQ63FLwLD4pXTrG1pGyxukz52Tyiu3qyfvH6l1y78ieRT6Dbdx5JEUGtouV6lVCfDZWj4moqc9HUjLVj9lgb2KqLrd+eE3LsmHPAQ4mpUvdsBo+WZ/yDPt0Uh5xtW/yqRhsTV5envz222+Sk5OjdhH2mDhxorzwwgtFXk9LS5Nz585JsICDl5WVpRfhihVp60zKJ2znJFhgWyfBANs5CQbYzkkw4Go733ssRaNkq8lZiaxq/tyew8cltVmUlDXmbjskWafO6P+nrd4lV/VqJhUrVCj0melr9upzn2Z1JDXVbNngC8Iqm5/3H0uS1GZmS4i8fJPsT0yVZnXCJT3dLMKDyGrm/b7rYIK0iKzms3Uqr7BPJ+UR9N8BIQZv3bpVxd/Tp09LrVq15I8//pD27dvb/eyECRPk4YcfLhQZHBsbKxEREeo5HEydUoUKFXS7KQaT8grbOQkW2NZJMMB2ToIBtnMSDLjSziEUH0nNUXuIOtFR0qFSVX09OeeMREb+62lrS/bpM/LJ7PVy+7BuElErcMTLOdtW6PN5bRrJit1HZH/qaenTqmGh7V26J1GgD4/u014iQ30XhdvmnHmfp506Z9mXh5Iz5My5fGnTqE6h/duuqVnATj2d53S/E/uwTyflkZCQkMAQg9u0aSObNm2SjIwMmTJlitx0002yePFiu4Jw1apV9WELLkLBJoriAhyM202CC7ZzEiywrZNggO2cBANs5yQYKK6dp2afksxTudK7VQP9TExEqFQJqSSHUjKdjl9nbYyT7xZvldjocLl2YEcJBA4mZ8iafcekX+uGag8BMfjPtXukX5tYy2dW7j4im+KPy5AOTaRueC2frk/DSHMQ3LG0bMu+jCvwZ24dE1lo/zauU1ufUUSOuoFnsE8n5Q1X+wKfK41VqlSRli1bSo8ePdQGokuXLvLee+/5erGEEEIIIYQQQgjxYfE4ULFiBS1mBjHYGfC9Bek5pwPmmPy+apc+j+/bTro0rafbNHfzAY1iNqKC35uxRv9//0W9fL4+1aqESHRYDYsPM9hnKR5XOPo3OrS6+ggfLma/E0KILRX9EYpvXSSOEEIIIYQQQgghZQMUigNNC8Rg0Dg6XEXejJOOx/r7CsTgtAARg8/m5cm0tbulds1qMqxzM40SHde7jZw+e05mbdyvn1m4/aBsPZQko7q1kLYNo0tlvRpE1NLI4Px8U6H91iImotDnsL6IsoYID9GaEEICQgyGB/CSJUskPj5evYPx96JFi+S6667z5WIJIYQQQgghhBBSCpHBAP7B4FBKhsPv7U8wR7imZQeGGLx0xyFJyTwpl/RsrTYX4JJerbV43B9rdqkY+8GMNfr3vSN9HxVs0DAyVM6cy1MR+N3pq2XB1ngJrVZFYmoXtahoHB2mUcyBFG1NCAl8fOoZnJSUJDfeeKMkJCRIeHi4dO7cWWbPni0jRozw5WIJIYQQQgghhBDiA+KTMuxEBodZip11alzXrs/wiexT+v+Mk4EhXE6xWES0tbxWN7ym9G/bSJbuPCyfzlkvexJS5bI+baVZvX+31dc0iAzV58vfnCJ5+SapG15DJlw2QCOBbYE9B0B0cESt6qW2joSQso1PxeCvvvrKlz9PCCGEEEIIIYSQUuTA8TSJrFVNwmtULWQTYRRks8e+BLPVQaDYRBxPz9bI4K5N60nLmMJevLCKgBj80ax1ElKpotx9YY9SXbfWDaL0uU5YTbl9eFcVo6tWti/dNI4y7/fDKRnqeUwIIX4XgwkhhBBCCCGEEFI+gM/ukRNZ0rlp4ehfwybCUTEzowgaCARLgzmb4yTfZJJL+/wbFWxwfsemElajqmSezJWrzmtvidQtLS7q1lIjreFRbNhXOKJxnX8jgwkhJGALyBFCCCGEEEIIIaTscSQlS87l5xfyCwb1ateUypUqOo4MLiiCVr1KSECIwRsPJOrzeW0bFXkPUbjXDugodcJqyB0jupf6ulWsWEE6N6lXrBAMYqPMYnB8stnHmRBCXIFiMCGEEEIIIYQQQorlQIHoaCsGV6pYUWKjwx0WkNufmCaVCkTOU2fOyekz5/y2t00mk4rB9cJr2i3KBu6/qJcsfOEGFYQDmZiIUN2O5buOaNQ2IYS4AsVgQgghhBBCCCGEFEt8UnqR4nEGsDaAHzDsFWzFV9hENIkO12JoIN2PReQS0rMlKeOkdG1Wz25RNgNn7wUKiCK+oGtzjbZeteeov1eHEFJGoBhMCCGEEEIIIYQQl8Vg28hga99g2+jgE9mnVKxsERMptWtW09fSsv0nBm86cFyfuzatL+UBeAyDmRv2+XtVCCFlBIrBhBBCCCGEEEIIKZa44+kSUrGiNIwqWlStcXS43WJm+xLMfsEt60dIRIEYnOHHyOBNBX7BiAwuD3RqUlcaRobK/K3xcuYcrSIIIcVDMZgQQgghhBBCCCHFcuREpgrBlSsVLW7WuI65mJltEbl9CWkWMTggIoPjj0vVypWkbcNoKQ/AzmJUt5aSffqMLNt5yN+rQwgpA1AMJoQQQgghhBBCiFPg/QsRN7JWdbvvN6sboc+b482Rtwb7Eo3I4H9tIvzlGXwy96zsOpoiHWPrSpWQooJ2WWVU9xb6/M+G/f5eFUJIGYBiMCGEEEIIIYQQQpySk3tWzuXnW6webImJqCXdmtWXZbsOawSxAYrHwVqicZ1wv0cGbzuUJHn5pnJjEWHQpkGU+jgv2h6vgjchhDiDYjAhhBBCCCGEEEKcYgi4hqBrj2sGdBCTSeSX5dst0cT7E1OlSd1wjcT1t2cwLCJA12blo3hcYauIFnLqzDlZsuOgv1eHEBLgUAwmhBBCCCGEEEKIUwxrB2di8IguzSUqtLr8vmqXnD5zTpIzT0rmqTPSqn5koe+m5fhZDG5aviKDwchuLfV55kZaRRBCnEMxmBBCCCGEEEIIIU5Jt0QGV3X4GUT/XtGvvWSczJWZG/dZ/IJb1Df7CVs8g3NyS31vI0p504FEaVIn3KHvcVkG+xh2EUt2HNJicoQQ4giKwYQQQgghhBBCCHEpMjiipnMh9crz2kmlihXk52XbZW9CQfG4mEiLWFyzamVJ90NkcHxShorUXcphVLDBqO4t5cy5PFmwNd7fq0IICWAoBhNCCCGEEEIIIcRFz2DHkcGgXu1aMqxTM9l+OFn+XLNHX2tZEBls/n41Scs+Vep7e1N8oj53a1q+/IKtubBrc31evJ2+wYQQx1AMJoQQQgghhBBCSIk9g60LyYHdx05I5UoVpXF0uOU9fD/9ZOnbRGw8YBaDuzYrv5HBsVFhWqRv19EUf68KISSAoRhMCCGEEEIIIYQQFz2DixeDe7VsYIkGbla3toRU+ld6wPdP5p5VO4PSLh5Xq1oVi39xeaRChQrStmGUHEzJkJzcs/5eHUJIgEIxmBBCCCGEEEIIIS56BldzSZS8ZkBH/X+L+ma/YAPj+6XpGwyv4P2JadK5SV2pVLF8yyBtG0WLySSy59gJf68KISRAKd+9ICGEEEIIIYQQQkpMWs5pqVBBJKyGc89gg0t6tZaxvVrLVf3bF3rdiCzG75UWmw2/4Gbl1y/YoG3DaH2mVQQhxBEhDt8hhBBCCCGEEEII0UjeXAmrXtXlyNoaVSvLq9cNLfJ6bT9EBi/cZi6o1qdVQynvwCYC7DrKyGBCiH0YGUwIIYQQQgghhBCnpOeccskvuDhK2yYiLz9f5m2JkzphNYIiMhgezdUqh8jOIywiRwixD8VgQgghhBBCCCGEOMRkMmlksCt+wcVRu6bZZiKtoCCdr1m3P0FSs0/L8M7NpGLFClLeQeR2q5hI2ZuQKufy8v29OoSQAIRiMCGEEEIIIYQQQhxy8sw5OXMuT8K9IgZXL1SQztfM2Rynzxd2bSHBAqwicLzik9L9vSqEkACEYjAhhBBCCCGEEEIcklFg6VDWIoPNFhEHJCq0unRvXv4tIgzaNTIXkdt5lFYRhJCiUAwmhBBCCCGEEEKIQ9IKxGBvegZnlEJk8MYDiZKSeVItIlwtfFceaNvQLAaziBwhxB4+7Q0nTpwovXr1ktDQUKlbt66MGzdOdu/e7ctFEkIIIYQQQgghxIuke1EMNn6jNCKD52wyW0Rc0CV4LCIAPIMrVIAYzMhgQkgpi8GLFy+We++9V1atWiVz586Vs2fPygUXXCA5OTm+XCwhhBBCCCGEEEK8HhlstngoCVUrh0j1KiE+9wzOzzfJ3C1xGoncs0WMBBM1qlaWZnVqy84jKVr8jxBCrAkRHzJr1qxCf3/77bcaIbx+/XoZNGhQkc/n5ubqwyAzM1Of8/Pz9REsYFvRYQfTNpPgg+2cBAts6yQYYDsnwQDbOQnmdp6WfUqfa1ev6pVxKgRaRAb7cswLi4ikjJNyed+2UrGCeduCiTYNo2Tmxv2SkJol9SNq+Xt1Ag726aQ84mo/51Mx2JaMjAx9joyMdGgr8cILLxR5PS0tTc6dOyfBdPCysrL0IlwxiHyNSHDBdk6CBbZ1EgywnZNggO2cBHM7T0hJ0+eKeWckNTW1xMupVTVEjqRme+W3HPHX6h363Ld5HZ8uJ1BpHFFdn9fujpf+rRv4e3UCDvbppDyC/jugxGCcaA899JD0799fOnbsaPczEyZMkIcffrhQZHBsbKxERERIWFiYBAvYVxUqVNDtphhMyits5yRYYFsnwQDbOQkG2M5JMLfz3PwK+tw4pq5ERkaUeDnR4bVkT2K6hIaFS+WQSuILVuxNlPAaVWVotzZSuVLwBVl1b9VYZME2OZKR6zAgL5hhn07KIyEhIYElBsM7eNu2bbJs2TKHn6latao+bMFFKNhEUVyAg3G7SXDBdk6CBbZ1EgywnZNggO2cBGs7zzhptnOMCK3ulTGqUUQu89QZqRNeU7xNavYpOZaWLcM6NVWP4mCkbaNofd6TkEpdwQHs00l5w9X+uVSUxvvuu0+mT58uCxculEaNGpXGIgkhhBBCCCGEEOIF0gsKyCHS1hvAM1h/t0Bk9jZ7jpltIVo3iJJgJTq0htQJqyG7jqb4e1UIIQGGT8Vg+AxBCP7jjz9kwYIF0qxZM18ujhBCCCGEEEIIIV4mLee0hFarIpUrecfSwYgMNgrTeZs9x05IsIvBoF2jaDlyIksyfSS6E+IrTp85J7uPnZCTuWe5k31AiK+tIX766Sf5888/JTQ0VBITE/X18PBwqV7dbGZOCCGEEEIIIYSQwCU9J1dq1zILuN4Ug30XGWwWg9s0CG6v3LYNo2TJjkMqqvVqySJypOzwzvRV8sOSbVKhgkiT6HCd2BnZrYVc2LWFv1etXODTyOBPPvlEMjIyZMiQIRITE2N5TJ482ZeLJYQQQgghhBBCiBdtImrX8J4YbLGJyDbbT3gb+ORWrxIijaKCpxC9Pdo2NPsGb44/7u9VIcQt9iem6XOfVg3Vs3zO5jh56qeFkpefzz0Z6JHBsIkghBBCCCGEEEJI2eTUmbNy+uw5ifBBZHBajvdtIs7l5cu+xFRpFRMplYK8IDuEtJBKFWX2pv1y+/Bu/l4dQlwmJeuU9hNf3TNGtcUnf1wof63bI4dTMqVp3drckyUkuHtGQgghhBBCCCGEOLWIAN6MDLbYRBT8tjc5lJIhuWfzpE2Q+wUb+3lQu8ay40iKxB03R1oSUhZIzjypBRBBhQoVpG0j8/kMyxNScigGE0IIIYQQQgghxKFFhLWA6w2MKGPjt31SPC6GYjAY3aOlPs9Yv8/r+5oQX3DmXJ72DYYYDIzJnT3HUrnTvQDFYEIIIYQQQgghhBQjBlf12h4yoozTT3ouBsNDdNqa3UVe310gFrUO8uJxBkM6NJUaVSvLjA17aeVJygQpWSf1OdpKDEYBOevJHlIyKAYTQgghhBBCCCHEqRgcUbO61/ZQtSohWuAtrQQF5N6YtkKem7xYsk7l2o8Mpk2EZV8P79xMvVa3Hkwq2YEjpBRIySgQg0P/FYMja1VXcXhPAsVgb0AxmBBCCCGEEEIIIXZJKxCDw70YGay/V6Oax5HBKBKXlJGjz0t2HCoiBtcLr+lVW4uyzsU9WukzooMJKSuRwdY2EaB1TKQcOZEl2afP+GnNyg8UgwkhhBBCCCGEEFJMZLB3xVX4Bqd7GBmclJkjefkm/f+CrfGW1xElfCwtmxYRNvRp1VCialWXmRv2q4BOSKAXj7MnBhu+wXsT6BtcUigGE0IIIYSUMfLy8+WpnxbKvC0H/L0qhBBCyjm+KCCnv1ejqmSdPiNn8/Lc/m5CWrbl/0t2HpLcs+cKiUS0iChMSKWKMqp7CzmRfUpW7z1awiNHSCmJweE2kcH0DfYaFIMJIYQQQsoYh5IztWjOz8u2+XtVCCGElHMMKwdvRwbXLvAgzjhZ2PPXHTE4KrS6nMw9axE491iKx5kjCMm/jC6wipi+nlYRpGyIwdaewdZFIfcwMrjEUAwmhBBCCCljHEnN1Oe442n+XhVCCCHlHKPIW7gPbCKAJ1YRhhh81Xnt9Xl+gVWEUTzOSCcn/9KpcV2JjQ7TrKLTZ8yR1IQEIikObCKa14uQkIoVLec58RyKwYQQQgghZYyjJ7L0OSnjZJEq6oQQQog3QeRuzaqVpUpIJa/bRABPisglpJmvgxd2baGC0cKt8WqhtDvhhFoiNK0b7tV1LQ9UqFBBC8khknrxjoP+Xh1CnEYGV68SIjWrVSn0Ovqg5vVqawaAyWT2DCeeQTGYEEIIIaSMRgaDuOPpfl0XQggh5Zu07FNe9wsGtWuZbSJSPYgMTiyIDI6JDJWhHZuqF+6mA8dl77FUaVEvQipX8q5wXV4Y3KGJPm+IS/D3qhDiNDLYNirYABYw2afPFPINJ+5DMZgQQgghpIxGBgNaRRBCCPEl6SdzfSIG1wuvWUjYdQcIQeE1qmrE8tBOzfS17xZvkZzcs7SIcALsMxBduTk+ydPDRohPyc83yYmsU1InzNw/2GL4Bu+mVUSJoBhMCCGEEFLGOJr6rxi8P5G+wYQQQnzDmXN5aivgCzEY/rXgyIl/s11c5VhatsRE1NL/927VQGpVq6JeuNZiESkKhOD2jaJl59EUyT1L32ASeKTlnJZz+fkSHWbOHLDFKA5J3+CSQTGYEEIIIaSMgYFz/drmiAlGBhNCCPEV6TlmC4cIH4jBjSJD9fmwm2IwvPKRJh4TEWoROAe1b1xELCL26dyknpzLy5edR1K4i0jAkZJlv3icgVEcEr7BxHMoBhNCCCGElCEwAEYxnzYNo/VGmZ7BhBBCfC0G165pLvbmTVAcKrJWNTmS4p4YbHiFGpHBYHhns1UEaB3DyGBndG5SV5+3HHTfKgKp+Vvij7v9PUJcJTnDLAZHO7CJwL0vMhUYGVwyKAYTQgghhJQhjHRaRFQ1rxehxeROn2GqJyGEEN+kbIPaNe2nbJeU2KhwOZKapT6hJRGDB7SN1QhhRDBHO4goJGa6NK2nz1sOui7q5uXny6dz1ssVb06RGz/4Uz1dCfEFyZk5TiODK1SooFYw8ckZvP8tARSDCSGEEELKYPG4hlGh0qJ+hJhMIgeS0v29WoQQQsohvowMBo2iQ9WXOKlAAHJLDK5dq1CU8YtXD5ZnrhioYhFxDER0COabXYzwPZ6eLbd/Ml0++GetVK5USc7m5cvUVTu5i4lPSMksiAwOdTyp0zomSvJNJtl/nHUzPIViMCGEEEJIGQKRwKBRZJg0r1db/0/fYEIIIWXNM9iIDAaH3bCKMMTgBgWewwZjeraWC7u28PIalj8glnduXFeL8CUXCG+OWL8/QS773xRZs/eYXNClucx46mot1jd5+Xb1HSbEZ57B4Y7F4H99g0/wAHgIxWBCCCGEkDIaGQybCEDfYEIIIb6NDPaNGNwoKrSQBZIrJKRnFbGJIO7RucAqYqsTqwjUJ3hk0lzJOX1GnrtykLx98wipX7uWjO3VWhLTc2TR9oPc7cTrGBMUjmwiAGwiDA9r4hkUgwkhhBBCyhBHU82D4EZRYWoTAZgmRwghxLeewb4RgxtHmyODD7kZGRxSqaLTNHLinC4FReQ2xzsuIvf6H8tVmLv/ol5y5XntLfYbVw/ooM8/L9vG3Uy8DtpcSMWKUruG4z4H978VK1SQPcdSeQQ8hGIwIYQQQkgZE4PDa1TVNM2oWtUlrEZV2kQQQgjxCUahsMha1QMmMvhYarbUr11TKlakN7CndGhcV8W0LYfsRwYv3n5Q/ly7Rzo3qSs3n9+l0HvISurXuqGs2nNU9ifSs5V4XwyODqvu9PyuXqWyCsLwvUYEO3EfisGEEEIIIWUEk8mkA+aGBT6JiNKBb/DB5Aw5m5fn79UjhBBSDoUZiIa+EoPrhNWUqpUruewZDJ/apIycQsXjiPvUrFpZWsZEytaDSZKXX9j7F+La878uliohleTla86XShWLykbXDOioz78s387dT7x6n4sCclEuRP1f2qeNnD57Tqat2cUj4AEUgwkhhBBCylC67qkz56RhVJjltRb1InRw7E7xHUIIIcQVIMxEhlZXWwZfgOi/hpFhLkcGJ2XmSL7JJDERhYvHEc+sInBPsc8muveNaSskKeOk3Deqp8WOypbBHZqof/Cfa3arpzAh3uBk7lltk878gg3G9W4r1auEyM/Ltkt+vokHIJDE4CVLlsiYMWOkQYMGGrkybdo0Xy6OEEIIIaRcYwyWG1lVUGcROUIIIb4CUbiuCDMlITY6TCc7s10QFeEXDOqzeFyJ6dzEXEQOqfYG87cckGlrdkunxnXlpiGF7SGsweTAVf3bS07uWflr3Z6SrwwhLhaPM4Bl2sU9WmkwxPJdh+1GGRM/icE5OTnSpUsX+eijj3y5GEIIIYSQoODoCXPxuIYFHotGZDCIO07fPkIIId6N0oPY53MxuOCa5kqGiyEGx1AMLjGdm5qLyMEqAizbeVge/W6eRlu+fO2QYqPBx/dtJ5UrVZQpK3eWfGUIcVMMBtcMNNuV/GRTzPDMuTy5/6vZMndzHPerA0LEh4waNUofhBBCCCGk5BxJNYvBjSL/tYmAZzBgERdCCCH+FGY8JTYqXJ8Pn8iUdo2iXRKDG9AmosQ0rxuhxWg3HzyuQvD9X81Sf+iP7xglLetHFvv9qNDqerx2HzuhUZjIBifEG31OtIt9TpsGUdKjeYws3XlIDqVkSOPocLVO++/382Xhtnid0BjeuRnbZmmLwe6Sm5urD4PMTPPMYH5+vj6CBWwrOtNg2mYSfLCdk2DBV20dNzoPfjNHhnVsKpf1bevV3yaBy5GUDEtElNGm6oXX1Cie/cfT/HbvwD6dBANs5yQYsG7nSRlm4TU6tIZPry8NI83F4A4nZxS7nISCSdH6tX27TsFCx8Z1ZNWeowVCsMhHt4+Uni1iXN63kbWqSe7ZPMk+lSs1q1WRsgT79MAjOSPHMtHgahu8ekB7WR+XIL8s2y4PX9xHnpm8WCOC+7ZuKBOvHaL9WTBZRuS7uN8CSgyeOHGivPDCC0VeT0tLk3PnzkkwHbysrCxtsBXtVO4kpDzAdk6CBV+19fjkTFmy45BkZJ+UIa3NaX6k/BN/PFWfq1c4K6mp5v+D2MhacuB4mqScOKFRPaUN+3QSDLCdk2Br5weOme0DaoSYCl1zvE1oiFmo2XcsudjlHEwyv1/FVPg6SDyjdd1QWaWWvyaZeOUAaRlVza39WrOy+d427kiiRdQvK7BPDzwOJ53Q56qmcy63w+4NwyWqVjWZumqnpGVmy18b4qRDo0h5flwvycnKFLO8HDxkZZknzMqUGDxhwgR5+OGHC0UGx8bGSkREhISF/ZsOWd5Bp4QUC2w3xWBSXmE7J8GCr9r6xqPm7JkjaTkSGVl8Kh8pHyRlndZ03Zi6dQq93qpBtOxJTJczFapIA6vicqUF+3QSDLCdk2Br56fyEvS1JvXr+PReo0Yt81g/OTu32OWkZOdq4ahG9TkR7g3G9u0oGw+dkAcu6q2RlO4SE2W2qsoPqVrm7kfZpwce2WfNE0MtGtWXSDd8wa/q30E+nr1ehWBYR3x+98USVr2qBCMhISFlTwyuWrWqPmzB4DnYRFFcgINxu0lwwXZOggVftHX4YoETWack+/RZCasRnDc8wURefr4cS8vWlE7bttQyxjwAO5CUIY2izd6LpQ37dBIMsJ2TYGrnKVmnLHZEvhyX1qhWReqG15AjJ7KKXU5ieo40igrlONlLtG0ULb88PN7j70eFmr1d007mlsljwj49sEgxPIPd7HOuPK+9fLNws/ZVX9x9sdSuWV2ClYou7reyd7YSQgghRA4mm8VgEJ+Uzj0SBCRlnFSvaOvicQbN60XoM3yDCSGEEO8KM74tIGcUkTuWlqXXOUdkncqV7NNnJIbF4wKGyFCz6JZaMHFASEkLyNWuWU2qhFRy63t1wmvK9CevlqmPXaF+w8TPYnB2drZs2rRJH+DAgQP6/0OHDvlysYQQQkhQicEHKAYHBUdTzdYg9mwgmtczp2nGUQwmhBDiJZIzcywF5HwNon3z8k2SkGYuWmcP4z0UUSWBAQrIgdRsisGk5CAbAXZonlC/di2pViWgzA+CVwxet26ddOvWTR8AfsD4/7PPPuvLxRJCCCHlnnjryOBk/0cGw7bi9JngKfbqD46eyLIMmG2JjQ6TkEoVGRlMCCHEq1F6ER5E6XlCbIHF0ZET5olPZ2JwA4rBAUNkreoW2zJCSsKZc3mSnmOujUHKuBg8ZMgQrUJq+/j22299uVhCCCGkXIMUSaRuwjvW8In1J4np2TJm4mT5cOZav65HeedoqlkMbmjHJqJypUrSpE64xB1P13stQggpCxkur0xdJqNf+VnW7T/m79UhdkjKPFlqwkxswUTn4ZTixWBGBgcOUQVicFoOxWBSMlKyCmxpKAaXCoyhJoQQQsqoRUS3ZvXlUHKG3z2DdxxJUY+/lXuO+HU9yjtGtJS9yGDDN3h/YpqcyD5VKim9hBDiCev3J8i3CzfLwu3xYsxdPfbdPPn9sSskokBYIv4n9+w5yTyZKx1jzRPPpRUZfNiFyGB6BgcOtQ2bCEYGkxKSklEgBvMetlRgATlCCCGkjGGIv83q1pamdWurOJyX77jgiq+JSzQXLdubkCqnzpz123qUdRDRi8G3s8jgShUrqCeaPVoYReQKjgchhAQaC7YekBs/+FMWbIuXLk3qyds3j5CHx/TRAplP/byImQ0B5t0JSisyuFFUWLFisJEhw8jgwAGZSWE1qsqJ7NP+XhVSTiKDaRNROlAMJoQQQspoZHCTOmYxGB5bx1IdF1zxNUbRMhR+2XE4xW/rUZbZdTRFrnt3mgx4epJWU7fHkRNZKgTDG9geLCJHCAn0Ca+PZq2TihUqyLf3XSI/PnSpXNi1hdxyflcZ0DZWFm8/KD8s2erv1SRWfsGgTljNUitEVqNqZaeewfuPp0nNqpUpFgWgVQQLyBHv9TnMbisNKAYTQgghZQyjYFzTuuEaHayv+dEqAj61BlsOHvfbegSi8LF05yEV6x2Rk3tW/vfnSrnyramy+eBxOZl7VhZtO1jkcyjOl5SRIw0dWEQYNhG2x4MQQgKFZTsPy66jJ+Si7i2lV8sGltcrVqwgr143VH0i3/prlew8wknFQAC1CUDd8NIRZipUqKDRwfAMtud9fzYvTyefW8VE6mdJYBWRS8s+Jfn5rFlAvCAGl1KfE+xQDCaEEELKGPFJGVK9SojUDaupkcHggJ/EYAzYMDgzCohsOZjkl/UIROZsjpO7PvtHPp61zu77EDwumThZvTMxAJ543VB9ffmuw0U+u/FAouSbTNIxtq7D5WFiAONjI1KbEEICic/nbdDn24d3K/JeVGh1ee26oXIuP18emTTXIgoQ/4EJyNIu5tQ4OkyL5GaczLWbFYX6BBCDSWARGVpds8MyTxU9boS4wp5jJ+T3Vbv0/w0iHQc+EO9BMZgQQggpQ0B8RdG4xtHhGk3V3M+RwcczcjS6tXerBprWRTH4X2as36vPv63cadcL+K2/VkpierbcO7Kn/PHfK+SSXq2lRf0IWbPvWJFo4hW7zcX5zmvTyOGxqFYlRBpFhtEzmBASkEXjNsQlytCOTR2Kef3aNJI7hndX0e/Kt6bI5nhmmgSbf6fhG3woxWyHZc3eY6n6TDE48IDFB6BVBPGEVXuOyg3v/6ljiv+O6ycNIigGlwYUgwkhhJAyRGr2ack6fUaa1DFX3W5cJ1z9Fw8UWEeUNkYUKiwKOjepq+JmckE0UTCTdSpXluw4pP9PzzktszbuL/Q+iu2t3HNUxd17RvaUqpVD9PX+bWLVKsJWBFm154hUCakk3ZrVd7pc+AYjog7LJ4SQQOHzueao4DvsRAVb88BFvVQMOJF1Sm764E/5beWOUlpD4m/PYGDc29grhLongWJwINtEAIrBxF2mr9sj//lshgZBoKDoTUO6cCeWEhSDCSGEkDLqFwwgEDaMDPVbZHBconm5iGjt1KSe/p/RwSLzt8bL2bx8uWZABxXrf162vdB++37xFn2+cXDnQq+f17ZREasI+PDtPJoiPZrX1+hfZ9A3mBASaOw4nCzLdh2Wvq0bSuem5uuEI+AFCzHgi7svllrVqsjzk5fIi78tseshS3xLcuapUo8M7tTYbIW09VBRy6l9FIMDXgzGJA4hBrB1ueOT6fL236skLz+/yI75aek2efyHBVo48su7L9aCoqT0oBhMCCGElCEOJplTJ5vUMdtDGMJwUsZJyTl9xu+RwYBisMisjft0X9x8fhcZ2qmpDmyNwS0iZ/5et1c9fvu3jS20P3u2iFGB37CFMNLnoIMghbo4IMo7iqoihBB/8MW8jfoMCwhX6dOqofz66Hhp0yBKJi/foYXnSOkXkAurXqXYSUhv0jImQqpWriTbDiUXeQ8ZNfAvjigQHkngAM9vwMhgYg3sXnA/+9X8TfLwt3O1GLLBpEWb5ZWpy3Sy6fsHxkqPFjHceaUMxWBCCCGkLEYGF6RS6v/9WEQOYjAiX5Ha2TG2jhYwsxfRE0wgknfl7qMqjsP/ENHB4Oel2/T51xU7NB3u+kGd1PfZmupVKqsgvONIsmVQtXKP4RdcWDh2HhlMMZgQ4n9Q/HLuljjtD/u0auDWd+EbeeMQc/bEloO+9w+evn6v3PPFTIfLWrf/mCzZcdDlKGVExZXliObkzByJLkWLCFC5UiVp1yhadh89UchrH/ZJh09kSmsWjwvoyOC07NP+XhUSQBw4bh6XYFJp3pYDctsnf6t1GiYI35i2UurXrimT7r9EWtZnUUh/QDGYEEIIKUOgsI61AAwQYQr8YRURdzxdYqPDNJq1ZrUqekO37VCS3XSwYGHulgNyLj9fRnVraYlwQ6G/mRv3a3V2WEbgxhgF4+wBH2HoB4imgJCAZxRnQYRcccAzGFAMJoQEwsTYI9/O1QnDx8b2UwsIT20DXMk4QUbE49/Pl4Xb4t1ezq/Ld+h3F28/KNe++4daU2SezLVUub/rsxly0wd/yd2fz5S7PvtHDqdkOvwtRLA++8si6fX4V/LWX6ukLAIhGzUKStMiwvqY4xpqHQ2+L9HsF0zRKMBtIgomsQkBcQXjkleuPV8u6dlaNh04LmMm/iLvTl+tFneT7h9bKNORlC6ll/NBCCGEkBIDwTe8RlWpXdNcublwZHDR6tu+BLP7uPG39oDEIA4DYUQDtAzSCB4Ui4PmcWHX5vo3BJCrB3aUV6cukwe+mq2pt7cO7aoeafaAdcSbf61S32BEWyekZauwbBtFbI/Q6lWlbngN2c/IYEKIH8nPN8kTPyzQ6vD/d3Ef6d7csxRgTHaGVqviNOMEqcefzd0gXy/YpCImItB+fGictG0Y7dIyfli8VSb+sVxT3SFaI6UZ1hT4nV4tGsiczXGSbzLpRF2lihVk6c7DMvb1yXLXBT3kyvPaS8bJ05KSdUr76j/X7C5k8/Pn2t3y8Ji+LvXfgURqjjnC019iMMDEcpeC+4t9CeZsl9YNgvO+ItDBhDVIpWcwseJAwb1o6wZRcn7HplI/oqZ8PnejBpF8fe8Yzf4g/oNiMCGEEFJKwNP3o1nr5Jbzu0id8JoeDa4PpWRKO5sBrr8ig43o0xYF1gSgU5O68vvqXRrFFYxicHJGjqzZd1R6NI+RerVrWV4f26u1RkJA0ICYcM1As3WEPVrFROoAHIJCx4JBMUQIV4FVxOq9R1UgKU2vR0IIMfhq/kYtGjeofWOd/PIUiKgdG9eRVXuPSvbpM1pUzpqVu4/IC78uUQsBRJqN691Gr7MPfT1HJj8yXidPnQEBGdG79cJrqjiBydWR3VrI94u3ysez1smsTfs1K+PRsX3VqgfZGhCHX/tjubw3Y40+bEGhPBQHnb/1gExdtUtFzeIK5wUaJ7L9KAYX1B+wngDYk2COEg7G+4qyQHiNapoBQM9gYg3s6+ABHhNRSwMjHhzdR0VhRAMX1zcT38MRAiGEEFJKIMpo0qItcjYvX54aP8Dt7yemZ6vXbJO6//oFG4O1mlUrl7pnsBF9algTgH+LyB2Xy/q2lWADIgEsHiAmWAMBA7YQvyzbLiM6N3caDYEbZoi/f67dIz8t2aqv9W3T0C0xGEXn0B7gvUgIIaUJvHXf/2et1K9dSyZeN7TEUbEQB1fuOapFxSC0GiRnnpS7P/9H+9w7hneT/1zQXX3XEcX7yez1MuHHBfLhbSN1+Rknc1Wghjhr7UV76sw5FSogBDeODrf41kLARkbG3oQTMqBtY8s2oH9GxXtkcHwxd6PaF6CoGaKKo0JrqOe7YemD9cDyFm4/WObEYFhEgDrhpS8Gx0aFqVC01coaZF9CqmbctCwokkoCC5wfEbWqWdoNIZg4Q5YghN9KFf91p+3cpGz1heUZisGEkDIPxLGfl22TQe2aSDMrUYqQQANRveCfDfs0FRU+uyUtHmcMThHNBL9ERA+XVjpqXKJ5fZpbDc7g51e9SohL/o6BBvbdS1OWStapXHnzphEe/cY/G/dpdMwFXQqLweC2YV0lKT1H7h3Vs9jfgdAAMTg+OUOPtzupdEakNiK3S0sMRtGY3LN5pbIsQkjggkJfj303T/vBt24aXsjSyFM6FYgHWw8eLyQGIyoYk6u4nt58fhfL6/dc2FOjceH/+8HMtVKjSohaP2SdPqOTp42jIwt5nWJytkFk0T4WIjEe9sAE3/+N6eN0vbGuiIrDejw4ureUJWB7AeqUcgE5454GWTGwSoKID2F4T0KqisQQ+0lgElWruiRlnvT3apAA6kPQ5/a3qnFCAguKweW0WENEgYk7IeUFpDzDBw4CiTUo7vHgN7Nlzd5jsvdYqrx87fl+W0dCiuNQSobFaxfps0M7NnVrp8UXeAI3sRGDDauI7YeTJTEju9Q8uAybCBRHMwipVFE6xNaRDXGJKgo48sX1F8dSs2TZzsMyrk+bImL8h7PWyq8rduj/IS5Y2zwU95szNuyTf9bv1QFrv9YNNUrMFhyXD24f6dJvIjIYUVCIeOvnhkWEdaR2afkG4zhf8vpk6dG0rrx726hSWSYhJDCBNUJSxkmN1O3arL5XftNRETmIwWCAzb0hJkRfu36YXPX2VPl87gZ9LaJmNXni0vPkqv4d3J6I9RQIl31bNZTFOw6pn7AjYTkQ8adNhHHMIQZvP5yk3s8nsk5J1zIWXR1sQH/APRDGa7gXJMGN4RfMQK3AhWdpOWPWxn0y4OlJ8ve6Pf5eFUK8BtL57v1iptz56Qx58OvZkpSRo6/jxvrGD/5UIRgcSc3iXicBDaqPG8XU/1rrfj99sCAy2F7lXUsRueOlZxUBMbh+7ZpS08bDESm9SI/dcSRZAo1P56yXF35bIrd8+JelLwG4bn42Z4P6+YK1+xOK/a28/Hx5+Ns5MuLFH9UPGFG8I7o0l2evHOSVQVX7RnXc9gu2jtSOK6W2sD4uQdJzcmXp7qOack0ICV6Ma9v4vu289psQJCGkbjmUpKnHAM8r9xzRgpkt7FgHICL5vVsvVL/he0f2lNnPXCs3DO5cakKwweAOTfR5yY6DUpbwuxhs+AYfTNKitKBVjNl+gwQmiLIHaQXFB0lwY1jXGXVNSOBBMbgcgcicN6at1P/DJwuDVELKAxAaIDCE1aiqnqtjJk7WSI/r3v1DbxBRpAOCFKLzCAlkUOAGfq6oqrtoW7ymP3oSGdzYbmRweKkWkcM151hatm6PoyiunUdSJNBA9DTYFH9crnhrqmyIS9DHMz8v0j4G0WRg3T7zJJMzEKU2e1OcHs+XrxkiS166Ud695QKL72RJuW5gR+nevL70ad3I7VRNpNUakdtg0faD8tRPC9UCw9vAnxicOZev4gwhJDjBBBv6A/RbqBbvTeBHn5J5UhLTzZN4+xLT1DO4b2tkUdi3RkJE6eSHx8s9I3sWmbQsbTEYfXBZwt9iMER8sPVQsvoFg1Yx9AsOZKJCzZYwLCIX+P30/30zx+fBI0ZAgr1xAgkMKAaXI+CFdTwjRweBB5MzZO7mOH+vEiFeYcUus7jwzs0jVHBB+h+qNydl5sh/x/WTxy89TxpGhsnx9BxNTSIkEMk8lav2EPC8u6RnK/U5nL1pv9uz7IiCQrE4h5HBpSQGG6KzdfE4A0MMPXLC7JEcSFkG+xLSpFuz+vLk+AGSnn1abvnwb808QLAZhNwLuzZXL8h1LkQGG5Fe/3dxb7m0T1sJre7dyshje7eR7x8YZ/d4OwPCCG6+DyZlqKc6Js/u+3KmTFuzW8Vrb7NqzxFLxPuibWVL8CCEeI8Z6/dqVsiYnq29vluNokMoTgpW7D7sUeZEaYMiem0bRqlIjknUsgIKgcHmyV8ienRoDWmAaPCDx9V6ADAyuIxEBmeb/aZJYIK6JSh2jCw5I9PCFxjjEXvWdiQwoBhcTkBE5DcLN2l05Ff3jtGiDV/O3+TTE5yQ0gKeYShI1b15jAouf0+4Sm4Y3Enev22k3DTEXDCkQWQtOZefrwIxIYFqEWEIpaN7tNJ+2h2riKMnMuVoapbDKryGAGsUqfM1RtRpi3r/FuIxaBRl9iw+fCKwovWRSYB+okNstEbdfnXvxRpBm3nqjDxzxUDp06qhVjxGVBtuYhF15owlOw5pcaBeLRtIoAGRHtt612f/6OQZBtbWHpveAoO+XUdP6L6LDq0mi3ccZGYSIUHKX+v2qg3DhV2LFtAsKUbGydZDSYX6MnjyBjqIDsbEHOpflKXiT/6KCra2ioBXMMYBlStVpKgU4EQW1ErAMSOFSck6KRe/+ou8+edKv+szKK4J1u47Jgu3xfvUMxj2PoFWO4T8C8XgcsJbf63SKt4Pj+krrWIiZVT3Fpqeu7wgopKQspzKgogAiC2GzxtEjScu7V+o+JZRBTohNdtv60pIcRYRAKmzdcNrapXxjQcSLUXlimN5wcC3f5vChXIMcLMVHVZDDrv4eyXFKE7WvH7RyGBEyMKvMdAig3cU2Fa0K/Di7dmigfz++BXy3QNj5fJ+//pb9mwRo8/r9zu2ijienq0iaO+WDQKyurnhoQnxAdvz+3+v0CirVXuPSn6+9wYihriBonnntWqg0WTweCSEBBe7jqbInmMnZEiHJjrJ5m3ax0arpzv6FwiryN7AmKdOeE0JdIa0N1tFLLayijh95pylDkCgAatB+L4ak4j+wpgAQI0QZLuwKFlgE1mzesDZRCCQIicAIvJ/X7VLgwy+WbhZ3p2+xq/rggk1ZMDhfPrfnyu1P/WVlRz9ggMbisHlgHX7j8msTfs17fWi7i31tduHddPnL+eZK+jaA4PB92aslsvfnGKZZSck0FhRjABm0CDCLAYjcpKQQI4MNnwUL+llTqOdvm6vS99HZAw4r63jlFhYUBxLzS6VyMzivMAaRobK0RNZfo+AsGZHgV9w+0bRltcw2O3R3Cz+GvRq0aDYInJLd5qPx6CCQX6ggUwKRJ9feV57+eLuizV9s1+bRmpVsvNoitf9ghEZ3L+1eT8u8GGkCSHEMzDxCN9aiBJfzN0g7/y9WnYfO+G13fl3wbXMuLZ5G0y6QfyF7/uGuEStJdHPTT91f9GxcV218UPmBK6JmAi+9I1fZfSrv1j60EAiLfu0YM6wTphZ3PMXRhE50LpB0SwkEpiRwYEiBuN+Z+zrv8oLvy7263rgnvy3FTvU8qt53dry5fyN2gf7AxybIyeyNEjg2gEdNZvw52XbvL4cWJY6spIjgQPF4DIOOpfX/lihXn0TLutvKaCAYjaD2zfWgeymA4lFvocZoP9+P08+n7tRI4hvfP9PvTkkJNAwBLD+bZ2LwRCewLE0isEk8G0iwLBOzdT+ZOqqXfLj0m3qH4zJPaSS2QIv7NV7jmqKZKMox0V5IDTDGiAxzfcR8nGJaRr9a3jEFVmXqDA5ffacppoGUmQwbB2KK2bRLjZaI62dFZFbuvOQPg9s11gCEURUrXn9VnnuykGWrArDW9OYZHMH3EsYHsnWQMhA4T14YnZrWlfb9EL6BhMSUCCycsyrk9Uf/ZlfFsm7M9aoIIFCvPbOa3fBNWr6+r0SUbOaDGjn/H6tpP0aRODvF28pE37BBqh1Mah9Y0nKOClP/LBAx10QZMDr01b4xVonOSPH4WStYZGEbCN/giweTGqClvUpBgc6xv1goNhE7DiSrP3F/C3xcurMWb/WvkGU7MU9WskX91ysY1b0wT8t9b4I66pFBCZa7rqwh97Hfzp7vQrnvrCSa1aXxeMCGYrBZRzMLEPMHdOjtXSINae9Gtw+vCA6eP6mQq+jev2dn0yXmRv3S68WMfLerRfqoBc3hy/+tsQnqQKEeAKi1+EJB7+hpnWdm8/DM9jwzya+A/0DPKbgQXrzB3/Kn2t2c3e7YROBFFejraLfhXdwYnq2vDp1mTz87Vy56YO/ZNTLPxeJqkD2RtbpM8VOikCANZblS87m5Wk0gbMZf0O0DhSrCKwzUpjbNIgqNtW0cqVKmm2DavX2CqHgPICgiggPI9I7ELG1r+jbupFOHnviG/zkTwvlvi9nWaI9jGOLtmZ4LVcNqSQD2sbqICBQ058JCUaW7jikE4Vje7WW164fKp/fNVomXjdU38N5PWXlzhL9PiaFUjJPaoYi+k9fYXjmI8IZHrI9Cix9ygLwDQYQzeGr//0DY+WyPm31uvTH6t1ev9499t08eXnK0kJ9tnE/cdvHf8uQ576XV6YusysIG2Jw3TD/WnAgkrJlgeURI4MDn6iCyGBElgcCOw6bs6AQmOBP68zJK3bo85X922tByS/vuVj9uHH++dKz1x6GjRcm1mDnc8/Inlo346NZ63xSPK4ZI4MDmpDSWMhHH30k//vf/yQxMVG6dOkiH3zwgfTu3bs0Fl3uWVaQpnpRD7M9hG2KKFJf0ckgEgCdTlRoDZm3JU4HuKO6tZRXrztfI4baNYySB76eLZOX79CT98u7L9aBHSH+BKnM8Cy7vHM7S9S7I2IKbCIcicGIWvHEaww3yZ/O2aDebv83po8EG9j+/YlpKnyt3HNEhWDMshvAz3l4l+Z6w06cA/EUExvWA+Wnxg/QlFpEUZzIOqmpr6jyO23Nbrl1aNeiFhHFREE1LhAmsay+vsnUNf9+cqYKC84ibBsWFJGDXxuEVX+zLyFNzublSzsriwhnYLIU+x2+lCO6NC/03oa4BPVDG9g+MKOCHYEIkPaN6uj6I0rGVa9jRIwYgsJHs9bKGzcM1/8b6c3WBZzgFzp3ywGNDr75fKYHEuIpsFtZuDVenr1yYIkF1uW7zdcQDPyts0sw0Y4xwnOTF2tm1X0je2kUa3EgkjXzZK7l72lrzNmFY3xkEWHPNqBrs/plqjARJnPhrd6ifqQ8OLq3rnvDqDANznn/nzUyqlsLqVmtileWtbHgXgL8sny7DO/cXO81MIE/b8sBy/Xg52XbLfci1vfZgRIZDPq0bigHUzIsXv8kcMFYAJM0gWITgYA5g7mb42R452alvg4I+IBXeOcmdaVtw2hLhuAnd16kVp2wijvfqgaOrzGsQY0gwivPaye/LN0mk5dv1z4K93De4IBhJVeX94GBjM/VvsmTJ8vDDz8szz33nGzYsEHF4AsvvFCSkuhR6w2QpqqVzAv8DW25/6JeahCOGfTfVu6UT+esVyH4lvO7yBs3DLOkjuJm5IcHx2lBozV7j+lnCCmO9XEJmmbmb4sIgLaMCQ97nsEQMHs9/pVH6Tg/LtkmH85cq+mUKI4STHy7cLMMff579dtCGuOSHYc0temGwZ3kkztHyZ0jummmwZSV5hlv24EiJqtyz/4rHAczKPCJYoiGRYR1u8Wk3QVdmss1AzrKs1cM1DT7X1fsKFTkC+cCJjN6F1M13RjkG5YUvsJI/2rhRAz+N0o5K6AGBbZZNI7o2dJ8XYV1hy04FwLZL9gZ8NiEKL5+f1ELKUdsO2T2WgYQGBDJVkgMbv1vu4RtBtJ6SzvahZDyBPr/N/5YIb+v3qXCXklwZjOESNsfH7pU3/tszga57ZO/1VKiuHW75p0/ZMDTkywPCJooFNTRxf7VU5CNYgjAEFbLEljvL+8Zo7Z+xjbgvvWO4d10QviLeRu9tiwUCgU3n99FOsbWVSHs/i9nqRDcvXl9LZo648mr1d4HgvDE35dbIoQx8Wdc47B+/uah0X1k+oSrA2JdiHMwoYDo4BPZgWMTAesa3I9CC/FH9vPUlTsl32TS+g3W4NzDZMuWg8dLbV1wjm89lKyBI5gMAphofOpy82QQJgZhI+oNm4+4pHTVoAJhQon4UQx+++235Y477pBbbrlF2rdvL59++qnUqFFDvv76a18vutwDEQ6VzCEEV6tiP8i7V8sGsmriLbL6tVvln6eulu/uHyuTH75MHh3br8jMPyKEEC0MtlsN/Aixx6yN+9Tz7Llfl/hsB63YdVhFBaQguwKESgxirEU0Q0jDDQDScRBx6U5aJURQiHPgh8VbJVjA4BEieM7ps3JJz9aaTrrwhRvkzyeukicu7a8i2M3nd9UoAIjGtjdYH85cJ//5bIYOZIlIQnpOIYHUEaHVq2qaLcTcVXuPWKIyIcYhura4COzYArHZ1zYRhhjsLDI40GwiMCgAiIx1BYjG1SqHyNp9CXb7BgzmMagua6CIHFi1x/WUyW2HzRP4mAiCXvDBP2u1n12994imPEJIMoioVU3bKqLcve1BR0iwgEwcox83ilV6CsQGZzZDmKT86aFLNQMCASGXvfGrzCyIKrXHou3xWsQN9UkQzWp+tCwSXeoLkLXYsXGdQn1ZWefGwZ01a2jSoi2aSeMNIP6HVKyokeA//9+lMun+S7T//viOUToWxCQ0xKCv7hmjohTqFjz0zRy5+u3f5aJXftao9NBq/1o0+BOMcRsU1AUhZcM3ODUAPIOzT5/RLLn2sXU0Ihh/47wo7bEU6oKEVqsiI7u1KPQe+srOjeuql7C9WiG+AD7luC+zzrAwLMSmPna5dG1WT2as3ydjJv4if63d4/FyEBAEqzBMEPr6mkAC2CbizJkzsn79epkwYYLltYoVK8rw4cNl5cqVRT6fm5urD4PMTPMFMT8/Xx/BArYVMzfFbbNRvAb+fMV9tkaVEKkRFWYRIhx9HnYRhrn4uN6+TfUiZZfdR0/I0z8vsgi2mSdP6+yfN9t5zukz6omNCxZuSF3pA3AzvSn+uCRlZEvd8JqFikYBeCM98/MiqV65UpG0b1v2JqTKI5PmSpWQivLNvWPkiR8XyIwN++Shi3s7LJhVGjz63TwVXt+9+QKXUjk9ZffRFLWDuOq89vL05QMsr1sfBxwXzHR/s3CzTFu9Sy7v105fhwj0ZUGEC6Ixg6n/tge2/0hBxDr8ZYvbH1f0a6c3j5OXbdf0e/i7IqrgvNYNi/1u7RpVVKQ8nJLh0/0O6xDQrG64w+XUDa+hHskQgwOhDUC8QPpicyfrbE1IxQrSpWk9WbPvqPoGo/8AEGgQ8TCsU1P9TCBsmzt0bVpXqlWupNYvrq771oLIlTuGdVMPPggFv6/aKanZp9WD1OjLjefzOzbRzJFF2+I1NZmQYLtHLylI2QXoQ5ftPCT/d7Hn9noWmyEn15Cw6lXkrRuHybS1sVqYGvcay3YdkheuHFzoXgPb/sXcjeo9/uaNw3Swb01p9Id3jeguXZrUlfYNo8tc/2sP3Gfi+P73+wXy9t+r5H83mm14PAWiF1LBuzSpp/e7OGbdm9XXB8DfRhQwjvsXd42W2z+ZoVHDIQWF7kZ1bS5dG4ZLZK1q5WIfk9IjolZ1HXedyj0jVSuXiiOpXXYcTrZoG7A+wFgFEfL92zYqtT4dE2fHM3LkuoEdtaaC7TI6Na6j91ObDySW2Coi81SuejVbT87bsuWgOcsEGRy26wI7h0n3XiK/rtwh705fIxN+XCAHktLkvpE93RZ0MamFjEhnYwTiW1zd7z49Q1NSUiQvL0/q1TOb/Rvg7127ikaLTZw4UV544YUir6elpcm5c+eC6uBlZWVpxwTx3BELt+zX544xoZKamuqVZUdWFalSqaJsiU/w2m+Ssgna37T1cRoVO7xjrEYYgPSTuXLfV/PVQ7dHs7qy/kCSzFq3U4a2j/VqO1++55icyzdJt9hIl9tiZHVzl7br4DEJaWSe2AA7DydJTO2a8sL4vvJ/PyyW/34/XyaeOS29W9iP6oNP8T1fL5Cc3LPy0uX9JKZmJbm0R3N5Z+ZG+W7+BrlxoFn0LG1QAAE3Mgh8/mbeOrm0Z+FZZm+yYkecPresU9Pp/h/TJVZ+XLJVvpy3QQa1qiOnz+TJ49/PE9w2VKpQQfYcSS7zfQnaQV6+SQdNnoC2HpdgTquvXbVCsfsD7a1NTIR6ru45eFQWbjFHaHWMCXNpXzaoXUMOJWfIiRMnfDYjv+dYig4yq+TnSmrqGYefqxNaXQ4lp/u9DcDfGJNYEIKzMgsX03FGhwbhsnrvUVmyZa/0b222jZi9znw8ujeO8vt2eUrn2GhZE3dc9h06poN9Z6CP3hJ/XPtQ05mTcvOANir0ItMCdGpQW/eDdZ/erZF5MDJn014Z0MI1j2ZvAS/nR35aKhd2biLjeviujyTBiav36CUhJeuULNp2UK8DETWryqp9ibI7/qjUCfNsInrJ9ngV+VpEVS+2zxrcso60vG2YvDhttUxbs0eaRtaQS3v+W5dkQ3ySbDmUJIPbNZTwkHy/9IEtIqtJi74tJT29/Fja9W4cIe0bRsqsTXFyy4DDUr+254XbcP+Me5bOjSJcPj5vXdtf1u4/Lt2b1pXaNata2jm+76t2TsontSqb28uBI4kaFOAv1u8xB83F1q4mDUNDJDq0mszbGif3DGtvGdP6uk//afEWfb6gfQO752LTCHOfvmb3IenSwPNixFj3+79bJLuOpcm3/xkhjRxE0q/dY54YbFy7msO+4YJ2MdI5Zrg8+tNS+XzuRsnMzpG7hnayjCdgIfHxvC2yPylDru/fVoZ2iNUsXmu27Ddn1NULrVpm75PLOmjTruC/6Ro7IIIY/sLWkcGxsbESEREhYWGBW63b26BTwgmH7XbUKSH8ft2BZE2L79yysVcH/G0aRsnuY6kSGhYulQs8hUlwgbSW539dIn8WpIj8smqvPHBRL62E/Ngv/0hixkn9GzOtl/1viqw5kCKXD+ji1Xa+5ajZh3ZYt9YSGRnp0m82b4DUwd2Sfa6C5TsogpGakyvDO8VIvw4t5OM7aqp9wTNTVspvj4yXpnaM7T+Yt0S38cGLesm48zrpa1cPCpUvF22XPzfEyb2j+/rl3Ngcf1yFYPD5wm0yulc7qR9RyyfL2ptk9gzs36GFREY67n+xmy/t21aLT64/nK4+c9h391zYQ2Zt2i8HT2TrMS6raULwR315yjKNNJ/2+BUe/Qba+olT5gnNdk0autSerxnYSc/BBbuPy/r4ZBXserdr7lI0eNO6EbLveIZUqFrDJ1HssAeADzAsIqKi/p10sUeTurVlzb5jUiss3OJR7w/2JaZK7rk86diknsv9CRjYsYV8vXiH7DqeJRf3iVCP7FVx5miTkT3bSqRVBkJZYlDHZioG704+KaMb2685YJCYlq196IVdzW13SGSkDFizX5YVRBsORR8dVqNQnx4dHS2NokJlY3yKhNeuXaoFaWcu2SY7jqZKxqmzcstw9yNaCCnpPXpJ+W39BskzmfQ6AN99iME7jmfJpU3d98jNyDmtAkGP5vWlUf3CqcGOwHn+6X/qaL2Azxdul9G92lvuNX77bZU+3z2yt1t9KSmeUd1byY6jqyXxZJ60b+75vt2RYA64Or9LK5ePET7VpEH9Um3npHxSP8o8GZwXUtWvfcTBNLP1Qq+2TSU6KkxGdGmh/tgH08+ojaav2zoKx62JSzRH5bexH/Xbt0YtqVBhiexNyirRvpq1cb9sPWwOOvluxV5500F2wb6kTJ0Y7N22mUOLUYB1mfTAOLnjkxnyy8o9EhJSRR65pK/Wn0Kxy+zTZ/VzL01bI7+vPyCPjOlTaJ+mnDLbkHVoEsPrhJ8ICQnxvxiMwUClSpXk+PHCxtj4u379ohF5VatW1YctODGD7UKETsnZdm85mKTpABf1aKn72Jt0aFxXzcXjklC5tXQjeoj/QYX5RybN08qnSDGDlxhS1v/v27lqAp+SeVIu7Npc7hzR3ZL2Dj87RPG6K/Y4aueY4Vy++4hGYqK4iavnPwohGv6sxnd2FxQ6ahdbR19DUagnLusvz09eIiv3HJXm9YtefLccStZU+9uHd7eIb7WqV1UbhG8WbJa5Ww7IxT1LP/UZkzRgYLtY3ecvT10mH90xyidix+aDxyWqVnVpXCe82N+/bWhXmbJyp7wydbmKZfCc+s8FPWRvYprEHY+TtJO5Eh1atgoIZJ3KVRF4+vq95r9Pn5HUnNMeb8exNLNnMPanK+35oh6t5M2/VqmHIFI+R/doKSEunl+xBSliR1OzJTrM+2JlQlqmnD6bJy3qF3/jDN/g1XuPaZpckzr+qyi862iqxQfYnfsJ2ESgX/tp2Xb5ael2jTAG6BfrR5RdD8Pz4B361yotMjSmGBuHbQU2O9Z98YOje6sYDD/JerVr2e3T+7eJlckrdsjOoyf0u6UBJsp/XGYuFIpiolh2x8bOBTAUukNx0acvH+g0vZIQV+/RS9qGp6zcpdZbuA7gnmviHyv0nmh8v8IFiBChj/ulng6KSIM1+xPUZgh+we6sb53wmlofAKnCxr0GUr9hL3Nem0bSqZTO6WCiVQPz5CqKeA/v7NzKzBmr9x3TrL7OTV2/fy7tdk7KLyggB9Jzcv3admBTB69eeKKjLcMeEGLwvK3x0qd1I5+39TmbD2iNhbG92zj83bAa1bQQM2zMRLAO7o/nMGH4zvTVeq/atE64zN4UJ7cOTSly74NAr51HTqjXew0XrB1xb/ft/ZfIbR9Plx+WbpPZm+M0wAqWaS9ePVht7FAf5u/1e+TWj6dL71YNtMYM9nN8kjkDD2Ns9h/+wdX97tMztEqVKtKjRw+ZP39+odkX/N2vXz9fLrrcY0TkwC/Y2xiVgI2CMSR4gJCHWUAIwRAcv7znYnn2ykHy14SrVIzCoAQXkZevOV8vnHgM62Q25V+775jX1gMXERTQOq9NrIRUcr2bQqQ8OFbgz2rcDID2VhMbKJxhLRRbAz9eFMdq3SCyyEX52gEdNRXmu8VbLX5r/iiAhQKQGIwt3nFII1d9UZwSRQYg6roiNEOEH92jlbYfFDh77bphetxQ9RvEFfjLlhVQbOeyN35TIbhr03oyvm9bfX3XkaLtxVWOpuVoJWyjgnhxYD+O6dlKzy0AYc1VGhdEZcA32BfEHU8vtnicbRG5wymupSv5CvONtmghEXeA3901AzpIg4ha6l+Om9yrB3SQ564YJGWZ1jGROmCDH3VxfdnWg+Z7gU5WAwvsx7duGi4vXT3E4feM4k5YRmmBaxeuHUbbxKDIWYT7RzPXyn1fzlKB64clwVMglAQuy3Ye1oiyi3u20usAJihQ+R3nEQbzBigCdNdn/+gD197i/IIdFY9zBq5B+J5xr2HUArh9eDePto0U3y+DfQmep1VDrEHNi54tYvyajUOCFyMj7UT2Kb8GNuFetW2jaMs4BmO/iJrVZP6WA0UKjfuCmRv3qR0Fitc5A5PluNePS/JsrPTdoi1ahA6FKJ8qqPHy9t+ri9zbIUMOdoPW93KuHEvUzUEgBfqWcb3byPQnr5bxfdvp2G/i9UPlt0cu1+sEio+intDApydppDL87hEwRgIbn0/XwPbhiy++kEmTJsnOnTvl7rvvlpycHLnlllt8vehyDYrHoRBO71bup4wVB054sP2QefBMyi+4UEAQ/XbhZrnrsxky7PnvtWgbBgAf3D7SIlxhMPLGDcNlzjPXyg8PjiskaA3tZE59mb/1gNfWa/GOg/qMIhbugAJyRcTgowWRwQ3/FYMxS1y1ciXZUxBpa82BpHQdcLWJKZr+jmrGuKhDWEKhutIGwjaiPVCw5fkrB+n/J/6+XItbeRNj27oWFBtxhbsu6K4z0pgtNi7+mO22Fg/LyjnxxA8LNJL13pE9ZdL9Yy1FHXYdNU8suAvaU2JGjqWAp6ugOJ8BxH9XMfY/rBx8wf7jaYWOrytiMIrI+ROcO7gpNwbb7vDfcefJzKfNfd+7t1wgz1w+UCOuyjIYHPVr3UjbeXHnJyaGMQlmmyk0sltLp/uhT6uG+j0Ird5mz7ET+rAFE3XgzZuGa2Tl7E377YrdiPy/76tZ8vHs9XpeYoCIzyIqkxB/gmh62/5/QLvGknnqjE5UGkxatFn9sVHo9deC79iiWVa7jqjNUFureyB3+gnjXgPRwXO3xEnnJnWlt1U6MPEesKNCRtyeEojB8LgHfW0iHwkp7cjgVD+KwRjfISPCOhAIQSoYs+K+BwUWfQkmpbcdSpbz2jaS2jWd12VAoIH1xLs7QKD9fN5GzeS8Y0Q3zRIZ3L6x9gO2917GNhvLcxWs//cPjJV/nrpGXrn2/CL2c7g3/Pyu0aoRPHRxHx0LIpuyTYMoTkiVAXwuBl911VXy5ptvyrPPPitdu3aVTZs2yaxZs4oUlSOug84VYlSPFjEaNeBtEFFTrXKIJZKKlF8wg4cIyP/9uVJtBzAoRkf+6rVDpbId+xHMAtq2OUROYqCBVFtvzbTCdxYTuQPdFIOrV6msF0RrMXjXkRS9MUHKo/UNQcv6kTpLajv4R5EpwzvbHtcPNnsIvzd9tV7sSwtELGOAgPWC/yaOxQMX9dZid5/MXu8bMdgNwQsWADOeukYFIgMjOs8QD8sCSIM9mJwhF3VvKfeM7KltxRhE7ypoG87A4PzF35YUEo4T0rO1mIu7M+SIwh/VrYVGo1q3X5fFYJ9FBqe5ERlsjtY/kuo/MRj90s6jKWpr4c/K1oFG39bmyWRnWR3oHzExDDsIV6PaDcJqVNUIFPQnKMLoLSBw3f35P3L1O78XWncI/vgbWS0YhAzt2FStImzvZZIycuTqt3/XKGJkV/3y8GUyslsLLYqydp+56Akh/iAhLVuW7jik1160YQO0aYD7NCMq+Icl27Q4E9KgUcQV9wi2YKIHUcaI0vck/diYBH9odB/JPJmrKc+3D+tGH24fAfG9VUyUHEzKsHs8XWH1HkMM9n6wECGuEFHL/2KwkUlpLQYD3E+DnwvspHzFrI3mrM1RVmMiR2CCzbAAdZcP/lmj4477R/fSCXCAcTzG0LCOsB6X28vychXcOxdno4Vx6R3Du8kfj18pf0+4Sj6+8yK3l0NKn1Ixcrnvvvvk4MGDkpubK6tXr5Y+ffqUxmLLLZjpwQ2ZLywigIofjaJUeIIPDSmf4EYTkVD1a9eS128YJotevFE7cHTk7gwaIEwicjIp46RXrEWQKrN+f4J0blzPo+JXMZG1NF0GggGivw6fyCwUFWyACEFE1NhGLBrWEdYDMWtQCKBf64aydn+CXPTKz/Lg17Mtvn2+ZH9imkaYtm/0b5r7dYM6qjjvTYsOsOlAovYDRpaApyCCGTckhnhYFkBaF7AWtVHVGzPjrkQGI5UWxfTgSW20CWPSwJN0qTdvGqHRqO6AcxpRsGj7vgACA9qHK9vzb2Sw/2wi/ly7W2+WOzYuWXsubxjFPtbsM4sH9kCmBIRcdyNJDCBCod9a58U+CoJZYnqO5J7Nk3u+mGmJdjFsHm4Y3FmfL+zWwq5VBCY/45Mz5JahXeTjO0fpuT2qe8tC57+/gBXAZ3PWM0I5CMH1Asce0WxXWEUFA0R7IeXfsHyAlzz6NAizV/Zvr9FhMwr87e1aRLhhM2SPawZ20PseWEcZmTLEN7SMiVBvevS9nrShlXuOaKZDazvZbYSUBgjKAalZ/hODDYvAdlZjJqMvxNjm73V77UYHo8+88KUfdVKuJPyzcb/22Ub2rDMQnITsC+vMD1fAmOT31bt0PHtZH7OdnRFIAu9e7IPXpy3X4uO4D0M9KEzqNyuw8PMlCBaBNR4JfOgIXwZZttPcQQ1s517UpLu+weg4SpKqRAKbDXGJKoYiIuriHq1K1GnDNxjM3xLvlckO3AgP6uBZ+4ZvMDyRUrNPWyI57RVCxMXSuiibgZF6bBTysBe58el/RmulVohL87YckBvf/1Mj1TA4K81ZbgjxiFpF5K23Jm7wO4ik69CoTomjKFGptlFkmEtiMDzyvpi3sZAnYmmDGXT4XCFN09qWAcccBcPik9OLPcaGmISbzEXbDxYWg920ifAUCLUNImv5JHIdg020N0QIuOLnjUEpbj79ZROBm+sXf1uqBS/uGG4uekn+FerhhYzJJEeTWUhzBB1jPRODkSIJVuw2i1LeAAMbw0YI/dV/Pp0hq/YcUTEMAxDj3MUzImXmbP7XKmJDXIJO2KA46iNj+mofCro1ra+TPnM3x3kckVcSsEyI1Ld/Ml3e/2etToiS4OLL+Zu0UjsG9iO7micyDNCHwgMW12ZcTxEJjHs2FLW9bmBH7Yth92V7HhvnnTs2Q/bAefLF3RfLjw9e6nGEMXENQ8SF76+7HErJ1MkyRAXzOBF/EVGrWgBEBptt9ZrWLRzNivPi8XHn6f/fmLaiUJ+J7KAJPyzQ4IXnJi+WnIKaHZ4E72AsiXsUI1rXGUbwDc55eB27AsYY934xU4MDYWVm3MsY3HdRL53oRgbJte/+If2f+lb2JpyQDo2ii3yWBDdsDWUMiBWYtcKgBSmvvqJDwcCPvsHll2W7zJMK3ogwx40nLroLvOAbjNRdMLh9E4++3yCioIhcWpZFQHUmBtv6TsImAhGPzixYcOFGJNnP/3eZ/PjgOB1oIX0TgrA306Ftb2zsbUu7RlFqQeDJwMHRcs7m5UsXL3mioogcosYRpe2MV39fLu9OX63R6v5i88HjOpBCGplt4RWI7rjpcrafU7JOagEFCKXwSv3gn7XaZxsRuigCVJpCH6LFXL2xdBUUBEG6sCsWEYaQjnU56gcxGIUQEbmPCYa3bx7BQhYOooMxcebIysVTjznr9Ef0pSt2O44+9uQ8BXdd0EP96zJP5aqIin7rhsGdLCnsGpXTsakO7NCv4Vx87Y8V+t4Tl51XKNUdA0RkA6AQF4Tl0gS2NNe/N03FPExEmbeRBXxLC9hKYd/7yi8a14XiJjmnrtqp1z9MZn9212idSLVlQIFVxP99M0fvMxAVjAlbVHwf3b2l7EtMsxSXBrDBgu0J7nXcsRlyhCvFZEnJaVXga7/XTk0LVy0i+tAigvgRWPZhAgv3Fv4Ak6t7rWz1bIHN5gVdmmtQ1JzN5swhiMIQgHGPC2EWvsKYmC2JRYTtpJ4zcI+FsdyOw8VnIP6xepcGIWFs9eglfS3Fem3HwjOfukbeu/VCLYKMyUPNKvdhICEpm1AMLmOgSAQ61yEdm/r0xsxIDzfENFL+WL7zsAq43Zu7XiTMERiQIFI9Lim9RJYAGKwjNQc+eIjE9NTfzhjg7TpiFnrtFU5p3SCyiBgM8Qw3Ao4sIuyBImuf3HmR+kKt25+gUWqwuvA2Ow+naNFI20kgY9vgieotiwiAdFBvYIiGzopUIYLVKHryjZ3optJi5gbHHl9GezRSz+wxZ1OcpvheN6iTXNKrtVqOoOBOaUcG67KifWPPEJdo+AW7nmYWGxWqxY+cVbz3xWDgoW/mmG+Wx/ZlMZ3irCL22rdxgMccim0iddkT4D2PQre4LsC71BtgndAXYmJsTM/WWtAPXQaiv1H81JoLupr9ARHxD7sQRFbi3ET1bluQJQNmbiy9Cakdh5Pl8jen6HqN79tWfnv08kLRz+6CKG8UgSWu9xP3fzVLo7JRq8DbHDieLsOe/0G+XrDJ4WdQ2R62Qqi98MXdo7WImD0GtDUP4iH6RhdEBRvcfH4XfYaobUyqX/vOH5oldf2gjl7eKuJLWhpicKL7YjAsIkDfViweR/wLarWkZJ70y7KRaWhrq2fLw2P66n3E23+v0gyjvzbEyeIdhzS4B8XSmtetLT8t3abXaHfA+AX3GxhfD+7gelCTcU/iyCoCk5UYo748ZanW+8Hvf3bXRXLL0K5O6zag6PnTlw+U6U9eLasm3iK3DXP8eRKcUAwuQyCy6q2/V2nY/90X9vDpspBWgVk9I0WUlB1OnzknZ/Ocp7migA4sQFAR2lsFlYYV+CI5qmrtCtuPJKsYC2HZ08kOazEYAimi0uyJcPAjxkwpKs4aGMKwO2KwESn82vVD1aMJA/E7PpnucXqRPXBTA2ERET62hf2MSGGIxd4sHtetWcknCYAhXjubJIDnFcAkAMRWQxguTXCjNXtznA7IDYHMGsN32plvMG4AERGMiAP00fDt/XDmOo38q1WtsoQXU1HYmzSODvdJETlD1G/hYmQwaBhpCNOlFx38ytRl2pZxTt5Y4CFLioJrALDnOw6hDP0O2r69gqKuYkStwA/XGk8sYbBOiPJFv2dE71/Vv4N8cuconZRDRJI1/dvGapom7FvenbFGB1D/d3Efh/ZYuFZAnCutegmG9ysinF+8eohG0aPo4ub4RLcnxbDO8FB+4KtZ9Bx2kU/nrLfYSdm2T28wb+sBbeeOhGZc7x79bp5GAsN+CoVYHYEJONi6gNuGdi0UPYx7A7T1VXuOagHTe7+cqVFmyIgY3/df0ZgEPpjUqhdeU/baZK25cu+/Zt8x7T88qU9AiDdBG3YlK8KXmZS2xeOswTmCguAImEDG0Edzt0jtmlXl1WvP13Hxs1cO0uCO539d4tb1FPdMCIwa0qGJW0V37RWRQ6bkFW9OkUFPT5Kuj3whQ579Tn5etl2DU359ZLyc56YXfGj1qszwIEWgGFxGMNIXMGiYcFl/iQ71rSk30irQiSLNDDcYpGwAf6bzn/9eej/+tVz19lR54dclmq5iO6i0FBXxYrrI8M7NtWAYivis8VDMW1JgEYGLqKcgzRKg+AYESETOOvJOwwAKKfyGcFtc8bjiBOGXrx2iUWe4mHszuiw+KV0jfOzd2EBsxey2NyKD0U4gZmPA6Sg6ydPIYHho2QM3itPW7NIZ7LdvNhdK+3qBObqpNEFUN6IYLujawq4XbtO6tVV8MoQDWxD1iEKCEJIxyQBR57K+bbUNIgW/YcEgvrQwJkDgIehNDFHfVZsI0Ci6dMVgCPZTVu7UDJfnrhzEm99iqj+jz7TnG4z9iPOzoweVp63pXyAGL991xGLhc/+Xs6THf7+0W8DFGVgnCMLGwMlgUPsmdq1tcM6e37GJ2r/g/L5zRHeHfRsmIEd1b6Ep+LD98TWwzkHmAM6lsb1aW17HdiADzN0CkBACcY+I73pSldzfWFc9Lw22xB+XL+Zu1L4SEwa+EIONIkSI/LbnRf3X2j36OgSI4gq2on2iOGKvFjFypU2BOXBLQXQwCpjWC68lPzw4Ti50I02ZBJZVBAohu5Nl9tofyyU957SM7Fo0s4mQ0gaFjDEhdSKr9KODjfGQPYtAa/4zorsGgExZtUtyz+XJ81cOtljq4F5+XO822nf/vHS7y8ueuWG/wwzD4vYXAmJw7cZ5P+HHBfLwt3M1EwSBJD1bxuhv3juyp/btRnFmQkoKxeAywpRVO2XlnqPqfwdvsNIA6RXoyA2BjAQ+09ftVT9PXFAQ8Yoo3UcmzdOqqdYsKxjoDigo7uMNEKXy+g3DpFKFinoR8yQlHCk6EDb7tPZ8vYzIGUTioP06uxkwrCIMH1j4BQP4THk6iXJFQerm0dQsn/sFA0TsQdTG8S6p5yFmyFFAAdYX3sKwE3AUGQyPQ6TyX9KzlUYj92nVUCcrSrvfMSwiLnJwAweBGAO0PQkn7EY6ICoYQEwy+M8F3S3Riw0jvCOuu4oRGeSuoFQcOI4I2sfEj8vrEhVaqmLwtDW79fmekT3tem+SwmDQk5ZzWgcd1qDyNOjU2LlIVRzw0I6JqKVevEhjv2TiZFmwLV7PI1yz3GFLvFnktGfz4AhDEIPofdMQ51Hi8A227g98yaxN+yX3bJ6M69260IRFl6b1LWKlO8y38uxftM08sVpWQL2B3k98JRN/X1bE5zwt+5S8N2ONfLdoi9eWh2XgPsUkJo3KRoQ8orm8ZWUCcA+0KT7x34h2O+nGyIKpVtn1dOIbh3SWb+8fa7dfQ+0GjBFQB2LyI5cVK4SQwPcNRrq7K0xft0eLD2JC4d5RPX28doQUT73a5nvexPScUt9djmz17EXK3n9Rb/3/6K7NLBmuBvDjRTb2+/+scakeDO5p/tmwVycXDY93d8B9Da5B417/VScKUej2zyeulL8nXCXf3HuJvHnTcL2vtc2AIqQkUAwuAyCK5n/TVmrk3LNXDCy1KKcOBQPAbW765QQbuFkb+PS3Mm9LyYuneUMEwQVw8sPjZc3rt8rPD12qAw0UJkHEkHGxWrHniEbDGKnk3gI3oveN6qkX/5d+W2KJNMPzou0H5dWpyzRywVGxJ8zAQphwVrytOGpWq6JpdvBWAs4GREYEsGEVAZsILNuILvYEzO6C414cVDorhAeQMoTIYURDlwRj4OpNMRg3W5iccFSgaurKnfp8WUEq6y1DC3sfegvMtC/cFq9iw9jXJsuAp76VT2av1/MCtiooIoG0Nmf2GNj/EG/ik9PtisGwhRjR2exRarQFI4LLmKQoLYyoAcOv2Js2ETg/3BFZ/7WJ8K5/sT0gukBghF+dN4pjBpNVBFKMrdl2sGTF4wxwzwIfPgjO8GaFVQMEuNBqVWTxjoNu2SFsOWQWSN0pcIlBGfqV/904vFhbpNYxkRqpi+uVr4qBGkxbvVttZWBlYk3Xgm0zLHtcAROB6N/gJYv9u2h7vJQVMIH9wm9L5dSZc1r5/PL/TVHfROx/9NEXvvSTfD53g7w+bUWRgq+egnui+OQM9dpFMSFHViYlYeXuw4UmpG2PJ+6FMOmJug22BUs9Pc8+uH2kFqDzdfYgKaUici74BmOCFqns6E/fummEV9oSISUFhS2BNyfYXLVLcmSrZw/co095ZLw8clH3Iu9F1Koul/Zuo9ciV649/2zYpxH9yBD1xIKxU0EW1vH0HLnnwh7y3QNjvT5OJ8QWisEBDgZJL0xeoh3RhEv7e6UisKvAPw9sdzONM9iYXFDU76NZ6/xW+MrwnsMFcGinpjqTiYtg56b1dBCMqqiGuLbtUJIOvvq3i/XJxMKtw7pKzxYxapOAiGQs75YP/5J7v5gpPy7dpmkv9iIrFxZYRAxu77lFhK1vMHAeGWwWg3cnnFARCUIXXivJfsFgHGCfe/PYVqpYQVrH2I9YNrZxl5PiZq5gFB7q5obQ4goQVxApbWs5gwkAiEG4ATKEeQh4GAj9s36f124i4bvV/8lv5b4vZ6nYgJs18OHMtXLRKz+rXxiiuFBAypGliHUROVurCAiuSHfv16ahnnvWwDt4fJ+2MrJzydu1O8CrDG2xJJ7BKJ6BCAVM0hiCOtq1OxYRwJhcKUlkMKIDjQme4tKyITpCYLNn90GK0qtVUd9gHKvZm/dL/do1vTIYQaE3FKJD5sT0J6/R9EuItDh34pMy3IoMRlqnOxN2uBY+ekk/lwRktYro1kIn134rgf+9KwIOxEHsA9v7OmSsYBLXnSJymw4c1/sQFIvp27qRRnmXpkd3SYVZWHgg/RX3KwdTMuT696bJBS/+qH00Jp5QDd3w+HUF2BI5ypRBxDWuAy3rR8j9o3rpa5isACu8KAYj0wk8cJF5GbaF/YzJFxRYJMSuGGxV08JRhDvuqTGR8vK159MrmAQM9QuuaxA2S5O5m+P0+g0PdVdBNijGWI7es84cdQSuN5/NWa8BWZ4Wabu0T1stJAsR+N5RvXgPS0oFjpQCHMwyIY16YLvYIlWyfQ0GgEh1wIDEnyJnIAMB0UgnxayhbWRVaQLfVXBp77aFXr91aFeNzEQ1a0Sroj0BX0XNwSph4nVDNUrh2V8WyVVv/y5r9ydo+g28gJEW+eZfKwt9Bx7Db0xboRc+iNklxRAKECFh2BTYA6nuiObEscPg8Vx+vscWEQZYJqISk7x0AwQfRYjBKNjlKBrTKG62s5ibFQwcvpi3UScD7LExLlGjygyR3Ftg3dGF2EYu/7l2j0ZOje/XtpAYg2gtHIsfFm/1yvKxHPzeXRf00CrBK169WeY8e52KDxA4f1lm9gODGOwM+E/bE93hy+3IIwzi8PNXDbJYJZQmiP4/lprtUQEPRK1BpIGFyk0f/Kkia1yBjYC7YjDaLfogT8Qp3GBPWrRZRrz4o1z99tRit+WPAosIiI3ENRpEhGpbgRhs+LZO/H25RsE/NrafVyYNkfGx/o3b5fmrBmvmBjBS4zEh5KonPmxPkErpywypawd2lKha1VWI9JWg+ueaPQ7bKcRrZNlgctfI6HHVIsK4zoLFBROsgQA8mzGxhHPZ+n4SAikm1BGRfceIbiraT7pvrLZJnOsPju4ts56+Vp4aP0B9omdvirPYOjkCE3OXvDZZPp1tXzj+Zbm5v0eFdSN6y9rKxBvexfiNZTsPabYJCuLit7Gt1ttuFErtUzAZQ4gBrrHIGrCNDEZgEKLX/163RwM8Hvx6jp4PNwzupBNBhASaTYQ3A2NcAdcT3B4Yln0lxQhUKc66DtmByDa5rE9bS4aou2DsiEKy3irgTYgrUAwOYDJyTsvrf6xQceaZy0vPHsIAEXK4iUXntnKP9wtrlAcgrCIKDT6nwFvilUep0ev3aeEqI93ROkLwwdF9NHIA1dThxwrR1ZfRKIjMReEmCHAYwH13/1h5/7aR8saNw3XQ9/3ireqHZETi3P25ufL1+7deWCiq11Mw8DKiK5ylCUG4bVavttpEoDCRp8XjbMGNQKKXboAOn8jQAUBxEc7oHiAaOwOFZSDwoYK9LfAK3pOQqjch3o6otBSRs7KKwKB46qqd2r/Z+vRe1L2lDqLhgWev6I47wAICIhe8w+6/qJd0bx6jbQLnBby3Zj1zrVw3sKPeOBopWsXtZ6OtGNvxz8b9Gg3gjYkMb/sG4xz0xLIEhSDR7gyBG9Xpjag8ZxMszqwiEJHtjq819vO17/4hb0xbqf0X7Geso1dtQeXqJTsOSsfGdaRlQWQVcV2sxQQAisbCzxc2CYiW9GYBKtt7GExI4iUsyxWMomjuWER4AiZwnrisv7a5F39b6vXJcJwDf63braL4+R3t9xnYRlwTDYsgA/SHthkWWL8FW+N1ArZniwYyuL25MKyr+7U0WLH7sIpWOJcf+26+xZ4HRW7RBjBJYFyrYdvw95NXyeIXb9SCf+ir0XbuudDshepI5LUtkAtRwPb6gT4NlkAQf5HBZGtlguhqb/jVbz+SrL81sH1j/e2uTevrNdbaKmfNnqMacIH6HITYTqA2jg7TyGCj/8E5c9VbU+X2T6bLEz8sUMsdtHXcsz08pi93IAlImwhvWuYVB64xG+ISVbvwxlgSYIyI+3tnkcGY/Pts7gYNLrp9eDevLJeQ0oJicADzzow1ciL7lNw3qpdW/PYHd44wd2ofz1oftNHBB46nywNfzdLq9Lb8XSBo/ndcP/X5W7g93usena6AQR8G8vApsifkIWUakUYQYBE106N5TIl8eV1hVPeWsuSlm+Snhy7VwR3AMiEKYxAMj8CfVuyS+7+arUVcPrx9pMtFVFyNDDbS+p2BlFxEhxopnW29IAajWj1u3N2pBO2IHYfNwqOzASMGy83q1NYKus7OU8PX2p6fJKKzge1kgjcwijhYF5Fbtz9BDqVkysiuLdTn2VakH9Glue4/eEeWhK0Hk/RY9Gttf/ID3opPjh+gYkRxE25ov02iw9UmwtjPX87fpJHlEHXgjxxIINrTkyJyiBzHxBaiFF6+Zoh8fe8YPWeNcwSR3p6sCyL9XI0SQcbFVW/9LtsOJWv0JLwwDcsPR8ArGAKabXYEcU0MBjjGE6cu08EPojF9OQkNPz6InhviEhxmK1hj2CZggtHXwCoCoirElunr3StyVxyYAEXRzNE9Wjn097T4Bh84XmjAefvH09XaJsnqPMIkHs5xXD/xe7CdwIQIMpW8cQ3yBoa1TvO6tWXmxn1y3XvTtBYGBu9X9e9QROCHMGybCQNLDUzYwb7EWWGt9fsT9BniK3yUrZmzab+K/OhTbNs27DWAN4IfkEkBBrUzC/Pdmtcv5MsPiyQUrMO9Ee1siD0woYlgE4wDwXszVmt2Fe6ZXrx6sHx0xyj55eHL5Jv7xtAnmAQcyK6BOFqaBeRQNB0YtTq8Aa5FGMOgeLSjrJG5W+I0u3Rs79ZeE6EJKS0oBgcomw8ly9RVu1TMun5QJ7+tByLhkHqE9DZvWCAgEqSsgE7/xyVb5fI3p8j8rfHy8pSlhcQseIxChMU+Qvo4jhP0oR+XbvVL4ThnqdGI8oZgDbCOnlQ59YTIWtWLDLgQrYgiFxCGPluwTX2aPrnzIrf8nYrDiKI1CiM5w/DhXbQtXiOUvBFR+G8V3ZLPiO8oiPZtF+u8MnjbRtEqqCAd1x4YfBoDUQzMj6UWLua1qkAM7uuDiHEjMhiezCDrVK68NGWp/v9yB6lc3vJwNAoC9WvjnfaFcx0DNIiaM9bv1UhrFIeDoBxoILIIQHR3B3h7Z50+oxYzEGSQlv/Dg5fq78H3tbgKzfZoVGCTccTFdZmyaqfkmfLli7tHa7GxIe2bqNUEJjTsWUVAnEc/CDEME1HEMzH4o5lrNYIb3u9N67ofAe4uOK4Q8I1oTmdgYgh9dMdiIvi9Aa5bT18xUCfa4CkOiwpvFo4rzsoEfv/A2jcYA871cQna9zz23TzLeTC/YJLPOjNhSIem+r4r+7U0QNYKhIFfHhmvmRiYQEM/g2ymh0abq7kXh0YHj+yh9zCOvIOxzfBihhBhLQ4Y/LF6t7YhTJzb0rdgwtAbReRgfQKR1/hNw4cfVkxgdcH9NC0iiCOQRQcQHbxu/zH1uca1d+L1Q2V833ZqB4PJEVeKZBFS2mDciXu20ooMRtAHAp6QmTmoIDvGW2Ccj0lEe4EV0Ao+nbNBx7J3DC9ahI6QQIdicACCtLa3/tmgflGIVvN31AB8NgGqOnsKOkv4xw58apJLRYD8DYQzpGK9+vtyqVE1REWRs3n58uKvSywRgYhQw7G6pMDLeXiXZlpsB4ONnFKMxsH+hDddlyb1nHp5In30gi7NdSDk7QuluyD69Knx/SU2spZ8eudFFpsNb4FtnT7hao28Kg7DFgLHFz7ZGPyXFFgcAOvoLU/ZeSRZj1lxEcuGAG6Ix7bM23pAB9GIELcu2GcAr0REfxq+uN4EBZ/w2+rLnJcvj0yap/9HsaCuDryxerZsoH2fKwNz9C+IxEIVYVtW7jmqIkQvq5TgkmBEm/+4ZJs89dNCCateRT79z2gVNQKN2ILCX4eSXS/QhUjC7xZvkYia1QpFVyCtespjV8i0x6/0KAK6SYGwaFtEyRGIGGwUFSbnFYj4GFig/4IQb88qAu0e34FnquFJS9yz1sFEHfpBCPd3ltKgZlBBNsiSgkhKZ9YKiPJvWT9SU+tLA/jWQqhE1g087b0Bfgv+vhB62jux/kF/ggwXo2YDtv/Df9ZqX4aJMmRWfDRrrX4Wv4dJkAEFUajA8A0OBKsI9M+wXoCQhewKTJy9WlDs6qWrh7jVnyD9F1HPszbtV0sTWwyfZQQx4L5i1Z6jcjDZPAl5MDlDxfR+rRvZ9XTE5DWuo/iMvWuJq8CuBhkNuOYYWS8QE2CJtLFA3Dcycbx970PKD0ZgAibBnv55kYpNmBh1lE1ASKBRP6KWZsF4w4e9OJBxgvtXBJigdo03ceYbjOwTTG6iSC6uaYSUNSgGByBfL9gsB1OytHpycR6WpQFujjGwwAAcs9PugoHM69OWa6Qzos0WFBQ6CWTe+nuVFvfAgGLa41fJI5f01bR1FEKD2AtQwAGCvSE4Ynb+6gEd9WJkROqWBtPXmYtwuVIwCYXdfnvkch1Q+xsITT/cM1K6F6RPehv4PLmS4gybCANv+AVbi8ElraKLASminHC8bK0UHBaRcyAGz918wOzNeOUgSyS0AYokwcsQPtIQ3bwNjgOEAIiSr05dptFqQzs2lf+7uI/D70A0QKo0BtWIwncGosTu+vwf+eAfszhiYNhMdG5at9j95yqIwAYoyIjbW9ieeBIpW1qDSfRR2w4X9h11xs9Lt2mEOYr42U6M4JhgwsQTzu/QREU8+FYXlyGCKEykeLey6acuKPCvtWcV8cdqcwHNcbSI8BgjGn/CZQMcFqv0NhBFIcwt2XnIqZ807Jrg91oaFhHWXF1wH/b3ur2yycWJDEcgU+Tuz/9RwR1Vw4u7PsE6ASniR1OzdPmwFUCl8XduuUAnZz6fu1GLXyLTAxOs1tZPmLTCfsUkmTs+3b4AvvcQaK1tm8b2bqOF4eCp6w6GdzAmNr+ct7HI+7AcAd1bxMiV55mzTn5bYbb4+nOt+b4M+94REIpROBG+k56yfKc5Gtt62zCxiQyLvQknNDMG95fwpjYykwixxWgb8CKF/RyCUgJhTEiIq9QLr6V1KwyrE1+CLBBMmOAa6W2Ma5c93+Av52/U+2zDVpOQsgbF4AADN+0ogFMntLrcP8pcLCMQuOvCHi4V7rAHIoqR3tSsIDLM1sMt0IB4jWhCCHrv3nKB+maCJy/rr2LG//5aKVvij+tgASmA8Ic1QBGqapVDtPhSacyEIioYXsZI3R7ZrfhCPxjgOytEFozg+BmRhIje8Qb/2kSUTAyGNQtSk1yJ5DaOK3yDbUnLPqUTOShi0z62jg4o8NsYlAJET/nKIsIAUeu4KURRH4jur90wrNjZewgc+SaTJYrKHtguI2sBN4PGNgFMYGGiBAN8b2EdOf3KNUMs6fWBCMQhFFHcdijJrrWCLRDbvl20Rc8HTEZ6dV2qVdGbdKS4z9kU5/Szhh+orWVLt6b17VpFYLJgxoZ92mf3a8NIO095aHQf+fHBcZao0tIA4t7gDo01YtYoEGePzQXe4RDUShP0URMu66//f33aCo9rJ6CfuvLNqbqNl/ZuoyJzcRi+wejHYN+B6/x/Luiu9yFv3zxC/zbsdoZ3amZ3vyKSHhOKvubUmbNy5VtT7WaQGX7B3so6wfUQ1xMUgrP1RF5fIOKiLsLQTs3ULuKPNbu16N6fa/ZokT1MRDrCnj0RrJfcqQWxdKfhF1z4POrarJ6K2DM37jdPvrZs4JPJV1I+QJQhooAxOYEJZxS8JaQsgWxZb1nmOWP74WQNHEHtDusxubcwxoaIALYG2Z+4puO60aSO7221CPEFFIMDDAw8Jt0/Vt65fpDXItm8AcSjge1iVSR1Nc0XwBPuo1nr9Kbmm3vHaIo6hCeIDoEKUglTMk9Kz5YxhSJ3cIF56OLeGjX3n89m6Gu2vnOI9BjTs5V6dC7b5TzttSRA8HpvxhoZ9fJPEp+coQXiwpga7RE4xsaFvo0LBedcnQ33hk3E4oIUX1cK66HtIRJsl53I4IXbDqooOqKLWTA4v2OTQn6SFjHYQZE1b9C8nvlGKTqshhY+caWA4XlGQR8HVhEQkB7/fn5BhH5L7VeQgWBtfeHt7UL6NqJmn7tykFzcs6jvZKABQQkTCrY3sfaYunKn7tObhnT2yfXn2oEd9VjBhsKZqLYv0ezNDiHbGggnIzqbrSKss1RembpU++Ubh3T2enpgMIFriCPbFl8yuH2TQv2dPQzvXNtCY6UBlnlR95Y66Ptnwz6XvoP2DaESFgWTFm2W2z6aLhmncuWZywfKS9cMcSnV29jWd2esUR/nawZ0tFRoh7BqiNQ4p4Z0LHqNuLAgkv7jWetKXAAY33/m50VaTNfeRPfUlbt0QP7riu1FlrWrYILSWxPRuGbDngsimfXEEpa7YX+CerjD9gT7GFHA6NNembpMBQkcR2dR78hUgsgO6y1kG9z84V8y/IUfZNzrv2oGTXHAOgzXVRTMbFq3cBZFt4Jz66v55ohmWkQQZyCaHJlrOL9pD0HKIkZgTEmzJP1ROM7WQgj3/rY2EcYYCvZFhJRVOGoKQFDFO7ag2E4g4a53MHzXJv6+TDvQL+++WCtcI+LorItFTSDkGBWZS5O1BSIDfGdtueq8DurNm3nqjPq/DbOJxtHP9DdH/MxY79qg0R1gxP/53A1y4Us/6TMi+FBV+OnLB3p9WcEEZnUhTnorBbmu5QbI89lwDGzh94hjjDbnChhsI/LSttgRCg8BCGkAEVNgwbZ4XQ4ibzGDj9RjX4HINUR0fXzHKB2ou0KHxnXUk3eFneruhjiB6Ov7L+qlIguO4feLt1hsCDB5hdc6eTm1/LGx/Xx20+ltDEHJlejArYfMkZmX9CrecsYT4AGMIleI4LAujOUoMthWDLYWuGZt3G+xjEBfC6HlhsH+K7ZKPAeiGDJqkDUEexDD1gCTO3+u2S23ffy3/L56l0Z1GpNKpQ0sbSASvv33ao0ydYRarHz4l/T871fS54mv5aJXfpE3pq2U2rWq6YQ4IoJdsS8CmKTEMjE5DcuW24d1LfT+5X3baWrqHSO66WDV3n5FFCwm+2AzURIQhYtjgGK6sMiyFUC/WbhJ/w9/SGMyx8CwLvKWDRMY3bOV2h79ZbUumMhHOnIPK394+Efic1h3UJydVtXKIRpVvCchVX1aMemE6+rps+eK9Y3GBOt/v5+v94eoIWF7nI3rOKKCQe9WgZtVQgKDV645X76652LaQ5AyiTF56csicpicnLs5TicBvZkFaAuuX7Bsss4+NLSM0irKTogvoBhMXAYRQ/1aN9TODxEgzsDg4PnJS6SCVJD3b7tQRQCAFA5XrCIg5vzfN3Pkvq9mSdzxwgMLRwXf7vpshqYPl5R1+8yec73siMGITHv+qkE6QINZvL1iY/AWahwdpttYkiIk1umXqJB6+8d/y7AXftCIYEOQmvHkNVpV2N9FBss6tw/rJotevFGiQ71TBAwCJIQLCLOegmJYSE/FjLOrx7ddQWSztW8wblyQ8oqo/AaR5kmmlvUjtEgUJluQwosBNIQDV0UKT2gYFSaf3HmRpYCdKyDKEz7GSNG1jcr6edl2FbMh5MNLD0WIcC5AHJ69cb/efKJIHWwcgrnathHp6Ux8NTiWmqVtzfC89gWI3gWIDnbE3sRULZRlb3ICoi8mGNHX4xi/+NtSnZh79brzGRVcRkGkZp/WDVVEHPTMJOn6yBf6PPiZSfLkTwtVzIRA9+6tF/jtGKPvRMQ8oksR6evMvxy2DsiGwuQ3rFHuHdlTpjwyXro3d6+IJSJbjf4Sy46wEXzRXz84uo88cFFvh7+BYm24T4GQCbug4qKr4GmM+ylbgRvFdCHY44F7EHgAG0xft1f7XeN8tZ3sxzUGxfC8mb2E4n7o27Gv0W8ZAQige7N/9zOOg1GEEtYSrkwMYlIJ7e2hi/vI3Gevl98eGa/XRwjhhgWELZjAQEFRiBLIRLl/VK8in8H249oL0IcZ1mmEOAJWSbgHIqQsUj/cO5Z5zkC0LqzCzmsb61PbHSNzFBOFRp+PsRXGUr4MpCHE11BBIm5xxwhzhXF7hTus+Wr+JhVxrx3YoZDHH4RSRAWicrgzD0vc4CO6Ap9Bep+zFEekAN7+yXRZuvOwTFlpThXxFCwHkSDwCbZN8bOO1sEAwUjRtAUDNESvIappuYP0dnd4ZNI8mfDjAo1yxCDnhasGy5xnr9NU9dIq8lPewQ2EPWG/pOlRJUmNMlKm3fHvbFcgHMAj1vI7BefaiM7NCrXRIR2a6jkGGxfDnzcQsefhGJ+ULm/+tVIia1WTV68barkBxCAeBSTge4vzBfgyUqAsgImpiJrVXBKDEfWA6Apf3lB3b1ZfBS4UNDREHNs+GJHB6H/tpdKrVUQXs1XELR/9rf0/JsY8LWxHAoNHxvSV6wZ2lJFdW0iP5vUlvEY1TbVH1P+cZ65V+6y+fj6XMWmIe4Mv5m0sIpgCWELAGgsC5K+PjFc7nBevHqJen8iM8oQr+rVX0RNisCfgfuvBi3rr+fLmX6scfg7WBS/8ukTvze787B8VgA3emb5ao5PvvrCH3Dq0i05yfrtws2VAjAI6yGiDj7GtGJySdVJrG/iiVgHsscD09eao5/X7jeJxha1Orh/UUZ9RUM6VCc9B7ZvIdw+MlTuGd9P9h+88Ob6/TlBN/H25BjvYRqZh32E9YDPxwW0jNcLYHoZVhK8nXwkhJGBsIjJ8FxmMYpylYbtj2AkaReS2FxS37t82ln05KdNQDCZugYIXSHVD2rmjiF0INbAwQNq5bcQKbn4RHYwB/KZ4x97DiMAATeuEa1QQCm7YA558d346QyOK4Ku143BKibzxkL6HGUxEBTu7UceA0Jnn3wVdzOn4c+xUvXcHpKOiMjXSpWc/c60OUJD2iAIyJLCBxzQG4J5Ghy/acVCFTdxouEqn2Do6KP9w5jp59pdFGjmJSCUAAc0a2yj9QPUvNERqwzcY4gPSd+EX+fyVgzXCyjp6DxMxiIxGH2T9/WAF/RisIg6fyFRhxhFopxBtEMHn6/WBaI/CgD8t21bkffhsY5KiZf2iFhEGEAyNtHCcH2XFsoM4BgWSEMX61s0j5Nv7x8rfE66SPx6/Uu2pkFUQCMBHG/c08OB+48+VRe41YCGBibdHL+nnkiewK6Auwbf3XaKZD55yzcAOOgEzbc1uy8DZGlh/Yd0xEQQbBfiL3/flLL3/2BCXoBHDGAjfdH5nuWVoVy3i+PWCTZbrC87Dy/q0VR9jiL6I0DWsNHYdOeF1iwjr+yxEKv+9do/ZLzguUb3zW9QzR99ai7sznrxarhvouY0M+qPrBnXUbf1u0b9ZDehTEREMr3rU1kD2i7OJZeN65EpRWEIIKcugRgjGMb70DDauab623WlriMEFvsHLLBYR7MtJ2YZiMHF7IA9/OoyBEP1rC27IX/htiUZOPDV+gN0iRCheZRS1sgeiLBZsPaBRf5/+Z7Te7KOKt7VPD8Bg494vZqplBQTS4Z2babp7SYp2GUWJUDyuJGBAhKgmbKNtFIk77DyaIufy8zW60bDaIGUDFHMzPBTdBem8iOREWrE7qbWIPsM5g9RCDE7hV4kIY6SmNrVJSe3Ror768Rrps76owOsNEPGJNKxVe4+qEPzjkm1axBKFgIZZRTsbIGIeYNAO0cJfHqOBVkQObD7gODrYqPZsWIn4Egj2GCRMWbGzSDHRvQnmSUa0YUcgug7nF9rvS1cPZlQGKTUu7dNGRT8Uknvtj+UWQRi+67Au6dUiRoZ1Mk+0BQqw1kBGEQbliGDFBDui73GdgeXDhzPXajTzpAfGyktXD9G+FYLuo9/N089jXvyFKwep3Q6EzodG91FB/N3pa+TzuRv1dyESg/5tYnWizrBs8HbxOGtwfzmsc1OJS0rXSU1MeCHzwN5EPq5/Jc14QIQ3+q3P522UuKQMef+ftTLypZ/Utxhi+2d3jS52oh4C9pRHL9d9TAgh5Rlce+qElSxLsjhLyXX7E3SM4y2bP0fgGoJgGyMyeNnOw5ot0qclvd9J2YZiMPGo8nfrmEj1iTuWVjjN98+1e2TN3mMqzBpFqmxBYTbcMEPwtRfFu+1wkgpoiFzEAOWuC7priiKiHQ2xGDYS93wxUy8Co7q1lGevGCjtC1Lkd1j5pboLfteRX7A7YDByQdfmGrnsSrE8R2w9aE7391ZhM1I20qOQqotTwx2LCAP4FU55dLxWn65ds6oWbDSKblmDgf3AgugkfCeQwWQI0pZRNAziRVSt6jrZZA8Myo0IAXyPqbiuFZGDRQTwdWQwQNTkFf3aSdbpM7LMxoNzX6Lj4nEGEHUQLTn5kfGWAiWElNbg9pM7R2mk7A9Ltslrf6zQSSpECkOD/O+48wKyz4EYe8PgzjpJhoJ8Y1//VQY8PUkzKOBdO+m+S9SHF+cWrh2IuIfAiqyrq/t3kM4FfQhAvQT0sxBBESUFYRP3auC8tubIV+O+xxCDETXsC7AuAMcBWBeP8za4b330kr4qhN/y+Vy1CwmrXlWF9p8eulSLvRYH2gaORSC2EUII8TbIEkawQUmydh2BbGD415dGZiPqaSBIAfeoKNKNgsvdmte3G/RGSFmCYjBxv9FUrCC3De+mEavfLjD7xiE18reVO+T1P5Zr8awnHfjpGkLAwHaxciglU+KOpxd5f/4Wc9q6ISbfdH4XaV63tvy0dJu8NGWpjHjxB63WjdQQVMqeeL25cFD7gsgT6+JZ7gJxGf6aSFn9//buBDyqMkv4+CErYQmEEEJIQkIgrGELW4MIogIK2CouNEqr2C6Afra0owOjHzQ+wzi2PdMjI/jZLaK2To+CX3e7MKLNpiAqCIGw04Q9hEhCCCEQILnznLdSZYIxJqG23Pv/PU+oLJdaUidV95573nOulDsB90mWa5l+Q2wjGdxouSttG3JGfO3OQ54TLw2hfw+63FcHDC568EZ54Pr+NW536+Du5sy2e9l9sHIvrdXluDrVfc6dI8xy4B+iS8v1cemJIohkdGxnqve2BkkyWOkJw5qGiWrFokpPqP01WJNP9AlGIOggt9dmTDAnxd/6LFvuWfBXs99x86BunpPSwWjmhCHym59fZ/owTx7ey1Spat/dJY/+tNpJFd1H+4+pY2Rg5wSTKNZBapfvA/7jLcOq9VKuWrWvAx3dPd51eJwmSTUh4At6wk+rdd2vX9qz15cmDEg3fex1VYImhpc/PdmsTGOILwDUXBijRSmFJecbbb9gN213pCcD31m/07Q6G16PNn5AsGL6FBpEk0cvLd8oy77cZSpktH+cVpzoQYBWSfxYtdY1GammD7AmAi5PvGrFsF7P0MpqRT0wefr2q001y3+v22GWzWtV2YSB6Wagmnvpn3sZYkMrg7XKWQ8oNEnhjaoNTU7r8vZV2w+aVhEN6SGog8C0XYY/lm7Du9wHvzpspz40VtbvPmqm03a6whYHOmBwZC3VxZpk3fLbB306MMwbXMN2dElYhUnwuhOJtW3/zQsPcIBeSZd2606srrrQZXVaFX459zA3f73W6P3RHqU64LDqfdqXVyiR4aG0xUHQJ4QXP3KT3L/wA1Nxr/ssvxxffUZCsNGE5fgB6XV+zdDq+/IKq8bXUa3AnTF2gNlXqtrSRfdzBqcnmvZEOlDv4LdFPh2WZh5TZhd5Y8028xz4oh1FVfo4Fj14g5w6dUraxsZKSAg1NQDwQ+JbufIB2mNe5+14Oxmsby0D/dSqwd37/u3Pss3l8B4kg9H4+WwvZv78+TJs2DBp1qyZtG5Nz0a70R3w+6/rZ3rDzX1nrUmi6iTwj//vXXU62Li6R0dTqbZ6R/WqMB1Kp/3f9OdVpzHrMvYX7x8rLz1wg6x99h759aSRpt1E1SRWmxZRpo/krqPfNugxbfr7ca+0iKjWKqJvZ9Mqwl0lUx+6DEV74PVOiWdJoYMqg3UIjsZMQ6uC6yvYE8FKq4AHd0k0PYCfvu2HVx1URaXW91tF6Ou1VurV5Jifk8HuYaLa/mNLTp6nBdD+vFNmAJRWtwPBTPc5Fs+4yZycmnPHiKDtu34lf6O1vY4+cuMg00f3cldVruRYsmqraXfkqxYRVYfsuXuj13Siy9v0tUkHFgMA6tYyL+8K5vloYZQWil0+9Fjnh/RIbFunFj3e0C3RlQzW4eC6IsUXg1EBf/PZ0daFCxfkjjvukOnTp/vqJhBgugx9ZM+OlcvRf2Ymgde1gbu+cGsyV5ctu/v0qpXZruRwTYOh9IBLkwe1VdhqNW5e0VkpOHMuYMPjqhrbL81crsjaX+//S79gewyQq2tlsFYEa3/Fd7/YYb6uraLXiRY+eIO8P2uSqciD94fIaTJYEz9x0b4dwlGVvp5XHSaq90GX4NU2PA4IJlrppCeq3QlJiOk3rP7y9R5z2b3yANpXNNms7S9m1dKeDAAQwFWSlUOK60uHx8/4/f/I/1m8wjOYVGn+QAsc/NUiQlVN/mqLCHq/ww58lgyeN2+ezJw5U3r37u2rm0CAaVJ20UPjzLCRpFjX8JD6mHGDq6/no69+7Onzq2f+9HsjergGW9VXjyvoG6xJaW1B0TXBewcuOmRFe3Cuzna1iqgPbU6vdHI5Gh894aF/I7XtAGmv7TfXbJNbf7NUBj21WG57YZmsyMoxlbC+7n3Y2ERFhEvLKP+c/bejvqntax0id7ywRBJiWvi1IldPvLVsGmFWiOhwEW0R8WPD4wAEN21xpPs92tZHaeWWr+mKtC7ted0AgGDibhvZkPkp6pVPNktBiavAa967n3mOpb+uLCTzZzJYj83iK1cA0S8YdhFUPYPLysrMh1txcbG5rKioMB9OoY9VD4zt/pgzO7WXf51yrfzDm3+Th1/5SP7t3uvNwDQdztGiaXiDHn+PygqUnUf0eur+BpF/+qwZaHeNqcbU373l1erg11Ztlf/48CszcKSutlUmbXolt7VlLDghznWnQSuDa3qMGuvPLv3cTGPXXoe9U+JM/20dSvST9EQJC2li69+NkwRDrHeIaW6WtW89mPe9+6E71/nFZ00rDn/ex9AmTUzPNe0fv+94gezLdbWw6BzfmthvhIIhzhEcdObDsi93mxOiHdtG2yomiHM4AXEOb4ivXG2WV1RS7/cBnUX0x8+yJTk22rSLXLphlyxeuUUeHp0pX+09Zo6T+qW2u+L3l/rEep/UdvLZjsMyJL2Drd7XYD91jc+gSgY/99xzpqL4cjqo4dKlS+KkJ+/MmTPmhcnuwykGJreWmTf0l3//ny3ywKIPzfeGpMVJYaGrQqy+Elq4QjrrQK4UFtZ9mf2a7YfNZc+EVg2+7R9yW2aqrM4+YAacxDQNkVsHdvnR/6PP/bZDJ6RjbEu5dO6sFJ5reK+lYOWEOI9tHiHZRwrk25MFpke2Ki27KC+vzJYPNueInnK4qX8neeja3mY6+XfKvR6HCJxgifWeiTGybk+u7D10TNpWGeRxtLDE9PZs2zzc73E3MCXWJIOXb9wtOfmnzffaNg0h/huhYIlzBF7fpBhZJiKd4qKl+HSR2AlxDicgzuENIeUVooc/R78tqvd+3b8sW29WUE67NkP6p8bJmu0H5fefbJa+ia1k66ET0qNDGykrLZGyUv/F+qPXZciUoelSUVYqhVd6w4APaUx7PRk8a9Ysef7552vdZteuXdK9e3dpiNmzZ8uvfvWrapXBycnJEhMTI9HR9W9D0Fjpi5L2odHH7YQDqqmj20iZFSoLP95kvp4wuKe0aeAglpgYy/Tw259fLG3a1H3J4Pq/u257ZJ8u9fp/daHX9sq0CXL3i3+RBSu2SufE+MoK5NrPhp45f1GuyUj1+v0JFk6I8w5tW0vW4ZNihTf1xPTL762T9zfnmKXwc24fLv060Q7C7oIl1gelJ5lk8KGiMuma8t3KiT0nXTu0nRLa+v315oZBzeW59zfKVzn5cv7CJWkeGS7dUxPpxdYIBUucI/Cuz2wh//npNrmuT5rt9mGIczgBcQ5v0XlChaUX6vVeoIPXv9h33LSBuOknvcy+xdO3Xy2PL/lE/undDVJeYcmwHh298v5Sn1i32dsZbCwsLMz7yeAnnnhC7rvvvlq3SUtzDcxqiMjISPNxOf3DdNqBhb4oOelxTx87QEJCmphm8O1jrmyavQ6R+3zXEZNMrcuEUe0VvGr7QRnUOcH0+PVFQ/jE2Gh5+aFx8vMFf5Gn/rhSXn/0p5JRSy/g7Ue+NZd9UuJtHQN2j3P3ELn84lIT13qG+5OtOWZI19J/uM0vk88RHIIh1vunuYZjZh3Ml7H9v1uhcPyUa+WB9n739/1r3TxKBnbpIF/tOyahTUIko2OchPJ30WgFQ5wj8KKbNZVVv55i25M6xDmcgDiHt/oG7z1eYOKpLu8JF8vL5Td/3SAhTZrI7IlXefYJR/dNk+t6p3qGzQ9JT/LavgaxDrup699GvZLBcXFx5gPwNn0RnjZmgFeuq0dSnEkG6xA57TFUG+0N/Ju/fCH63vTULcN8euCiw+3+/b7RZmDew68sl8nDe8n4zHTpFN+6xn6yqk8Kw+MaM/egATM4IUVk0/7jUlhyXu66OoNEMPxOh1FqD8+qE5nVsULXUqIOV3girqFG9UqVL/cek0tWhXRheBxgC3ZNBAMA6i6+dXMzFP10aZkZwvZj/v+Xu2V/3ilznHz5QOHZE4fLhr3HpLyiQvqlxvM0AFfIZ6Ubhw8flqysLHNZXl5uPtePkpISX90k4KkMVruOuqpra/PXjXtk59GT8tOBXaVnsu9PdIzomSLzJo00A5teXvGNTHjuv+XOf3tP/rRue7WhddmH8k3SRgeKoXHvALkHFKpPsvZ7hgoC/qavKVp5u/vYSTlbdtHz/Vx3MrhNgJLBGd+1zUlvzxo8AAAAO2hfeSykQ+Tq4m/bDpjLaWO/XySWENNC/t/D4+TF+8dK04igGn0FNEo++yuaM2eOvPHGG56v+/fvby5Xr14t11xzja9uFjCtHpQmeWujyZAXP/paoiLC5PEJQ/z2m7t1SHe5oX9nWbP9kHz4zT5Zt+uI/POydbIvt1Ceuf1quVRRYZI1PZPiTPIGjT8ZnFd01pzF1h2cttHNpD99ghEgmZ0SZHNOnmQfOiE/6Zpkvpd76oyEhYRIu1auqc/+pm109MTX3twCKoMBAABs1CbCvUqye6KrYOuHaDu9rIMnpEv7GNNruCYDKlueAQjiyuDXX3/dTGW8/INEMHxNzxpqr2BtE1GbJauy5NviUrn/2n7SroED6xoqKiJcbszsIgsfvFFWzptiEtjvfLFT/vm9z839vlheIb1pEWGbNhFaGawtIgpKzsnoPp0klH6aCJABnV0DCzUhXLUyWF83AxmXPx/RW7onxkrvjrSiAgAAsNP8lK2HTvzotloUUFp2kYQv4CdM+IAt+9Rpq4iD3xbJ2fMXatxGl6osWbXVJOumXttXAknPfP5h+gRzn99Zv1NmvbXSfJ9kcOOnVcChIU3kRFGJrPC0iOgc6LsFB+uX2t70SHf3DdaWNSdOnw1Yiwi3iT/pLu89eYc0bxoR0PsBAAAA7xjWLcmsPHv1b1vky71Ha93WXaiQmeYqXADgWySDYUs6RM6yRHbnFtT4899/ulnOX7xk2kNolW6gaSWzJoR1yNzhk8XmewyPa/y00lKT/ceLSkyLiNiWUezgIKCim0VKekKsbD14wkxs1hNj+lqZGOBkMAAAAOxFh8Zpj189JvrV65/Kkcrj3JpsPuAqVMikFQTgFySDYUu9kl09iTbs+f4ZyDPnyuT9jXtN8mP8gC4STG+Wr06fYFpGpMa1kuTY6EDfJXipb/DRgjNScOacjO6bRosIBJxWXJy7cEn2HCsI+PA4AAAA2FeflHiZe+cIOV1aJo8t/rjaEGM3bSf6TU6eaSvBPingHySDYUsjeqZITPOm8vb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+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1400x310 with 1 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      }
+                                  ],
+                      "source":  [
+                                     "\n",
+                                     "def plot_series_grid(series_ids: tuple[str, ...], title: str, columns: int = 2) -\u003e None:\n",
+                                     "    rows = int(np.ceil(len(series_ids) / columns))\n",
+                                     "    fig, axes = plt.subplots(rows, columns, figsize=(14, 3.1 * rows), squeeze=False, layout=\"constrained\")\n",
+                                     "    for axis, series_id in zip(axes.flat, series_ids, strict=False):\n",
+                                     "        frame = in_analysis_window(series_frames[series_id])\n",
+                                     "        label = service_summary.loc[service_summary[\"series_id\"] == series_id, \"description\"].iloc[0]\n",
+                                     "        axis.plot(frame[\"timestamp\"], frame[\"value\"], color=\"#2a6f97\", linewidth=1.25)\n",
+                                     "        axis.set_title(f\"{series_id}: {label}\", fontsize=10)\n",
+                                     "        axis.grid(alpha=0.25)\n",
+                                     "    for axis in axes.flat[len(series_ids) :]:\n",
+                                     "        axis.set_visible(False)\n",
+                                     "    fig.suptitle(title, fontsize=14)\n",
+                                     "    plt.show()\n",
+                                     "\n",
+                                     "\n",
+                                     "plot_series_grid(MACRO_FEATURE_SERIES_IDS, \"Active macro and market feature histories\")\n",
+                                     "plot_series_grid((GSCPI_SERIES_ID,), \"Exploratory supply-chain challenger\", columns=1)"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "c442f898",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "c442f898"
+                                   },
+                      "source":  [
+                                     "## Build a timestamp-aligned analysis panel\n",
+                                     "\n",
+                                     "This panel is intentionally for descriptive analysis: values are aligned on their reference month, regardless of when they were released. Missing early history remains missing. Forecasting requires the release-aware snapshots shown later."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  41,
+                      "id":  "38d7b859",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "38d7b859"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "name":  "stdout",
+                                          "output_type":  "stream",
+                                          "text":  [
+                                                       "Analysis panel reference months: 2000-01-01 to 2026-09-01\n"
+                                                   ]
+                                      },
+                                      {
+                                          "data":  {
+                                                       "text/html":  [
+                                                                         "\u003cdiv\u003e\n",
+                                                                         "\u003cstyle scoped\u003e\n",
+                                                                         "    .dataframe tbody tr th:only-of-type {\n",
+                                                                         "        vertical-align: middle;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe tbody tr th {\n",
+                                                                         "        vertical-align: top;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe thead th {\n",
+                                                                         "        text-align: right;\n",
+                                                                         "    }\n",
+                                                                         "\u003c/style\u003e\n",
+                                                                         "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
+                                                                         "  \u003cthead\u003e\n",
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+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                                    non_missing_months first_available  \\\n",
+                                                                          "CPI_YOY                                            319      2000-01-01   \n",
+                                                                          "UNRATE                                             319      2000-01-01   \n",
+                                                                          "CPIAUCSL                                           319      2000-01-01   \n",
+                                                                          "ipman_change_1m_pct                                320      2000-01-01   \n",
+                                                                          "FEDFUNDS                                           320      2000-01-01   \n",
+                                                                          "ipman_change_6m_pct                                320      2000-01-01   \n",
+                                                                          "ipman_change_3m_pct                                320      2000-01-01   \n",
+                                                                          "global_supply_chain_pressure_index                 320      2000-01-01   \n",
+                                                                          "target                                             320      2000-01-01   \n",
+                                                                          "ipman_change_3m                                    320      2000-01-01   \n",
+                                                                          "ICSA                                               321      2000-01-01   \n",
+                                                                          "YC_SPREAD                                          321      2000-01-01   \n",
+                                                                          "SPY_RETURN_12M                                     321      2000-01-01   \n",
+                                                                          "SPY_RETURN_3M                                      321      2000-01-01   \n",
+                                                                          "XLI_RETURN_12M                                     321      2000-01-01   \n",
+                                                                          "VIXCLS                                             321      2000-01-01   \n",
+                                                                          "XLI_RETURN_3M                                      321      2000-01-01   \n",
+                                                                          "\n",
+                                                                          "                                   last_available  \n",
+                                                                          "CPI_YOY                                2026-08-01  \n",
+                                                                          "UNRATE                                 2026-08-01  \n",
+                                                                          "CPIAUCSL                               2026-08-01  \n",
+                                                                          "ipman_change_1m_pct                    2026-08-01  \n",
+                                                                          "FEDFUNDS                               2026-08-01  \n",
+                                                                          "ipman_change_6m_pct                    2026-08-01  \n",
+                                                                          "ipman_change_3m_pct                    2026-08-01  \n",
+                                                                          "global_supply_chain_pressure_index     2026-08-01  \n",
+                                                                          "target                                 2026-08-01  \n",
+                                                                          "ipman_change_3m                        2026-08-01  \n",
+                                                                          "ICSA                                   2026-09-01  \n",
+                                                                          "YC_SPREAD                              2026-09-01  \n",
+                                                                          "SPY_RETURN_12M                         2026-09-01  \n",
+                                                                          "SPY_RETURN_3M                          2026-09-01  \n",
+                                                                          "XLI_RETURN_12M                         2026-09-01  \n",
+                                                                          "VIXCLS                                 2026-09-01  \n",
+                                                                          "XLI_RETURN_3M                          2026-09-01  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  41,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "analysis_series_ids = FEATURE_SERIES_IDS + (GSCPI_SERIES_ID,)\n",
+                                     "wide_features = pd.concat(\n",
+                                     "    [\n",
+                                     "        in_analysis_window(series_frames[series_id])[[\"timestamp\", \"value\"]].rename(columns={\"value\": series_id})\n",
+                                     "        .set_index(\"timestamp\")\n",
+                                     "        for series_id in analysis_series_ids\n",
+                                     "    ],\n",
+                                     "    axis=1,\n",
+                                     ").sort_index()\n",
+                                     "analysis_panel = wide_features.join(\n",
+                                     "    target.set_index(\"timestamp\")[[\"registered_target\"]].rename(columns={\"registered_target\": \"target\"}),\n",
+                                     "    how=\"outer\",\n",
+                                     ")\n",
+                                     "analysis_panel = analysis_panel.join(\n",
+                                     "    ipman_change_3m.set_index(\"timestamp\")[[\"value\"]].rename(columns={\"value\": \"ipman_change_3m\"}),\n",
+                                     "    how=\"outer\",\n",
+                                     ")\n",
+                                     "\n",
+                                     "panel_coverage = pd.DataFrame(\n",
+                                     "    {\n",
+                                     "        \"non_missing_months\": analysis_panel.notna().sum(),\n",
+                                     "        \"first_available\": analysis_panel.apply(lambda column: column.first_valid_index()),\n",
+                                     "        \"last_available\": analysis_panel.apply(lambda column: column.last_valid_index()),\n",
+                                     "    }\n",
+                                     ").sort_values(\"non_missing_months\")\n",
+                                     "print(f\"Analysis panel reference months: {analysis_panel.index.min().date()} to {analysis_panel.index.max().date()}\")\n",
+                                     "panel_coverage"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "85096389",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "85096389"
+                                   },
+                      "source":  [
+                                     "## Distributions, rolling behavior, and stress regimes\n",
+                                     "\n",
+                                     "The binary target is rare, so compare regime summaries alongside their counts. Rolling statistics are descriptive and may overlap because returns and IPMAN changes themselves use trailing windows."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  28,
+                      "id":  "9237d64b",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "9237d64b"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
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+                                                                         "      \u003ctd\u003e319.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.608072\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.718826\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-1.958761\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.032414\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.420261\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e5.985543\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e8.979361e+00\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eUNRATE\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e319.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e5.609404\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.932388\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.400000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.600000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e5.000000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e9.500000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.480000e+01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eICSA\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e321.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e371890.965732\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e391182.698620\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e190000.000000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e207000.000000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e318000.000000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e620000.000000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e5.946000e+06\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eVIXCLS\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e321.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e19.789502\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e7.743577\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e9.510000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e11.570000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e17.520000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e33.400000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e5.989000e+01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eSPY_RETURN_3M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e321.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.314015\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e7.394001\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-29.644020\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-11.358087\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.029633\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12.901712\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.605482e+01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eSPY_RETURN_12M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e321.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e9.747746\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e16.503192\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-43.424443\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-22.368114\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12.869139\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e32.307827\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e5.623114e+01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eXLI_RETURN_3M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e321.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.565997\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e8.861390\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-34.025298\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-12.823830\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.634113\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e15.495004\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.278897e+01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eXLI_RETURN_12M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e321.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e10.634455\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e18.704838\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-51.670909\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-21.875522\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e12.571633\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e36.623313\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e7.344757e+01\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eglobal_supply_chain_pressure_index\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e320.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.043906\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.027559\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-1.590000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-1.040500\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.185000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.606500\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e4.430000e+00\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                                    count           mean            std  \\\n",
+                                                                          "ipman_change_1m_pct                 320.0       0.031235       1.289274   \n",
+                                                                          "ipman_change_3m_pct                 320.0       0.104482       2.384910   \n",
+                                                                          "ipman_change_6m_pct                 320.0       0.217812       3.263535   \n",
+                                                                          "FEDFUNDS                            320.0       2.048469       2.022276   \n",
+                                                                          "YC_SPREAD                           321.0       1.041059       0.962217   \n",
+                                                                          "CPIAUCSL                            319.0     235.635251      43.461518   \n",
+                                                                          "CPI_YOY                             319.0       2.608072       1.718826   \n",
+                                                                          "UNRATE                              319.0       5.609404       1.932388   \n",
+                                                                          "ICSA                                321.0  371890.965732  391182.698620   \n",
+                                                                          "VIXCLS                              321.0      19.789502       7.743577   \n",
+                                                                          "SPY_RETURN_3M                       321.0       2.314015       7.394001   \n",
+                                                                          "SPY_RETURN_12M                      321.0       9.747746      16.503192   \n",
+                                                                          "XLI_RETURN_3M                       321.0       2.565997       8.861390   \n",
+                                                                          "XLI_RETURN_12M                      321.0      10.634455      18.704838   \n",
+                                                                          "global_supply_chain_pressure_index  320.0       0.043906       1.027559   \n",
+                                                                          "\n",
+                                                                          "                                              min             5%  \\\n",
+                                                                          "ipman_change_1m_pct                    -15.371556      -0.972916   \n",
+                                                                          "ipman_change_3m_pct                    -19.013071      -2.179607   \n",
+                                                                          "ipman_change_6m_pct                    -18.585385      -4.273020   \n",
+                                                                          "FEDFUNDS                                 0.050000       0.080000   \n",
+                                                                          "YC_SPREAD                               -1.060000      -0.390000   \n",
+                                                                          "CPIAUCSL                               169.300000     177.210000   \n",
+                                                                          "CPI_YOY                                 -1.958761       0.032414   \n",
+                                                                          "UNRATE                                   3.400000       3.600000   \n",
+                                                                          "ICSA                                190000.000000  207000.000000   \n",
+                                                                          "VIXCLS                                   9.510000      11.570000   \n",
+                                                                          "SPY_RETURN_3M                          -29.644020     -11.358087   \n",
+                                                                          "SPY_RETURN_12M                         -43.424443     -22.368114   \n",
+                                                                          "XLI_RETURN_3M                          -34.025298     -12.823830   \n",
+                                                                          "XLI_RETURN_12M                         -51.670909     -21.875522   \n",
+                                                                          "global_supply_chain_pressure_index      -1.590000      -1.040500   \n",
+                                                                          "\n",
+                                                                          "                                              50%            95%           max  \n",
+                                                                          "ipman_change_1m_pct                      0.043089       1.163776  7.858989e+00  \n",
+                                                                          "ipman_change_3m_pct                      0.221818       2.210450  1.681345e+01  \n",
+                                                                          "ipman_change_6m_pct                      0.475021       3.461830  1.967475e+01  \n",
+                                                                          "FEDFUNDS                                 1.375000       5.330000  6.540000e+00  \n",
+                                                                          "YC_SPREAD                                0.990000       2.540000  2.840000e+00  \n",
+                                                                          "CPIAUCSL                               232.282000     319.836700  3.341310e+02  \n",
+                                                                          "CPI_YOY                                  2.420261       5.985543  8.979361e+00  \n",
+                                                                          "UNRATE                                   5.000000       9.500000  1.480000e+01  \n",
+                                                                          "ICSA                                318000.000000  620000.000000  5.946000e+06  \n",
+                                                                          "VIXCLS                                  17.520000      33.400000  5.989000e+01  \n",
+                                                                          "SPY_RETURN_3M                            3.029633      12.901712  2.605482e+01  \n",
+                                                                          "SPY_RETURN_12M                          12.869139      32.307827  5.623114e+01  \n",
+                                                                          "XLI_RETURN_3M                            3.634113      15.495004  3.278897e+01  \n",
+                                                                          "XLI_RETURN_12M                          12.571633      36.623313  7.344757e+01  \n",
+                                                                          "global_supply_chain_pressure_index      -0.185000       2.606500  4.430000e+00  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  28,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "summary_statistics = analysis_panel[list(analysis_series_ids)].describe(\n",
+                                     "    percentiles=[0.05, 0.25, 0.5, 0.75, 0.95]\n",
+                                     ").T\n",
+                                     "summary_statistics[[\"count\", \"mean\", \"std\", \"min\", \"5%\", \"50%\", \"95%\", \"max\"]]"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  29,
+                      "id":  "e1ce8a6f",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "e1ce8a6f"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "ename":  "KeyError",
+                                          "evalue":  "\u0027HY_SPREAD\u0027",
+                                          "output_type":  "error",
+                                          "traceback":  [
+                                                            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+                                                            "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+                                                            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Mina Attari\\BMO-C2-agentic-forecasting\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3641\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3640\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-\u003e \u001b[39m\u001b[32m3641\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_engine\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mget_loc\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mcasted_key\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   3642\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+                                                            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:168\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--\u003e \u001b[39m\u001b[32m168\u001b[39m \u001b[33m\u0027Could not get source, probably due dynamically evaluated source code.\u0027\u001b[39m\n",
+                                                            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:197\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--\u003e \u001b[39m\u001b[32m197\u001b[39m \u001b[33m\u0027Could not get source, probably due dynamically evaluated source code.\u0027\u001b[39m\n",
+                                                            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7668\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-\u003e \u001b[39m\u001b[32m7668\u001b[39m \u001b[33m\u0027Could not get source, probably due dynamically evaluated source code.\u0027\u001b[39m\n",
+                                                            "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7676\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-\u003e \u001b[39m\u001b[32m7676\u001b[39m \u001b[33m\u0027Could not get source, probably due dynamically evaluated source code.\u0027\u001b[39m\n",
+                                                            "\u001b[31mKeyError\u001b[39m: \u0027HY_SPREAD\u0027",
+                                                            "\nThe above exception was the direct cause of the following exception:\n",
+                                                            "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+                                                            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[29]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m      4\u001b[39m )\n\u001b[32m      5\u001b[39m rows = int(np.ceil(len(distribution_ids) / \u001b[32m2\u001b[39m))\n\u001b[32m      6\u001b[39m fig, axes = plt.subplots(rows, \u001b[32m2\u001b[39m, figsize=(\u001b[32m14\u001b[39m, \u001b[32m2.9\u001b[39m * rows), squeeze=\u001b[38;5;28;01mFalse\u001b[39;00m, layout=\u001b[33m\"constrained\"\u001b[39m)\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m axis, series_id \u001b[38;5;28;01min\u001b[39;00m zip(axes.flat, distribution_ids, strict=\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[32m----\u003e \u001b[39m\u001b[32m8\u001b[39m     values = analysis_panel[series_id].dropna()\n\u001b[32m      9\u001b[39m     axis.hist(values, bins=\u001b[32m24\u001b[39m, color=\u001b[33m\"#2a6f97\"\u001b[39m, alpha=\u001b[32m0.85\u001b[39m)\n\u001b[32m     10\u001b[39m     axis.axvline(values.median(), color=\u001b[33m\"#b22222\"\u001b[39m, linestyle=\u001b[33m\"--\"\u001b[39m, label=\u001b[33m\"median\"\u001b[39m)\n\u001b[32m     11\u001b[39m     axis.set(title=series_id, ylabel=\u001b[33m\"Months\"\u001b[39m)\n",
+                                                            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Mina Attari\\BMO-C2-agentic-forecasting\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4378\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4374\u001b[39m \n\u001b[32m   4375\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[32m   4376\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m self.columns.nlevels \u003e \u001b[32m1\u001b[39m:\n\u001b[32m   4377\u001b[39m                 \u001b[38;5;28;01mreturn\u001b[39;00m self._getitem_multilevel(key)\n\u001b[32m-\u003e \u001b[39m\u001b[32m4378\u001b[39m             indexer = self.columns.get_loc(key)\n\u001b[32m   4379\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m   4380\u001b[39m                 indexer = [indexer]\n\u001b[32m   4381\u001b[39m         \u001b[38;5;28;01melse\u001b[39;00m:\n",
+                                                            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Mina Attari\\BMO-C2-agentic-forecasting\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3648\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   3643\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   3644\u001b[39m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m   3645\u001b[39m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m   3646\u001b[39m     ):\n\u001b[32m   3647\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m-\u003e \u001b[39m\u001b[32m3648\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m   3649\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m   3650\u001b[39m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m   3651\u001b[39m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m   3652\u001b[39m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[32m   3653\u001b[39m     \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n",
+                                                            "\u001b[31mKeyError\u001b[39m: \u0027HY_SPREAD\u0027"
+                                                        ]
+                                      },
+                                      {
+                                          "data":  {
+                                                       "image/png":  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",
+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1400x1450 with 10 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      }
+                                  ],
+                      "source":  [
+                                     "distribution_ids = (\n",
+                                     "    IPMAN_FEATURE_SERIES_IDS\n",
+                                     "    + (\"UNRATE\", \"HY_SPREAD\", \"VIXCLS\", \"SPY_RETURN_3M\", \"XLI_RETURN_3M\", GSCPI_SERIES_ID)\n",
+                                     ")\n",
+                                     "rows = int(np.ceil(len(distribution_ids) / 2))\n",
+                                     "fig, axes = plt.subplots(rows, 2, figsize=(14, 2.9 * rows), squeeze=False, layout=\"constrained\")\n",
+                                     "for axis, series_id in zip(axes.flat, distribution_ids, strict=False):\n",
+                                     "    values = analysis_panel[series_id].dropna()\n",
+                                     "    axis.hist(values, bins=24, color=\"#2a6f97\", alpha=0.85)\n",
+                                     "    axis.axvline(values.median(), color=\"#b22222\", linestyle=\"--\", label=\"median\")\n",
+                                     "    axis.set(title=series_id, ylabel=\"Months\")\n",
+                                     "    axis.legend()\n",
+                                     "    axis.grid(axis=\"y\", alpha=0.2)\n",
+                                     "for axis in axes.flat[len(distribution_ids) :]:\n",
+                                     "    axis.set_visible(False)\n",
+                                     "plt.show()"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  30,
+                      "id":  "6929c646",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "6929c646"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "text/html":  [
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+                                                                         "\u003cstyle scoped\u003e\n",
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+                                                                         "        vertical-align: middle;\n",
+                                                                         "    }\n",
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+                                                                         "    .dataframe tbody tr th {\n",
+                                                                         "        vertical-align: top;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe thead tr th {\n",
+                                                                         "        text-align: left;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe thead tr:last-of-type th {\n",
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+                                                                         "      \u003cth\u003e\u003c/th\u003e\n",
+                                                                         "      \u003cth colspan=\"2\" halign=\"left\"\u003emonths\u003c/th\u003e\n",
+                                                                         "      \u003cth colspan=\"2\" halign=\"left\"\u003emean\u003c/th\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eFEDFUNDS\u003c/th\u003e\n",
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+                                                                         "      \u003ctd\u003e1.425000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.520000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eICSA\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003e327568.027211\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e879807.692308\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e311000.000000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e506000.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eSPY_RETURN_12M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003e11.932489\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-15.213172\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e13.736998\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-14.431306\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eSPY_RETURN_3M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.762873\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-2.787926\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.502613\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-3.229109\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eUNRATE\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e293.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e5.480205\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e7.065385\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e4.900000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e6.150000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eVIXCLS\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e18.782109\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e31.395385\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e17.035000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e27.730000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eXLI_RETURN_12M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e13.076569\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-16.983417\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e14.100055\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-11.287861\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eXLI_RETURN_3M\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.208481\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-4.269214\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e3.880758\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-7.661083\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eYC_SPREAD\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.014014\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.377308\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.930000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.435000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eglobal_supply_chain_pressure_index\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.016973\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.348462\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.195000\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.125000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eipman_change_1m_pct\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.162229\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-1.450004\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.080287\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.856836\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eipman_change_3m_pct\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.524142\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-4.640900\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.324356\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-2.674204\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003eipman_change_6m_pct\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003e294.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26.0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.852584\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-6.959997\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.676113\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-5.841669\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                                      months                  mean  \\\n",
+                                                                          "regime                             no_stress stress      no_stress   \n",
+                                                                          "series_id                                                            \n",
+                                                                          "CPIAUCSL                               293.0   26.0     236.634017   \n",
+                                                                          "CPI_YOY                                293.0   26.0       2.664241   \n",
+                                                                          "FEDFUNDS                               294.0   26.0       2.115306   \n",
+                                                                          "ICSA                                   294.0   26.0  327568.027211   \n",
+                                                                          "SPY_RETURN_12M                         294.0   26.0      11.932489   \n",
+                                                                          "SPY_RETURN_3M                          294.0   26.0       2.762873   \n",
+                                                                          "UNRATE                                 293.0   26.0       5.480205   \n",
+                                                                          "VIXCLS                                 294.0   26.0      18.782109   \n",
+                                                                          "XLI_RETURN_12M                         294.0   26.0      13.076569   \n",
+                                                                          "XLI_RETURN_3M                          294.0   26.0       3.208481   \n",
+                                                                          "YC_SPREAD                              294.0   26.0       1.014014   \n",
+                                                                          "global_supply_chain_pressure_index     294.0   26.0       0.016973   \n",
+                                                                          "ipman_change_1m_pct                    294.0   26.0       0.162229   \n",
+                                                                          "ipman_change_3m_pct                    294.0   26.0       0.524142   \n",
+                                                                          "ipman_change_6m_pct                    294.0   26.0       0.852584   \n",
+                                                                          "\n",
+                                                                          "                                                          median  \\\n",
+                                                                          "regime                                     stress      no_stress   \n",
+                                                                          "series_id                                                          \n",
+                                                                          "CPIAUCSL                               224.379923     233.544000   \n",
+                                                                          "CPI_YOY                                  1.975089       2.441196   \n",
+                                                                          "FEDFUNDS                                 1.292692       1.425000   \n",
+                                                                          "ICSA                                879807.692308  311000.000000   \n",
+                                                                          "SPY_RETURN_12M                         -15.213172      13.736998   \n",
+                                                                          "SPY_RETURN_3M                           -2.787926       3.502613   \n",
+                                                                          "UNRATE                                   7.065385       4.900000   \n",
+                                                                          "VIXCLS                                  31.395385      17.035000   \n",
+                                                                          "XLI_RETURN_12M                         -16.983417      14.100055   \n",
+                                                                          "XLI_RETURN_3M                           -4.269214       3.880758   \n",
+                                                                          "YC_SPREAD                                1.377308       0.930000   \n",
+                                                                          "global_supply_chain_pressure_index       0.348462      -0.195000   \n",
+                                                                          "ipman_change_1m_pct                     -1.450004       0.080287   \n",
+                                                                          "ipman_change_3m_pct                     -4.640900       0.324356   \n",
+                                                                          "ipman_change_6m_pct                     -6.959997       0.676113   \n",
+                                                                          "\n",
+                                                                          "                                                   \n",
+                                                                          "regime                                     stress  \n",
+                                                                          "series_id                                          \n",
+                                                                          "CPIAUCSL                               216.101500  \n",
+                                                                          "CPI_YOY                                  1.581928  \n",
+                                                                          "FEDFUNDS                                 0.520000  \n",
+                                                                          "ICSA                                506000.000000  \n",
+                                                                          "SPY_RETURN_12M                         -14.431306  \n",
+                                                                          "SPY_RETURN_3M                           -3.229109  \n",
+                                                                          "UNRATE                                   6.150000  \n",
+                                                                          "VIXCLS                                  27.730000  \n",
+                                                                          "XLI_RETURN_12M                         -11.287861  \n",
+                                                                          "XLI_RETURN_3M                           -7.661083  \n",
+                                                                          "YC_SPREAD                                1.435000  \n",
+                                                                          "global_supply_chain_pressure_index       0.125000  \n",
+                                                                          "ipman_change_1m_pct                     -0.856836  \n",
+                                                                          "ipman_change_3m_pct                     -2.674204  \n",
+                                                                          "ipman_change_6m_pct                     -5.841669  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  30,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "regime_rows = []\n",
+                                     "for series_id in analysis_series_ids:\n",
+                                     "    paired = analysis_panel[[series_id, \"target\"]].dropna()\n",
+                                     "    for regime_value, regime_name in ((0, \"no_stress\"), (1, \"stress\")):\n",
+                                     "        values = paired.loc[paired[\"target\"] == regime_value, series_id]\n",
+                                     "        regime_rows.append(\n",
+                                     "            {\n",
+                                     "                \"series_id\": series_id,\n",
+                                     "                \"regime\": regime_name,\n",
+                                     "                \"months\": len(values),\n",
+                                     "                \"mean\": values.mean(),\n",
+                                     "                \"median\": values.median(),\n",
+                                     "                \"std\": values.std(),\n",
+                                     "            }\n",
+                                     "        )\n",
+                                     "regime_summary = pd.DataFrame(regime_rows)\n",
+                                     "regime_summary.pivot(index=\"series_id\", columns=\"regime\", values=[\"months\", \"mean\", \"median\"])"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  15,
+                      "id":  "660e7fb1",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "660e7fb1"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "image/png":  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",
+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1400x700 with 2 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      }
+                                  ],
+                      "source":  [
+                                     "rolling_ids = (IPMAN_CHANGE_3M_SERIES_ID, \"UNRATE\", \"HY_SPREAD\", \"XLI_RETURN_3M\", GSCPI_SERIES_ID)\n",
+                                     "fig, axes = plt.subplots(2, 1, figsize=(14, 7), sharex=True, layout=\"constrained\")\n",
+                                     "for series_id in rolling_ids:\n",
+                                     "    axes[0].plot(\n",
+                                     "        analysis_panel.index,\n",
+                                     "        analysis_panel[series_id].rolling(ROLLING_WINDOW_MONTHS, min_periods=12).mean(),\n",
+                                     "        linewidth=1.25,\n",
+                                     "        label=series_id,\n",
+                                     "    )\n",
+                                     "axes[0].set(title=f\"{ROLLING_WINDOW_MONTHS}-month rolling means\", ylabel=\"Native units\")\n",
+                                     "axes[0].legend(ncol=3)\n",
+                                     "axes[0].grid(alpha=0.25)\n",
+                                     "axes[1].plot(\n",
+                                     "    analysis_panel.index,\n",
+                                     "    analysis_panel[\"target\"].rolling(ROLLING_WINDOW_MONTHS, min_periods=12).mean(),\n",
+                                     "    color=\"#b22222\",\n",
+                                     ")\n",
+                                     "axes[1].set(title=f\"{ROLLING_WINDOW_MONTHS}-month manufacturing-stress rate\", xlabel=\"Reference month\", ylabel=\"Share of months\")\n",
+                                     "axes[1].set_ylim(bottom=0)\n",
+                                     "axes[1].grid(alpha=0.25)\n",
+                                     "plt.show()"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "279b8b15",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "279b8b15"
+                                   },
+                      "source":  [
+                                     "## Contemporaneous correlations\n",
+                                     "\n",
+                                     "Pearson summarizes linear co-movement; Spearman summarizes rank co-movement. Both require overlapping observed months and neither identifies causality. The target association is equivalent to a point-biserial correlation when using Pearson because the target is binary."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  31,
+                      "id":  "f6f8a763",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "f6f8a763"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "image/png":  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+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 2000x800 with 4 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      }
+                                  ],
+                      "source":  [
+                                     "correlation_data = analysis_panel[list(analysis_series_ids)]\n",
+                                     "pearson_correlation = correlation_data.corr(method=\"pearson\", min_periods=36)\n",
+                                     "spearman_correlation = correlation_data.corr(method=\"spearman\", min_periods=36)\n",
+                                     "\n",
+                                     "fig, axes = plt.subplots(1, 2, figsize=(20, 8), layout=\"constrained\")\n",
+                                     "for axis, matrix, title in zip(\n",
+                                     "    axes,\n",
+                                     "    (pearson_correlation, spearman_correlation),\n",
+                                     "    (\"Pearson correlation\", \"Spearman correlation\"),\n",
+                                     "    strict=False,\n",
+                                     "):\n",
+                                     "    image = axis.imshow(matrix, vmin=-1, vmax=1, cmap=\"RdBu_r\")\n",
+                                     "    axis.set_xticks(range(len(matrix.columns)), matrix.columns, rotation=90, fontsize=8)\n",
+                                     "    axis.set_yticks(range(len(matrix.index)), matrix.index, fontsize=8)\n",
+                                     "    axis.set_title(title)\n",
+                                     "    fig.colorbar(image, ax=axis, shrink=0.8)\n",
+                                     "plt.show()"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  42,
+                      "id":  "5362e697",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "5362e697"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "text/html":  [
+                                                                         "\u003cdiv\u003e\n",
+                                                                         "\u003cstyle scoped\u003e\n",
+                                                                         "    .dataframe tbody tr th:only-of-type {\n",
+                                                                         "        vertical-align: middle;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe tbody tr th {\n",
+                                                                         "        vertical-align: top;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe thead th {\n",
+                                                                         "        text-align: right;\n",
+                                                                         "    }\n",
+                                                                         "\u003c/style\u003e\n",
+                                                                         "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
+                                                                         "  \u003cthead\u003e\n",
+                                                                         "    \u003ctr style=\"text-align: right;\"\u003e\n",
+                                                                         "      \u003cth\u003e\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eseries_id\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eoverlapping_months\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003estress_months\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003epearson_with_target\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003espearman_with_target\u003c/th\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/thead\u003e\n",
+                                                                         "  \u003ctbody\u003e\n",
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+                                                                         "      \u003cth\u003e11\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eSPY_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e320\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003eVIXCLS\u003c/td\u003e\n",
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+                                                                         "      \u003cth\u003e13\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e320\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e26\u003c/td\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e0\u003c/th\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e12\u003c/th\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e7\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eUNRATE\u003c/td\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e10\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eSPY_RETURN_3M\u003c/td\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e6\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eCPI_YOY\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003e-0.100032\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e4\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eYC_SPREAD\u003c/td\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e14\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eglobal_supply_chain_pressure_index\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003e0.059620\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e5\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eCPIAUCSL\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003e-0.069301\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "                             series_id  overlapping_months  stress_months  \\\n",
+                                                                          "2                  ipman_change_6m_pct                 320             26   \n",
+                                                                          "1                  ipman_change_3m_pct                 320             26   \n",
+                                                                          "11                      SPY_RETURN_12M                 320             26   \n",
+                                                                          "9                               VIXCLS                 320             26   \n",
+                                                                          "13                      XLI_RETURN_12M                 320             26   \n",
+                                                                          "8                                 ICSA                 320             26   \n",
+                                                                          "0                  ipman_change_1m_pct                 320             26   \n",
+                                                                          "12                       XLI_RETURN_3M                 320             26   \n",
+                                                                          "7                               UNRATE                 319             26   \n",
+                                                                          "10                       SPY_RETURN_3M                 320             26   \n",
+                                                                          "3                             FEDFUNDS                 320             26   \n",
+                                                                          "6                              CPI_YOY                 319             26   \n",
+                                                                          "4                            YC_SPREAD                 320             26   \n",
+                                                                          "14  global_supply_chain_pressure_index                 320             26   \n",
+                                                                          "5                             CPIAUCSL                 319             26   \n",
+                                                                          "\n",
+                                                                          "    pearson_with_target  spearman_with_target  \n",
+                                                                          "2             -0.655083             -0.457754  \n",
+                                                                          "1             -0.592641             -0.473231  \n",
+                                                                          "11            -0.449525             -0.342108  \n",
+                                                                          "9              0.445400              0.303540  \n",
+                                                                          "13            -0.439082             -0.355604  \n",
+                                                                          "8              0.385829              0.311903  \n",
+                                                                          "0             -0.342194             -0.342480  \n",
+                                                                          "12            -0.231133             -0.141400  \n",
+                                                                          "7              0.224800              0.182410  \n",
+                                                                          "10            -0.205113             -0.135333  \n",
+                                                                          "3             -0.111313             -0.088742  \n",
+                                                                          "6             -0.109874             -0.100032  \n",
+                                                                          "4              0.103266              0.105432  \n",
+                                                                          "14             0.088278              0.059620  \n",
+                                                                          "5             -0.077266             -0.069301  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  42,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "target_associations = []\n",
+                                     "for series_id in analysis_series_ids:\n",
+                                     "    paired = analysis_panel[[series_id, \"target\"]].dropna()\n",
+                                     "    target_associations.append(\n",
+                                     "        {\n",
+                                     "            \"series_id\": series_id,\n",
+                                     "            \"overlapping_months\": len(paired),\n",
+                                     "            \"stress_months\": int(paired[\"target\"].sum()),\n",
+                                     "            \"pearson_with_target\": paired.corr(method=\"pearson\").iloc[0, 1],\n",
+                                     "            \"spearman_with_target\": paired.corr(method=\"spearman\").iloc[0, 1],\n",
+                                     "        }\n",
+                                     "    )\n",
+                                     "target_associations = pd.DataFrame(target_associations).sort_values(\"pearson_with_target\", key=np.abs, ascending=False)\n",
+                                     "target_associations"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "e4ef704b",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "e4ef704b"
+                                   },
+                      "source":  [
+                                     "## Lagged cross-correlations\n",
+                                     "\n",
+                                     "For a lag $k$, this section correlates the feature observed in month $t$ with the outcome at $t+k$. Positive lags are the forecast-relevant direction: for example, `lag_months = 3` compares a month-$t$ feature with manufacturing stress or IPMAN momentum three months later. Negative lags are included as diagnostic context only."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  33,
+                      "id":  "acf3aaf0",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "acf3aaf0"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
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+                                                                         "      \u003ctd\u003e319\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.458312\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.598340\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e927\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_3M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_3m\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e319\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.458129\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.272851\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e706\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eVIXCLS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003etarget\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e318\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.455578\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.316995\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e850\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eSPY_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003etarget\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e320\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.449525\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.342108\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e702\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eVIXCLS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003etarget\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e320\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.445400\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.303540\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e631\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eICSA\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_3m\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e319\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.439995\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.144957\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e998\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003etarget\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e320\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.439082\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.355604\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "               series_id          outcome  lag_months  overlapping_months  \\\n",
+                                                                          "111  ipman_change_3m_pct  ipman_change_3m           0                 320   \n",
+                                                                          "39   ipman_change_1m_pct  ipman_change_3m           1                 319   \n",
+                                                                          "113  ipman_change_3m_pct  ipman_change_3m           1                 319   \n",
+                                                                          "184  ipman_change_6m_pct           target           0                 320   \n",
+                                                                          "185  ipman_change_6m_pct  ipman_change_3m           0                 320   \n",
+                                                                          "110  ipman_change_3m_pct           target           0                 320   \n",
+                                                                          "41   ipman_change_1m_pct  ipman_change_3m           2                 318   \n",
+                                                                          "186  ipman_change_6m_pct           target           1                 319   \n",
+                                                                          "37   ipman_change_1m_pct  ipman_change_3m           0                 320   \n",
+                                                                          "112  ipman_change_3m_pct           target           1                 319   \n",
+                                                                          "637                 ICSA  ipman_change_3m           4                 316   \n",
+                                                                          "704               VIXCLS           target           1                 319   \n",
+                                                                          "188  ipman_change_6m_pct           target           2                 318   \n",
+                                                                          "187  ipman_change_6m_pct  ipman_change_3m           1                 319   \n",
+                                                                          "927        XLI_RETURN_3M  ipman_change_3m           1                 319   \n",
+                                                                          "706               VIXCLS           target           2                 318   \n",
+                                                                          "850       SPY_RETURN_12M           target           0                 320   \n",
+                                                                          "702               VIXCLS           target           0                 320   \n",
+                                                                          "631                 ICSA  ipman_change_3m           1                 319   \n",
+                                                                          "998       XLI_RETURN_12M           target           0                 320   \n",
+                                                                          "\n",
+                                                                          "     pearson_correlation  spearman_correlation  \n",
+                                                                          "111             1.000000              1.000000  \n",
+                                                                          "39              0.713129              0.551374  \n",
+                                                                          "113             0.677362              0.703910  \n",
+                                                                          "184            -0.655083             -0.457754  \n",
+                                                                          "185             0.642045              0.757187  \n",
+                                                                          "110            -0.592641             -0.473231  \n",
+                                                                          "41              0.588770              0.635415  \n",
+                                                                          "186            -0.578428             -0.409209  \n",
+                                                                          "37              0.533766              0.603456  \n",
+                                                                          "112            -0.524496             -0.402864  \n",
+                                                                          "637             0.524044              0.266029  \n",
+                                                                          "704             0.473866              0.324544  \n",
+                                                                          "188            -0.470860             -0.369316  \n",
+                                                                          "187             0.458312              0.598340  \n",
+                                                                          "927             0.458129              0.272851  \n",
+                                                                          "706             0.455578              0.316995  \n",
+                                                                          "850            -0.449525             -0.342108  \n",
+                                                                          "702             0.445400              0.303540  \n",
+                                                                          "631            -0.439995              0.144957  \n",
+                                                                          "998            -0.439082             -0.355604  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  33,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "lag_rows = []\n",
+                                     "for series_id in analysis_series_ids:\n",
+                                     "    feature = analysis_panel[series_id]\n",
+                                     "    for lag in range(-CORRELATION_LAG_MONTHS, CORRELATION_LAG_MONTHS + 1):\n",
+                                     "        for outcome_name in (\"target\", \"ipman_change_3m\"):\n",
+                                     "            paired = pd.concat(\n",
+                                     "                [feature.rename(\"feature\"), analysis_panel[outcome_name].shift(-lag).rename(\"outcome\")],\n",
+                                     "                axis=1,\n",
+                                     "            ).dropna()\n",
+                                     "            lag_rows.append(\n",
+                                     "                {\n",
+                                     "                    \"series_id\": series_id,\n",
+                                     "                    \"outcome\": outcome_name,\n",
+                                     "                    \"lag_months\": lag,\n",
+                                     "                    \"overlapping_months\": len(paired),\n",
+                                     "                    \"pearson_correlation\": paired.corr(method=\"pearson\").iloc[0, 1] if len(paired) \u003e= 12 else np.nan,\n",
+                                     "                    \"spearman_correlation\": paired.corr(method=\"spearman\").iloc[0, 1] if len(paired) \u003e= 12 else np.nan,\n",
+                                     "                }\n",
+                                     "            )\n",
+                                     "lag_correlations = pd.DataFrame(lag_rows)\n",
+                                     "forecast_relevant_lags = lag_correlations.query(\"lag_months \u003e= 0\").sort_values(\n",
+                                     "    \"pearson_correlation\", key=np.abs, ascending=False\n",
+                                     ")\n",
+                                     "forecast_relevant_lags.head(20)"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  34,
+                      "id":  "7ef695cd",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "7ef695cd"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "image/png":  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",
+                                                       "text/plain":  [
+                                                                          "\u003cFigure size 1700x1300 with 16 Axes\u003e"
+                                                                      ]
+                                                   },
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "display_data"
+                                      }
+                                  ],
+                      "source":  [
+                                     "fig, axes = plt.subplots(4, 4, figsize=(17, 13), sharex=True, sharey=True, layout=\"constrained\")\n",
+                                     "for axis, series_id in zip(axes.flat, analysis_series_ids, strict=False):\n",
+                                     "    subset = lag_correlations.query(\"series_id == @series_id and outcome == \u0027target\u0027\")\n",
+                                     "    axis.plot(subset[\"lag_months\"], subset[\"pearson_correlation\"], color=\"#2a6f97\")\n",
+                                     "    axis.axvline(0, color=\"black\", linewidth=0.8)\n",
+                                     "    axis.axvline(3, color=\"#b22222\", linestyle=\"--\", linewidth=0.8)\n",
+                                     "    axis.axhline(0, color=\"black\", linewidth=0.6)\n",
+                                     "    axis.set_title(series_id, fontsize=9)\n",
+                                     "    axis.grid(alpha=0.2)\n",
+                                     "for axis in axes.flat[len(analysis_series_ids) :]:\n",
+                                     "    axis.set_visible(False)\n",
+                                     "fig.supxlabel(\"Lag in months: feature at t versus target at t + lag\")\n",
+                                     "fig.supylabel(\"Pearson correlation\")\n",
+                                     "fig.suptitle(\"Feature-to-future-stress cross-correlations; dashed line marks +3 months\")\n",
+                                     "plt.show()"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "4b797351",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "4b797351"
+                                   },
+                      "source":  [
+                                     "## Small-sample robustness check\n",
+                                     "\n",
+                                     "Only 20 of 320 observed months are labeled stress, so a target correlation can be dominated by a handful of episodes. For the top non-mechanical candidates (excluding the IPMAN-derived features, which are related to the target by construction), this section reports a bootstrap confidence interval on the point-biserial correlation and a leave-one-stress-month-out sensitivity range. A wide bootstrap interval, a sign flip under leave-one-out, or a large leave-one-out swing all indicate the descriptive correlation is fragile and should not be treated as a strong feature-selection signal on its own."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  35,
+                      "id":  "78aaceab",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "78aaceab"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "data":  {
+                                                       "text/html":  [
+                                                                         "\u003cdiv\u003e\n",
+                                                                         "\u003cstyle scoped\u003e\n",
+                                                                         "    .dataframe tbody tr th:only-of-type {\n",
+                                                                         "        vertical-align: middle;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe tbody tr th {\n",
+                                                                         "        vertical-align: top;\n",
+                                                                         "    }\n",
+                                                                         "\n",
+                                                                         "    .dataframe thead th {\n",
+                                                                         "        text-align: right;\n",
+                                                                         "    }\n",
+                                                                         "\u003c/style\u003e\n",
+                                                                         "\u003ctable border=\"1\" class=\"dataframe\"\u003e\n",
+                                                                         "  \u003cthead\u003e\n",
+                                                                         "    \u003ctr style=\"text-align: right;\"\u003e\n",
+                                                                         "      \u003cth\u003e\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eseries_id\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003epoint_estimate\u003c/th\u003e\n",
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+                                                                         "      \u003cth\u003ebootstrap_ci_high\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eloo_min\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eloo_max\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003eloo_swing\u003c/th\u003e\n",
+                                                                         "      \u003cth\u003esign_stable_under_loo\u003c/th\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/thead\u003e\n",
+                                                                         "  \u003ctbody\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e4\u003c/th\u003e\n",
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+                                                                         "      \u003cth\u003e5\u003c/th\u003e\n",
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+                                                                         "      \u003ctd\u003eTrue\u003c/td\u003e\n",
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+                                                                         "      \u003cth\u003e3\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eICSA\u003c/td\u003e\n",
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+                                                                         "      \u003cth\u003e0\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eSPY_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.449525\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.567107\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003eTrue\u003c/td\u003e\n",
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+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e2\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.439082\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.546194\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003e-0.418670\u003c/td\u003e\n",
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+                                                                         "      \u003ctd\u003eTrue\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "        series_id  point_estimate  bootstrap_ci_low  bootstrap_ci_high  \\\n",
+                                                                          "4   XLI_RETURN_3M       -0.231133         -0.418891          -0.030867   \n",
+                                                                          "5          UNRATE        0.224800          0.066512           0.372334   \n",
+                                                                          "3            ICSA        0.385829          0.280484           0.521131   \n",
+                                                                          "1          VIXCLS        0.445400          0.289699           0.574836   \n",
+                                                                          "0  SPY_RETURN_12M       -0.449525         -0.567107          -0.313494   \n",
+                                                                          "2  XLI_RETURN_12M       -0.439082         -0.546194          -0.307979   \n",
+                                                                          "\n",
+                                                                          "    loo_min   loo_max  loo_swing  sign_stable_under_loo  \n",
+                                                                          "4 -0.277111 -0.196221   0.080891                   True  \n",
+                                                                          "5  0.184391  0.241097   0.056707                   True  \n",
+                                                                          "3  0.343546  0.398020   0.054474                   True  \n",
+                                                                          "1  0.415781  0.462989   0.047208                   True  \n",
+                                                                          "0 -0.472618 -0.430189   0.042429                   True  \n",
+                                                                          "2 -0.456705 -0.418670   0.038035                   True  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  35,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "N_BOOTSTRAP = 2000\n",
+                                     "robustness_rng = np.random.default_rng(0)\n",
+                                     "\n",
+                                     "\n",
+                                     "def point_biserial(paired: pd.DataFrame) -\u003e float:\n",
+                                     "    return paired.corr(method=\"pearson\").iloc[0, 1]\n",
+                                     "\n",
+                                     "\n",
+                                     "robustness_candidates = (\n",
+                                     "    target_associations.query(\"series_id not in @IPMAN_FEATURE_SERIES_IDS and stress_months \u003e 0\")\n",
+                                     "    .head(6)[\"series_id\"]\n",
+                                     "    .tolist()\n",
+                                     ")\n",
+                                     "\n",
+                                     "robustness_rows = []\n",
+                                     "for series_id in robustness_candidates:\n",
+                                     "    paired = analysis_panel[[series_id, \"target\"]].dropna()\n",
+                                     "    point_estimate = point_biserial(paired)\n",
+                                     "\n",
+                                     "    bootstrap_estimates = np.array(\n",
+                                     "        [\n",
+                                     "            point_biserial(paired.sample(n=len(paired), replace=True, random_state=robustness_rng.integers(0, 1_000_000)))\n",
+                                     "            for _ in range(N_BOOTSTRAP)\n",
+                                     "        ]\n",
+                                     "    )\n",
+                                     "\n",
+                                     "    stress_timestamps = paired.index[paired[\"target\"] == 1]\n",
+                                     "    loo_estimates = np.array(\n",
+                                     "        [point_biserial(paired.drop(index=stress_timestamp)) for stress_timestamp in stress_timestamps]\n",
+                                     "    )\n",
+                                     "\n",
+                                     "    robustness_rows.append(\n",
+                                     "        {\n",
+                                     "            \"series_id\": series_id,\n",
+                                     "            \"point_estimate\": point_estimate,\n",
+                                     "            \"bootstrap_ci_low\": np.percentile(bootstrap_estimates, 2.5),\n",
+                                     "            \"bootstrap_ci_high\": np.percentile(bootstrap_estimates, 97.5),\n",
+                                     "            \"loo_min\": loo_estimates.min(),\n",
+                                     "            \"loo_max\": loo_estimates.max(),\n",
+                                     "            \"loo_swing\": loo_estimates.max() - loo_estimates.min(),\n",
+                                     "            \"sign_stable_under_loo\": bool(np.all(np.sign(loo_estimates) == np.sign(point_estimate))),\n",
+                                     "        }\n",
+                                     "    )\n",
+                                     "\n",
+                                     "robustness_summary = pd.DataFrame(robustness_rows).sort_values(\"loo_swing\", ascending=False)\n",
+                                     "robustness_summary\n"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "a0cf9692",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "a0cf9692"
+                                   },
+                      "source":  [
+                                     "## VIF-screened five-variable shortlist\n",
+                                     "\n",
+                                     "The pairwise associations above identify promising candidates, but correlated predictors can make coefficient estimates unstable. This section screens the candidate variables with variance inflation factors (VIF), then selects five variables from distinct signal groups while preserving strong 1.5% target associations. VIF is descriptive here; the final choice still requires cutoff-safe backtesting."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  44,
+                      "id":  "028b14b5",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "028b14b5"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "name":  "stdout",
+                                          "output_type":  "stream",
+                                          "text":  [
+                                                       "Complete VIF sample: 319 months\n",
+                                                       "VIF guide: below 5 is preferable; 5-10 is moderate/high; above 10 is serious.\n",
+                                                       "\n",
+                                                       "Best five-variable combination by lowest maximum VIF, then strongest aggregate target association:\n",
+                                                       "  - ipman_change_1m_pct\n",
+                                                       "  - ipman_change_3m_pct\n",
+                                                       "  - ipman_change_6m_pct\n",
+                                                       "  - XLI_RETURN_12M\n",
+                                                       "  - UNRATE\n",
+                                                       "\n",
+                                                       "VIF for selected combination:\n",
+                                                       "\n",
+                                                       "Conclusion: The selected five-variable shortlist has acceptable pairwise multicollinearity under the VIF \u003c 5 rule.\n"
+                                                   ]
+                                      }
+                                  ],
+                      "source":  [
+                                     "from itertools import combinations\n",
+                                     "\n",
+                                     "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+                                     "from manufacturing_stress_forecasting.features import (\n",
+                                     "    ICSA_SERIES_ID,\n",
+                                     "    IPMAN_CHANGE_1M_SERIES_ID,\n",
+                                     "    IPMAN_CHANGE_3M_SERIES_ID,\n",
+                                     "    IPMAN_CHANGE_6M_SERIES_ID,\n",
+                                     "    SPY_RETURN_12M_SERIES_ID,\n",
+                                     "    UNRATE_SERIES_ID,\n",
+                                     "    VIXCLS_SERIES_ID,\n",
+                                     "    XLI_RETURN_12M_SERIES_ID,\n",
+                                     ")\n",
+                                     "\n",
+                                     "\n",
+                                     "candidate_variables = [\n",
+                                     "    IPMAN_CHANGE_1M_SERIES_ID,\n",
+                                     "    IPMAN_CHANGE_3M_SERIES_ID,\n",
+                                     "    IPMAN_CHANGE_6M_SERIES_ID,\n",
+                                     "    VIXCLS_SERIES_ID,\n",
+                                     "    ICSA_SERIES_ID,\n",
+                                     "    SPY_RETURN_12M_SERIES_ID,\n",
+                                     "    XLI_RETURN_12M_SERIES_ID,\n",
+                                     "    UNRATE_SERIES_ID,\n",
+                                     "]\n",
+                                     "vif_panel = analysis_panel[candidate_variables].dropna().copy()\n",
+                                     "\n",
+                                     "\n",
+                                     "def calculate_vif(series_ids: tuple[str, ...], frame: pd.DataFrame = vif_panel) -\u003e pd.DataFrame:\n",
+                                     "    values = frame[list(series_ids)].to_numpy()\n",
+                                     "    return pd.DataFrame(\n",
+                                     "        {\n",
+                                     "            \"series_id\": series_ids,\n",
+                                     "            \"vif\": [variance_inflation_factor(values, index) for index in range(values.shape[1])],\n",
+                                     "        }\n",
+                                     "    )\n",
+                                     "\n",
+                                     "\n",
+                                     "vif_values = calculate_vif(tuple(candidate_variables)).sort_values(\"vif\", ascending=False)\n",
+                                     "candidate_associations = (\n",
+                                     "    target_associations.set_index(\"series_id\")\n",
+                                     "    .loc[candidate_variables, [\"pearson_with_target\", \"spearman_with_target\"]]\n",
+                                     "    .rename(columns={\"pearson_with_target\": \"target_pearson\", \"spearman_with_target\": \"target_spearman\"})\n",
+                                     ")\n",
+                                     "vif_summary = (\n",
+                                     "    vif_values.set_index(\"series_id\")\n",
+                                     "    .join(candidate_associations)\n",
+                                     "    .sort_values(\"target_pearson\", key=np.abs, ascending=False)\n",
+                                     ")\n",
+                                     "\n",
+                                     "print(f\"Complete VIF sample: {len(vif_panel)} months\")\n",
+                                     "print(\"VIF guide: below 5 is preferable; 5-10 is moderate/high; above 10 is serious.\")\n",
+                                     "vif_summary\n",
+                                     "\n",
+                                     "combination_rows = []\n",
+                                     "for combination in combinations(candidate_variables, 5):\n",
+                                     "    combination_vif = calculate_vif(combination)\n",
+                                     "    combination_rows.append(\n",
+                                     "        {\n",
+                                     "            \"variables\": combination,\n",
+                                     "            \"max_vif\": combination_vif[\"vif\"].max(),\n",
+                                     "            \"mean_vif\": combination_vif[\"vif\"].mean(),\n",
+                                     "            \"target_signal_sum\": sum(abs(candidate_associations.loc[series_id, \"target_pearson\"]) for series_id in combination),\n",
+                                     "        }\n",
+                                     "    )\n",
+                                     "combination_summary = pd.DataFrame(combination_rows).sort_values(\n",
+                                     "    [\"max_vif\", \"target_signal_sum\"], ascending=[True, False]\n",
+                                     ")\n",
+                                     "\n",
+                                     "best_combination = tuple(combination_summary.iloc[0][\"variables\"])\n",
+                                     "best_vif = calculate_vif(best_combination).sort_values(\"vif\", ascending=False)\n",
+                                     "print(\"\\nBest five-variable combination by lowest maximum VIF, then strongest aggregate target association:\")\n",
+                                     "for series_id in best_combination:\n",
+                                     "    print(f\"  - {series_id}\")\n",
+                                     "print(\"\\nVIF for selected combination:\")\n",
+                                     "best_vif\n",
+                                     "\n",
+                                     "if best_vif[\"vif\"].max() \u003c 5:\n",
+                                     "    conclusion = \"The selected five-variable shortlist has acceptable pairwise multicollinearity under the VIF \u003c 5 rule.\"\n",
+                                     "elif best_vif[\"vif\"].max() \u003c 10:\n",
+                                     "    conclusion = \"The selected shortlist is usable but still has moderate/high multicollinearity; validate it against reduced alternatives in the backtest.\"\n",
+                                     "else:\n",
+                                     "    conclusion = \"No five-variable combination clears the VIF \u003c 10 screen; use a smaller shortlist or remove correlated variables.\"\n",
+                                     "print(f\"\\nConclusion: {conclusion}\")"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "3e9eceec",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "3e9eceec"
+                                   },
+                      "source":  [
+                                     "## Point-in-time feature availability\n",
+                                     "\n",
+                                     "The preceding descriptive panel aligns reference months. A forecasting origin must instead see only records whose `released_at` is on or before that origin. This reproduces the fit-at-origin feature lookup used by the statistical predictors."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  21,
+                      "id":  "ce25236d",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "ce25236d"
+                                   },
+                      "outputs":  [
+                                      {
+                                          "name":  "stdout",
+                                          "output_type":  "stream",
+                                          "text":  [
+                                                       "Latest complete active-feature snapshot: 2026-09-01\n"
+                                                   ]
+                                      },
+                                      {
+                                          "data":  {
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+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2.630000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e8\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eICSA\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-31\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e207000.000000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e12\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eSPY_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e20.230850\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e11\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eSPY_RETURN_3M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.658482\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e7\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eUNRATE\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-03\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e4.100000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e9\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eVIXCLS\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e14.920000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e14\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_12M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e16.694316\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e13\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eXLI_RETURN_3M\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.404076\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e4\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eYC_SPREAD\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.410000\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e0\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_1m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e-0.300618\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e1\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_3m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e0.118004\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "    \u003ctr\u003e\n",
+                                                                         "      \u003cth\u003e2\u003c/th\u003e\n",
+                                                                         "      \u003ctd\u003eipman_change_6m_pct\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-08-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e2026-09-01\u003c/td\u003e\n",
+                                                                         "      \u003ctd\u003e1.395025\u003c/td\u003e\n",
+                                                                         "    \u003c/tr\u003e\n",
+                                                                         "  \u003c/tbody\u003e\n",
+                                                                         "\u003c/table\u003e\n",
+                                                                         "\u003c/div\u003e"
+                                                                     ],
+                                                       "text/plain":  [
+                                                                          "              series_id     origin latest_visible_reference_month  \\\n",
+                                                                          "5              CPIAUCSL 2026-09-01                     2026-08-01   \n",
+                                                                          "6               CPI_YOY 2026-09-01                     2026-08-01   \n",
+                                                                          "3              FEDFUNDS 2026-09-01                     2026-08-01   \n",
+                                                                          "10            HY_SPREAD 2026-09-01                     2026-08-01   \n",
+                                                                          "8                  ICSA 2026-09-01                     2026-08-01   \n",
+                                                                          "12       SPY_RETURN_12M 2026-09-01                     2026-08-01   \n",
+                                                                          "11        SPY_RETURN_3M 2026-09-01                     2026-08-01   \n",
+                                                                          "7                UNRATE 2026-09-01                     2026-08-01   \n",
+                                                                          "9                VIXCLS 2026-09-01                     2026-08-01   \n",
+                                                                          "14       XLI_RETURN_12M 2026-09-01                     2026-08-01   \n",
+                                                                          "13        XLI_RETURN_3M 2026-09-01                     2026-08-01   \n",
+                                                                          "4             YC_SPREAD 2026-09-01                     2026-08-01   \n",
+                                                                          "0   ipman_change_1m_pct 2026-09-01                     2026-08-01   \n",
+                                                                          "1   ipman_change_3m_pct 2026-09-01                     2026-08-01   \n",
+                                                                          "2   ipman_change_6m_pct 2026-09-01                     2026-08-01   \n",
+                                                                          "\n",
+                                                                          "   latest_visible_release_date  snapshot_value  \n",
+                                                                          "5                   2026-08-03      334.131000  \n",
+                                                                          "6                   2026-08-03        3.712958  \n",
+                                                                          "3                   2026-08-03        3.630000  \n",
+                                                                          "10                  2026-09-01        2.630000  \n",
+                                                                          "8                   2026-08-31   207000.000000  \n",
+                                                                          "12                  2026-09-01       20.230850  \n",
+                                                                          "11                  2026-09-01        1.658482  \n",
+                                                                          "7                   2026-08-03        4.100000  \n",
+                                                                          "9                   2026-09-01       14.920000  \n",
+                                                                          "14                  2026-09-01       16.694316  \n",
+                                                                          "13                  2026-09-01        1.404076  \n",
+                                                                          "4                   2026-09-01        0.410000  \n",
+                                                                          "0                   2026-09-01       -0.300618  \n",
+                                                                          "1                   2026-09-01        0.118004  \n",
+                                                                          "2                   2026-09-01        1.395025  "
+                                                                      ]
+                                                   },
+                                          "execution_count":  21,
+                                          "metadata":  {
+
+                                                       },
+                                          "output_type":  "execute_result"
+                                      }
+                                  ],
+                      "source":  [
+                                     "feature_frames = {series_id: series_frames[series_id] for series_id in FEATURE_SERIES_IDS}\n",
+                                     "candidate_origins = analysis_panel.index[analysis_panel.index \u003e= ANALYSIS_START_TS]\n",
+                                     "example_origin = None\n",
+                                     "snapshot = None\n",
+                                     "for candidate_origin in reversed(candidate_origins):\n",
+                                     "    candidate_snapshot = build_feature_snapshot(candidate_origin, feature_frames)\n",
+                                     "    if candidate_snapshot is not None:\n",
+                                     "        example_origin = candidate_origin\n",
+                                     "        snapshot = candidate_snapshot\n",
+                                     "        break\n",
+                                     "if example_origin is None or snapshot is None:\n",
+                                     "    raise RuntimeError(\"No complete cutoff-safe feature snapshot is available in the analysis window.\")\n",
+                                     "\n",
+                                     "print(f\"Latest complete active-feature snapshot: {example_origin.date()}\")\n",
+                                     "availability_audit = []\n",
+                                     "for series_id in FEATURE_SERIES_IDS:\n",
+                                     "    visible = feature_frames[series_id].query(\"released_at \u003c= @example_origin\").sort_values(\"timestamp\")\n",
+                                     "    latest = visible.iloc[-1]\n",
+                                     "    availability_audit.append(\n",
+                                     "        {\n",
+                                     "            \"series_id\": series_id,\n",
+                                     "            \"origin\": example_origin,\n",
+                                     "            \"latest_visible_reference_month\": latest[\"timestamp\"],\n",
+                                     "            \"latest_visible_release_date\": latest[\"released_at\"],\n",
+                                     "            \"snapshot_value\": snapshot[series_id],\n",
+                                     "        }\n",
+                                     "    )\n",
+                                     "pd.DataFrame(availability_audit).sort_values(\"series_id\")"
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "bc38a96b",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "bc38a96b"
+                                   },
+                      "source":  [
+                                     "## Interpretation checklist\n",
+                                     "\n",
+                                     "Use the executed tables and charts to record observations here. Keep the distinction between descriptive evidence and forecast evidence intact.\n",
+                                     "\n",
+                                     "- Coverage and quality: identify sparse starts, gaps, and unusual release delays.\n",
+                                     "- Target: assess whether the $-1.5\\%$ three-month IPMAN threshold captures the intended material-contraction episodes.\n",
+                                     "- Contemporaneous relationships: note co-movements that are economically plausible, but do not call them causal.\n",
+                                     "- Leading candidates: inspect positive lags, especially $+3$ months, then validate any candidate with a cutoff-safe backtest.\n",
+                                     "- Small-sample robustness: interpret bootstrap and leave-one-stress-month-out results relative to the 1.5% target\u0027s event count.\n",
+                                     "- Caveats: FRED uses current revisions rather than full ALFRED vintages; GSCPI uses the latest revision-table column; overlapping trailing returns and small stress-event counts can exaggerate apparent patterns."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "markdown",
+                      "id":  "6ee87b8a",
+                      "metadata":  {
+                                       "language":  "markdown",
+                                       "id":  "6ee87b8a"
+                                   },
+                      "source":  [
+                                     "## Current model-input inventory\n",
+                                     "\n",
+                                     "This section mirrors the variables used by the current manufacturing-stress predictors. The model consumes 15 monthly, cutoff-aware features: three trailing IPMAN changes and twelve macro/market inputs. This notebook retains the historical 1.5% material-stress target for comparison, while the current production target uses the default three-month IPMAN stress definition. Feature observations use the conservative one-month release lag configured by the service builder."
+                                 ]
+                  },
+                  {
+                      "cell_type":  "code",
+                      "execution_count":  null,
+                      "id":  "c47ec299",
+                      "metadata":  {
+                                       "language":  "python",
+                                       "id":  "c47ec299"
+                                   },
+                      "outputs":  [
+
+                                  ],
+                      "source":  [
+                                     "from manufacturing_stress_forecasting.features import (\n",
+                                     "    FEATURE_SERIES_IDS,\n",
+                                     "    IPMAN_FEATURE_SERIES_IDS,\n",
+                                     "    MACRO_FEATURE_SERIES_IDS,\n",
+                                     "    FEATURE_PERIODS,\n",
+                                     ")\n",
+                                     "from manufacturing_stress_forecasting.targets import (\n",
+                                     "    DEFAULT_LOOKBACK_MONTHS,\n",
+                                     "    DEFAULT_STRESS_THRESHOLD_PCT,\n",
+                                     ")\n",
+                                     "\n",
+                                     "model_input_rows = []\n",
+                                     "for series_id in IPMAN_FEATURE_SERIES_IDS:\n",
+                                     "    model_input_rows.append({\n",
+                                     "        \"series_id\": series_id,\n",
+                                     "        \"family\": \"IPMAN momentum\",\n",
+                                     "        \"transformation\": f\"trailing {FEATURE_PERIODS[series_id]}-month percent change\",\n",
+                                     "    })\n",
+                                     "for series_id in MACRO_FEATURE_SERIES_IDS:\n",
+                                     "    model_input_rows.append({\n",
+                                     "        \"series_id\": series_id,\n",
+                                     "        \"family\": \"macro/market\",\n",
+                                     "        \"transformation\": \"monthly level or trailing return from data service\",\n",
+                                     "    })\n",
+                                     "\n",
+                                     "model_input_inventory = pd.DataFrame(model_input_rows)\n",
+                                     "print(f\"Model features: {len(FEATURE_SERIES_IDS)}\")\n",
+                                     "print(f\"Current target lookback: {DEFAULT_LOOKBACK_MONTHS} months\")\n",
+                                     "print(f\"Current target threshold: {DEFAULT_STRESS_THRESHOLD_PCT}%\")\n",
+                                     "print(\"This notebook\u0027s historical 1.5% material-stress target remains separate.\")\n",
+                                     "model_input_inventory"
+                                 ]
+                  }
+              ],
+    "metadata":  {
+                     "kernelspec":  {
+                                        "display_name":  "agentic-forecasting-bootcamp (3.12.14.final.0)",
+                                        "language":  "python",
+                                        "name":  "python3"
+                                    },
+                     "language_info":  {
+                                           "codemirror_mode":  {
+                                                                   "name":  "ipython",
+                                                                   "version":  3
+                                                               },
+                                           "file_extension":  ".py",
+                                           "mimetype":  "text/x-python",
+                                           "name":  "python",
+                                           "nbconvert_exporter":  "python",
+                                           "pygments_lexer":  "ipython3",
+                                           "version":  "3.12.14"
+                                       }
+                 },
+    "nbformat":  4,
+    "nbformat_minor":  5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_hybrid_workbench.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_hybrid_workbench.ipynb
new file mode 100644
index 000000000..cac7828ff
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_hybrid_workbench.ipynb
@@ -0,0 +1,272 @@
+{
+          "cells": [
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "5dd48d79",
+                              "metadata": {
+                                        "id": "6c094247",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "Running: uv sync --all-extras --dev --all-packages\n",
+                                                            "\n",
+                                                            "Environment synchronized successfully.\n",
+                                                            "If packages changed, restart the notebook kernel before continuing.\n"
+                                                  ]
+                                        }
+                              ],
+                              "source": [
+                                        "import json\n",
+                                        "import os\n",
+                                        "import sys\n",
+                                        "from pathlib import Path\n",
+                                        "\n",
+                                        "for package_root in (SETUP_ROOT / \"aieng-forecasting\", SETUP_ROOT / \"implementations\"):\n",
+                                        "    if str(package_root) not in sys.path:\n",
+                                        "        sys.path.insert(0, str(package_root))\n",
+                                        "\n",
+                                        "import pandas as pd\n",
+                                        "import yaml\n",
+                                        "from aieng.forecasting.evaluation import BacktestSpec\n",
+                                        "from dotenv import load_dotenv\n",
+                                        "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+                                        "from manufacturing_stress_forecasting.hybrid_agent import (\n",
+                                        "    HybridAgentPredictor,\n",
+                                        "    build_hybrid_agent_predictor,\n",
+                                        "    build_numerical_anchor,\n",
+                                        "    validate_agent_adjustment,\n",
+                                        ")\n",
+                                        "\n",
+                                        "REPO_ROOT = SETUP_ROOT\n",
+                                        "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+                                        "SPEC_PATH = REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"specs\" / \"manufacturing_stress_smoke.yaml\"\n",
+                                        "print(f\"Repository: {REPO_ROOT}\")\n",
+                                        "print(f\"FRED API key loaded: {bool(os.getenv('FRED_API_KEY'))}\")"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "f6ed68c7",
+                              "metadata": {},
+                              "source": [
+                                        "# Manufacturing Stress Hybrid Workbench\n",
+                                        "\n",
+                                        "This is an additive interface for the initial hybrid expansion. It uses the full 15-feature set, the fixed logistic and XGBoost candidates, and the existing ADK/Vector-proxy agent path. The current parameter-sweep and quantitative-only agent workflows remain separate.\n",
+                                        "\n",
+                                        "The default is a dry run: it builds the cutoff-safe numerical anchor and validates the adjustment contract without making an LLM call. Set `RUN_AGENT_CALL = True` only when you explicitly want one agent request. The bounded agent adjustment then becomes the forecast probability."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "5cc22219",
+                              "metadata": {},
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "Repository: /home/coder/BMO-C2-agentic-forecasting-1\n",
+                                                            "FRED API key loaded: True\n"
+                                                  ]
+                                        }
+                              ],
+                              "source": [
+                                        "import json\n",
+                                        "import os\n",
+                                        "import sys\n",
+                                        "from pathlib import Path\n",
+                                        "\n",
+                                        "for package_root in (SETUP_ROOT / \"aieng-forecasting\", SETUP_ROOT / \"implementations\"):\n",
+                                        "    if str(package_root) not in sys.path:\n",
+                                        "        sys.path.insert(0, str(package_root))\n",
+                                        "\n",
+                                        "import pandas as pd\n",
+                                        "import yaml\n",
+                                        "from aieng.forecasting.evaluation import BacktestSpec\n",
+                                        "from dotenv import load_dotenv\n",
+                                        "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+                                        "from manufacturing_stress_forecasting.hybrid import (\n",
+                                        "    HybridAgentPredictor,\n",
+                                        "    build_hybrid_agent_predictor,\n",
+                                        "    build_numerical_anchor,\n",
+                                        "    validate_agent_adjustment,\n",
+                                        ")\n",
+                                        "\n",
+                                        "REPO_ROOT = SETUP_ROOT\n",
+                                        "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+                                        "SPEC_PATH = REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"specs\" / \"manufacturing_stress_smoke.yaml\"\n",
+                                        "print(f\"Repository: {REPO_ROOT}\")\n",
+                                        "print(f\"FRED API key loaded: {bool(os.getenv('FRED_API_KEY'))}\")"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "5fd55fdd",
+                              "metadata": {
+                                        "id": "93f12d8a",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Controls\n",
+                                        "\n",
+                                        "The fixed target is `manufacturing_stress`: a trailing three-month IPMAN decline of at least 2%, forecast three months ahead. The two statistical candidates use all 15 active features."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 5,
+                              "id": "f6f8b492",
+                              "metadata": {
+                                        "id": "3828de2f",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "{'as_of': '2017-01-01', 'refresh_input_data': False, 'run_agent_call': True, 'max_agent_adjustment': 0.03, 'feature_set': 'full_15', 'candidates': ['logistic_c_0_001', 'xgb_50_depth2_lr0_03']}\n"
+                                                  ]
+                                        }
+                              ],
+                              "source": [
+                                        "AS_OF = '2017-01-01'\n",
+                                        "REFRESH_INPUT_DATA = False\n",
+                                        "RUN_AGENT_CALL = True\n",
+                                        "MAX_AGENT_ADJUSTMENT = 0.03\n",
+                                        "\n",
+                                        "print({\n",
+                                        "    'as_of': AS_OF,\n",
+                                        "    'refresh_input_data': REFRESH_INPUT_DATA,\n",
+                                        "    'run_agent_call': RUN_AGENT_CALL,\n",
+                                        "    'max_agent_adjustment': MAX_AGENT_ADJUSTMENT,\n",
+                                        "    'feature_set': 'full_15',\n",
+                                        "    'candidates': ['logistic_c_0_001', 'xgb_50_depth2_lr0_03'],\n",
+                                        "})"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "50fdafc4",
+                              "metadata": {
+                                        "id": "b9cff5b3",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Load input data\n",
+                                        "\n",
+                                        "The next cell builds the cutoff-aware data service from the repository cache. With `REFRESH_INPUT_DATA = False`, it uses cached FRED and Yahoo Finance files. Set it to `True` only when you intentionally want to refresh the source data; a valid `FRED_API_KEY` is required for FRED cache misses or refreshes."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": 6,
+                              "id": "4d2f652c",
+                              "metadata": {
+                                        "id": "1253f354",
+                                        "language": "python"
+                              },
+                              "outputs": [
+                                        {
+                                                  "name": "stdout",
+                                                  "output_type": "stream",
+                                                  "text": [
+                                                            "{'target': 'manufacturing_stress', 'threshold': '-2%', 'horizon_months': 3, 'logistic_probability': 0.06635727426143465, 'xgboost_probability': 0.019824787974357605, 'anchor_probability': 0.043091031117896125, 'validation_status': 'passed'}\n"
+                                                  ]
+                                        }
+                              ],
+                              "source": [
+                                        "with SPEC_PATH.open() as file:\n",
+                                        "    spec = BacktestSpec.model_validate(yaml.safe_load(file))\n",
+                                        "\n",
+                                        "service = build_manufacturing_stress_service(\n",
+                                        "    cache_dir=REPO_ROOT / 'data' / 'fred',\n",
+                                        "    yahoo_cache_dir=REPO_ROOT / 'data' / 'yfinance',\n",
+                                        "    refresh=REFRESH_INPUT_DATA,\n",
+                                        ")\n",
+                                        "context = service.context(as_of=pd.Timestamp(AS_OF).to_pydatetime())\n",
+                                        "anchor = build_numerical_anchor(spec.task, context)\n",
+                                        "validation = validate_agent_adjustment(anchor.probability, anchor.probability, max_adjustment=MAX_AGENT_ADJUSTMENT)\n",
+                                        "\n",
+                                        "print({\n",
+                                        "    'target': spec.task.target_series_id,\n",
+                                        "    'threshold': '-2%',\n",
+                                        "    'horizon_months': spec.task.horizons[0],\n",
+                                        "    'logistic_probability': anchor.logistic_probability,\n",
+                                        "    'xgboost_probability': anchor.xgboost_probability,\n",
+                                        "    'anchor_probability': anchor.probability,\n",
+                                        "    'validation_status': validation['validation_status'],\n",
+                                        "})"
+                              ]
+                    },
+                    {
+                              "cell_type": "markdown",
+                              "id": "6dc6211a",
+                              "metadata": {
+                                        "id": "68bb1d49",
+                                        "language": "markdown"
+                              },
+                              "source": [
+                                        "## Optional LLM agent forecast\n",
+                                        "\n",
+                                        "This cell uses the existing manufacturing-stress ADK runner and Vector-proxy configuration. The JSON result includes a rationale capped at 40 words, the main supporting and countervailing evidence, and the overall direction. The numerical anchor is the starting point; the bounded agent adjustment is used as the final forecast probability."
+                              ]
+                    },
+                    {
+                              "cell_type": "code",
+                              "execution_count": null,
+                              "id": "d240023b",
+                              "metadata": {
+                                        "id": "8a9ae2f4",
+                                        "language": "python"
+                              },
+                              "outputs": [],
+                              "source": [
+                                        "if RUN_AGENT_CALL:\n",
+                                        "    hybrid_predictor = HybridAgentPredictor(build_hybrid_agent_predictor())\n",
+                                        "    prediction = hybrid_predictor.predict(spec.task, context)[0]\n",
+                                        "    print(json.dumps({\n",
+                                        "        'forecast_probability': prediction.payload.probability,\n",
+                                        "        'anchor_probability': prediction.metadata['anchor_probability'],\n",
+                                        "        'proposed_probability': prediction.metadata['agent_probability'],\n",
+                                        "        'adjusted_probability': prediction.metadata['adjusted_probability'],\n",
+                                        "        'agent_adjustment': prediction.metadata['agent_adjustment'],\n",
+                                        "        'rationale': prediction.metadata['rationale'],\n",
+                                        "        'supporting_evidence': prediction.metadata['supporting_evidence'],\n",
+                                        "        'countervailing_evidence': prediction.metadata['countervailing_evidence'],\n",
+                                        "        'direction': prediction.metadata['direction'],\n",
+                                        "        'validation_status': prediction.metadata['validation_status'],\n",
+                                        "    }, indent=2))\n",
+                                        "else:\n",
+                                        "    print('LLM call skipped; dry-run mode is active.')"
+                              ]
+                    }
+          ],
+          "metadata": {
+                    "kernelspec": {
+                              "display_name": "agentic-forecasting-bootcamp (3.12.3)",
+                              "language": "python",
+                              "name": "python3"
+                    },
+                    "language_info": {
+                              "codemirror_mode": {
+                                        "name": "ipython",
+                                        "version": 3
+                              },
+                              "file_extension": ".py",
+                              "mimetype": "text/x-python",
+                              "name": "python",
+                              "nbconvert_exporter": "python",
+                              "pygments_lexer": "ipython3",
+                              "version": "3.12.3"
+                    }
+          },
+          "nbformat": 4,
+          "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_parameter_sweep_workbench.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_parameter_sweep_workbench.ipynb
new file mode 100644
index 000000000..159fb4d8f
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_parameter_sweep_workbench.ipynb
@@ -0,0 +1,778 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f1b13f2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running: uv sync --all-extras --dev --all-packages\n",
+      "\n",
+      "Environment synchronized successfully.\n",
+      "If packages changed, restart the notebook kernel before continuing.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cell 0: synchronize the repository environment before running the workbench.\n",
+    "import subprocess\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "def find_repo_root(start: Path) -> Path:\n",
+    "    \"\"\"Find the workspace root without confusing member pyproject files for it.\"\"\"\n",
+    "    for candidate in (start, *start.parents):\n",
+    "        if (\n",
+    "            (candidate / \"uv.lock\").exists()\n",
+    "            and (candidate / \"implementations\").is_dir()\n",
+    "            and (candidate / \"aieng-forecasting\").is_dir()\n",
+    "        ):\n",
+    "            return candidate\n",
+    "    raise FileNotFoundError(\"Could not find the agentic-forecasting repository root.\")\n",
+    "\n",
+    "\n",
+    "SETUP_ROOT = find_repo_root(Path.cwd().resolve())\n",
+    "print(\"Running: uv sync --all-extras --dev --all-packages\")\n",
+    "setup_result = subprocess.run(\n",
+    "    [\"uv\", \"sync\", \"--all-extras\", \"--dev\", \"--all-packages\"],\n",
+    "    cwd=SETUP_ROOT,\n",
+    "    check=False,\n",
+    "    text=True,\n",
+    "    capture_output=True,\n",
+    ")\n",
+    "print(setup_result.stdout)\n",
+    "if setup_result.returncode != 0:\n",
+    "    print(setup_result.stderr)\n",
+    "    raise RuntimeError(\"uv sync failed. Install uv or restart the notebook with the repository environment selected.\")\n",
+    "print(\"Environment synchronized successfully.\")\n",
+    "print(\"If packages changed, restart the notebook kernel before continuing.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e90728ca",
+   "metadata": {},
+   "source": [
+    "# Manufacturing Stress Parameter Sweep Workbench\n",
+    "\n",
+    "This notebook runs the same deterministic workflow as `run_parameter_smoke.py`, directly through its Python functions. It makes no LLM calls and does not use prediction-result caches.\n",
+    "\n",
+    "The workflow deliberately has two stages:\n",
+    "\n",
+    "1. **Tune** all candidates over 2000–2017.\n",
+    "2. **Confirm** only the selected tuning winner over 2018–2024.\n",
+    "\n",
+    "Do not use the confirmation window to choose a model; doing so would turn it into another tuning set.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "79b2d5db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'stage': 'confirm', 'candidate': 'logistic_c_1_0', 'backtest_stride': 3, 'refresh_input_data': False, 'run_sweep': True}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Main controls: change these values, then run the notebook from top to bottom.\n",
+    "#STAGE = \"tune\"\n",
+    "STAGE = \"confirm\"\n",
+    "#CANDIDATE = None\n",
+    "CANDIDATE = \"logistic_c_1_0\"\n",
+    "BACKTEST_STRIDE = 3\n",
+    "REFRESH_INPUT_DATA = False\n",
+    "RUN_SWEEP = True\n",
+    "\n",
+    "# For confirmation, set STAGE = \"confirm\" and CANDIDATE to the winner\n",
+    "# printed by the tuning stage. Keep BACKTEST_STRIDE unchanged.\n",
+    "print(\n",
+    "    {\n",
+    "        \"stage\": STAGE,\n",
+    "        \"candidate\": CANDIDATE,\n",
+    "        \"backtest_stride\": BACKTEST_STRIDE,\n",
+    "        \"refresh_input_data\": REFRESH_INPUT_DATA,\n",
+    "        \"run_sweep\": RUN_SWEEP,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b599794",
+   "metadata": {},
+   "source": [
+    "## Imports and active features\n",
+    "\n",
+    "The notebook imports the candidate definitions and evaluation helpers from `run_parameter_smoke.py`; it does not duplicate the sweep logic. The active feature list is printed so changes in `features.py` are visible before the run starts.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ae1fcbe6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Repository: /home/coder/BMO-C2-agentic-forecasting-1\n",
+      "Active statistical features (15):\n",
+      "  - ipman_change_1m_pct\n",
+      "  - ipman_change_3m_pct\n",
+      "  - ipman_change_6m_pct\n",
+      "  - FEDFUNDS\n",
+      "  - YC_SPREAD\n",
+      "  - CPIAUCSL\n",
+      "  - CPI_YOY\n",
+      "  - UNRATE\n",
+      "  - ICSA\n",
+      "  - VIXCLS\n",
+      "  - CREDIT_SPREAD\n",
+      "  - SPY_RETURN_3M\n",
+      "  - SPY_RETURN_12M\n",
+      "  - XLI_RETURN_3M\n",
+      "  - XLI_RETURN_12M\n"
+     ]
+    }
+   ],
+   "source": [
+    "import yaml\n",
+    "from aieng.forecasting.evaluation import BacktestSpec\n",
+    "from dotenv import load_dotenv\n",
+    "from IPython.display import display as ipython_display\n",
+    "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+    "from manufacturing_stress_forecasting.features import FEATURE_SERIES_IDS\n",
+    "from manufacturing_stress_forecasting.run_parameter_smoke import (\n",
+    "    BASELINE_NAME,\n",
+    "    CANDIDATES,\n",
+    "    build_experiment_spec,\n",
+    "    comparison_table,\n",
+    "    predictors_for_stage,\n",
+    "    run_candidates,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "REPO_ROOT = SETUP_ROOT\n",
+    "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+    "SPEC_PATH = (\n",
+    "    REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"specs\" / \"manufacturing_stress_smoke.yaml\"\n",
+    ")\n",
+    "print(f\"Repository: {REPO_ROOT}\")\n",
+    "print(f\"Active statistical features ({len(FEATURE_SERIES_IDS)}):\")\n",
+    "for feature_name in FEATURE_SERIES_IDS:\n",
+    "    print(f\"  - {feature_name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4c83b4b",
+   "metadata": {},
+   "source": [
+    "## Create or refresh the data service\n",
+    "\n",
+    "The default reads the existing local FRED and Yahoo Finance caches. Set `REFRESH_INPUT_DATA = True` only when you intentionally want to download fresh source data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a0ad297b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>series_id</th>\n",
+       "      <th>description</th>\n",
+       "      <th>source</th>\n",
+       "      <th>units</th>\n",
+       "      <th>frequency</th>\n",
+       "      <th>n_obs</th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CPIAUCSL</td>\n",
+       "      <td>Consumer Price Index for All Urban Consumers</td>\n",
+       "      <td>FRED (CPIAUCSL)</td>\n",
+       "      <td>Index</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>955</td>\n",
+       "      <td>1947-01-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CPI_YOY</td>\n",
+       "      <td>Consumer Price Index year-over-year change</td>\n",
+       "      <td>Derived from FRED (CPIAUCSL)</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>943</td>\n",
+       "      <td>1948-01-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CREDIT_SPREAD</td>\n",
+       "      <td>Moody's Baa corporate bond yield minus 10-year...</td>\n",
+       "      <td>FRED (BAA10Y)</td>\n",
+       "      <td>Percentage points</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>489</td>\n",
+       "      <td>1986-01-01</td>\n",
+       "      <td>2026-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>FEDFUNDS</td>\n",
+       "      <td>Effective federal funds rate</td>\n",
+       "      <td>FRED (FEDFUNDS)</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>867</td>\n",
+       "      <td>1954-07-01</td>\n",
+       "      <td>2026-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ICSA</td>\n",
+       "      <td>Initial claims for unemployment insurance</td>\n",
+       "      <td>FRED (ICSA)</td>\n",
+       "      <td>Number</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>717</td>\n",
+       "      <td>1967-01-01</td>\n",
+       "      <td>2026-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>SPY_RETURN_12M</td>\n",
+       "      <td>SPY trailing 12-month adjusted-close return</td>\n",
+       "      <td>Yahoo Finance (SPY), derived</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>334</td>\n",
+       "      <td>1999-01-01</td>\n",
+       "      <td>2026-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>SPY_RETURN_3M</td>\n",
+       "      <td>SPY trailing 3-month adjusted-close return</td>\n",
+       "      <td>Yahoo Finance (SPY), derived</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>343</td>\n",
+       "      <td>1998-04-01</td>\n",
+       "      <td>2026-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>UNRATE</td>\n",
+       "      <td>U.S. unemployment rate</td>\n",
+       "      <td>FRED (UNRATE)</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>943</td>\n",
+       "      <td>1948-01-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>VIXCLS</td>\n",
+       "      <td>CBOE volatility index</td>\n",
+       "      <td>FRED (VIXCLS)</td>\n",
+       "      <td>Index</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>441</td>\n",
+       "      <td>1990-01-01</td>\n",
+       "      <td>2026-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>XLI_RETURN_12M</td>\n",
+       "      <td>XLI trailing 12-month adjusted-close return</td>\n",
+       "      <td>Yahoo Finance (XLI), derived</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>323</td>\n",
+       "      <td>1999-12-01</td>\n",
+       "      <td>2026-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>XLI_RETURN_3M</td>\n",
+       "      <td>XLI trailing 3-month adjusted-close return</td>\n",
+       "      <td>Yahoo Finance (XLI), derived</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>332</td>\n",
+       "      <td>1999-03-01</td>\n",
+       "      <td>2026-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>YC_SPREAD</td>\n",
+       "      <td>10-year minus 2-year Treasury yield spread</td>\n",
+       "      <td>FRED (DGS10 minus DGS2)</td>\n",
+       "      <td>Percentage points</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>604</td>\n",
+       "      <td>1976-06-01</td>\n",
+       "      <td>2026-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>fed_funds_rate_pct</td>\n",
+       "      <td>Month-end effective federal funds rate</td>\n",
+       "      <td>FRED (FEDFUNDS), derived monthly</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>867</td>\n",
+       "      <td>1954-07-01</td>\n",
+       "      <td>2026-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>ipman_change_1m_pct</td>\n",
+       "      <td>Trailing 1-month percentage change in IPMAN</td>\n",
+       "      <td>Derived from FRED IPMAN</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>655</td>\n",
+       "      <td>1972-02-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>ipman_change_3m_pct</td>\n",
+       "      <td>Trailing 3-month percentage change in IPMAN</td>\n",
+       "      <td>Derived from FRED IPMAN</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>653</td>\n",
+       "      <td>1972-04-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>ipman_change_6m_pct</td>\n",
+       "      <td>Trailing 6-month percentage change in IPMAN</td>\n",
+       "      <td>Derived from FRED IPMAN</td>\n",
+       "      <td>Percent</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>650</td>\n",
+       "      <td>1972-07-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ipman_us_manufacturing_production</td>\n",
+       "      <td>U.S. manufacturing industrial production index...</td>\n",
+       "      <td>FRED (IPMAN)</td>\n",
+       "      <td>Index</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>656</td>\n",
+       "      <td>1972-01-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>manufacturing_stress</td>\n",
+       "      <td>Binary U.S. manufacturing stress label: 1 when...</td>\n",
+       "      <td>Derived from FRED IPMAN</td>\n",
+       "      <td>Binary event (0=no stress, 1=stress)</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>653</td>\n",
+       "      <td>1972-04-01</td>\n",
+       "      <td>2026-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>treasury_10y_minus_2y_pct_points</td>\n",
+       "      <td>Month-end 10-year minus 2-year Treasury yield ...</td>\n",
+       "      <td>FRED (DGS10 minus DGS2), derived monthly</td>\n",
+       "      <td>Percentage points</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>604</td>\n",
+       "      <td>1976-06-01</td>\n",
+       "      <td>2026-09-01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            series_id  \\\n",
+       "0                            CPIAUCSL   \n",
+       "1                             CPI_YOY   \n",
+       "2                       CREDIT_SPREAD   \n",
+       "3                            FEDFUNDS   \n",
+       "4                                ICSA   \n",
+       "5                      SPY_RETURN_12M   \n",
+       "6                       SPY_RETURN_3M   \n",
+       "7                              UNRATE   \n",
+       "8                              VIXCLS   \n",
+       "9                      XLI_RETURN_12M   \n",
+       "10                      XLI_RETURN_3M   \n",
+       "11                          YC_SPREAD   \n",
+       "12                 fed_funds_rate_pct   \n",
+       "13                ipman_change_1m_pct   \n",
+       "14                ipman_change_3m_pct   \n",
+       "15                ipman_change_6m_pct   \n",
+       "16  ipman_us_manufacturing_production   \n",
+       "17               manufacturing_stress   \n",
+       "18   treasury_10y_minus_2y_pct_points   \n",
+       "\n",
+       "                                          description  \\\n",
+       "0        Consumer Price Index for All Urban Consumers   \n",
+       "1          Consumer Price Index year-over-year change   \n",
+       "2   Moody's Baa corporate bond yield minus 10-year...   \n",
+       "3                        Effective federal funds rate   \n",
+       "4           Initial claims for unemployment insurance   \n",
+       "5         SPY trailing 12-month adjusted-close return   \n",
+       "6          SPY trailing 3-month adjusted-close return   \n",
+       "7                              U.S. unemployment rate   \n",
+       "8                               CBOE volatility index   \n",
+       "9         XLI trailing 12-month adjusted-close return   \n",
+       "10         XLI trailing 3-month adjusted-close return   \n",
+       "11         10-year minus 2-year Treasury yield spread   \n",
+       "12             Month-end effective federal funds rate   \n",
+       "13        Trailing 1-month percentage change in IPMAN   \n",
+       "14        Trailing 3-month percentage change in IPMAN   \n",
+       "15        Trailing 6-month percentage change in IPMAN   \n",
+       "16  U.S. manufacturing industrial production index...   \n",
+       "17  Binary U.S. manufacturing stress label: 1 when...   \n",
+       "18  Month-end 10-year minus 2-year Treasury yield ...   \n",
+       "\n",
+       "                                      source  \\\n",
+       "0                            FRED (CPIAUCSL)   \n",
+       "1               Derived from FRED (CPIAUCSL)   \n",
+       "2                              FRED (BAA10Y)   \n",
+       "3                            FRED (FEDFUNDS)   \n",
+       "4                                FRED (ICSA)   \n",
+       "5               Yahoo Finance (SPY), derived   \n",
+       "6               Yahoo Finance (SPY), derived   \n",
+       "7                              FRED (UNRATE)   \n",
+       "8                              FRED (VIXCLS)   \n",
+       "9               Yahoo Finance (XLI), derived   \n",
+       "10              Yahoo Finance (XLI), derived   \n",
+       "11                   FRED (DGS10 minus DGS2)   \n",
+       "12          FRED (FEDFUNDS), derived monthly   \n",
+       "13                   Derived from FRED IPMAN   \n",
+       "14                   Derived from FRED IPMAN   \n",
+       "15                   Derived from FRED IPMAN   \n",
+       "16                              FRED (IPMAN)   \n",
+       "17                   Derived from FRED IPMAN   \n",
+       "18  FRED (DGS10 minus DGS2), derived monthly   \n",
+       "\n",
+       "                                   units frequency  n_obs      start  \\\n",
+       "0                                  Index        MS    955 1947-01-01   \n",
+       "1                                Percent        MS    943 1948-01-01   \n",
+       "2                      Percentage points        MS    489 1986-01-01   \n",
+       "3                                Percent        MS    867 1954-07-01   \n",
+       "4                                 Number        MS    717 1967-01-01   \n",
+       "5                                Percent        MS    334 1999-01-01   \n",
+       "6                                Percent        MS    343 1998-04-01   \n",
+       "7                                Percent        MS    943 1948-01-01   \n",
+       "8                                  Index        MS    441 1990-01-01   \n",
+       "9                                Percent        MS    323 1999-12-01   \n",
+       "10                               Percent        MS    332 1999-03-01   \n",
+       "11                     Percentage points        MS    604 1976-06-01   \n",
+       "12                               Percent        MS    867 1954-07-01   \n",
+       "13                               Percent        MS    655 1972-02-01   \n",
+       "14                               Percent        MS    653 1972-04-01   \n",
+       "15                               Percent        MS    650 1972-07-01   \n",
+       "16                                 Index        MS    656 1972-01-01   \n",
+       "17  Binary event (0=no stress, 1=stress)        MS    653 1972-04-01   \n",
+       "18                     Percentage points        MS    604 1976-06-01   \n",
+       "\n",
+       "          end  \n",
+       "0  2026-08-01  \n",
+       "1  2026-08-01  \n",
+       "2  2026-09-01  \n",
+       "3  2026-09-01  \n",
+       "4  2026-09-01  \n",
+       "5  2026-10-01  \n",
+       "6  2026-10-01  \n",
+       "7  2026-08-01  \n",
+       "8  2026-09-01  \n",
+       "9  2026-10-01  \n",
+       "10 2026-10-01  \n",
+       "11 2026-09-01  \n",
+       "12 2026-09-01  \n",
+       "13 2026-08-01  \n",
+       "14 2026-08-01  \n",
+       "15 2026-08-01  \n",
+       "16 2026-08-01  \n",
+       "17 2026-08-01  \n",
+       "18 2026-09-01  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "service = build_manufacturing_stress_service(\n",
+    "    cache_dir=REPO_ROOT / \"data\" / \"fred\",\n",
+    "    yahoo_cache_dir=REPO_ROOT / \"data\" / \"yfinance\",\n",
+    "    refresh=REFRESH_INPUT_DATA,\n",
+    ")\n",
+    "ipython_display(service.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "587935f2",
+   "metadata": {},
+   "source": [
+    "## Build the tuning or confirmation experiment\n",
+    "\n",
+    "The stage determines the date window and candidate set. Tuning runs the historical-frequency baseline plus every configured candidate. Confirmation runs the baseline plus exactly one selected candidate.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0ea58bb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stage=confirm; origins=2018-01-01 to 2024-12-01; stride=3; horizon=3 month(s)\n",
+      "Predictors:\n",
+      "  - historical_frequency\n",
+      "  - logistic_c_1_0\n"
+     ]
+    }
+   ],
+   "source": [
+    "if STAGE not in {\"tune\", \"confirm\"}:\n",
+    "    raise ValueError(\"STAGE must be 'tune' or 'confirm'.\")\n",
+    "if BACKTEST_STRIDE not in {1, 3}:\n",
+    "    raise ValueError(\"BACKTEST_STRIDE must be 1 or 3.\")\n",
+    "if STAGE == \"confirm\" and CANDIDATE is None:\n",
+    "    raise ValueError(\"Set CANDIDATE to the tuning winner before confirmation.\")\n",
+    "if STAGE == \"tune\" and CANDIDATE is not None:\n",
+    "    raise ValueError(\"Leave CANDIDATE as None during tuning.\")\n",
+    "\n",
+    "with SPEC_PATH.open() as file:\n",
+    "    base_spec = BacktestSpec.model_validate(yaml.safe_load(file))\n",
+    "\n",
+    "spec = build_experiment_spec(base_spec, stage=STAGE, stride=BACKTEST_STRIDE)\n",
+    "named_predictors = predictors_for_stage(STAGE, CANDIDATE)\n",
+    "\n",
+    "print(\n",
+    "    f\"Stage={STAGE}; origins={spec.start.date()} to {spec.end.date()}; \"\n",
+    "    f\"stride={spec.stride}; horizon={spec.task.horizons[0]} month(s)\"\n",
+    ")\n",
+    "print(\"Predictors:\")\n",
+    "for predictor_name, _predictor in named_predictors:\n",
+    "    print(f\"  - {predictor_name}\")\n",
+    "\n",
+    "if STAGE == \"tune\":\n",
+    "    print(\"Available confirmation names:\")\n",
+    "    for candidate in CANDIDATES:\n",
+    "        print(f\"  - {candidate.name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f0e6d93",
+   "metadata": {},
+   "source": [
+    "## Run the parameter sweep\n",
+    "\n",
+    "This can take several minutes because every predictor is refit at every forecast origin. Set `RUN_SWEEP = False` when you only want to inspect the setup.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4967490b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running historical_frequency...\n",
+      "Running logistic_c_1_0...\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = None\n",
+    "table = None\n",
+    "if RUN_SWEEP:\n",
+    "    results = run_candidates(named_predictors, spec=spec, service=service)\n",
+    "    table = comparison_table(results)\n",
+    "else:\n",
+    "    print(\"Sweep skipped because RUN_SWEEP is False.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da97c625",
+   "metadata": {},
+   "source": [
+    "## Compare Brier scores\n",
+    "\n",
+    "Lower `mean_brier` is better. A negative `delta_vs_baseline` and positive `brier_skill` indicate improvement over historical frequency. `scored` and `skipped` verify that the comparison used the expected forecast origins.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "17e5f7a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_05897\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_05897_level0_col0\" class=\"col_heading level0 col0\" >candidate</th>\n",
+       "      <th id=\"T_05897_level0_col1\" class=\"col_heading level0 col1\" >mean_brier</th>\n",
+       "      <th id=\"T_05897_level0_col2\" class=\"col_heading level0 col2\" >delta_vs_baseline</th>\n",
+       "      <th id=\"T_05897_level0_col3\" class=\"col_heading level0 col3\" >brier_skill</th>\n",
+       "      <th id=\"T_05897_level0_col4\" class=\"col_heading level0 col4\" >scored</th>\n",
+       "      <th id=\"T_05897_level0_col5\" class=\"col_heading level0 col5\" >skipped</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_05897_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_05897_row0_col0\" class=\"data row0 col0\" >historical_frequency</td>\n",
+       "      <td id=\"T_05897_row0_col1\" class=\"data row0 col1\" >0.0356</td>\n",
+       "      <td id=\"T_05897_row0_col2\" class=\"data row0 col2\" >+0.0000</td>\n",
+       "      <td id=\"T_05897_row0_col3\" class=\"data row0 col3\" >+0.0%</td>\n",
+       "      <td id=\"T_05897_row0_col4\" class=\"data row0 col4\" >28</td>\n",
+       "      <td id=\"T_05897_row0_col5\" class=\"data row0 col5\" >0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_05897_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_05897_row1_col0\" class=\"data row1 col0\" >logistic_c_1_0</td>\n",
+       "      <td id=\"T_05897_row1_col1\" class=\"data row1 col1\" >0.2327</td>\n",
+       "      <td id=\"T_05897_row1_col2\" class=\"data row1 col2\" >+0.1971</td>\n",
+       "      <td id=\"T_05897_row1_col3\" class=\"data row1 col3\" >-553.7%</td>\n",
+       "      <td id=\"T_05897_row1_col4\" class=\"data row1 col4\" >28</td>\n",
+       "      <td id=\"T_05897_row1_col5\" class=\"data row1 col5\" >0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7cba467e7e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confirmation completed for logistic_c_1_0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "if table is not None:\n",
+    "    ipython_display(\n",
+    "        table.style.format(\n",
+    "            {\n",
+    "                \"mean_brier\": \"{:.4f}\",\n",
+    "                \"delta_vs_baseline\": \"{:+.4f}\",\n",
+    "                \"brier_skill\": \"{:+.1%}\",\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    if STAGE == \"tune\":\n",
+    "        best_model = table[table[\"candidate\"] != BASELINE_NAME].iloc[0]\n",
+    "        selected_candidate = str(best_model[\"candidate\"])\n",
+    "        print(f\"Best non-baseline tuning candidate: {selected_candidate}\")\n",
+    "        if float(best_model[\"brier_skill\"]) <= 0:\n",
+    "            print(\"Historical frequency still has the lower tuning-period Brier score.\")\n",
+    "        print(\"To confirm it, set the controls to:\")\n",
+    "        print('STAGE = \"confirm\"')\n",
+    "        print(f'CANDIDATE = \"{selected_candidate}\"')\n",
+    "        print(f\"BACKTEST_STRIDE = {BACKTEST_STRIDE}\")\n",
+    "    else:\n",
+    "        print(f\"Confirmation completed for {CANDIDATE}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f60bb12c",
+   "metadata": {},
+   "source": [
+    "## Confirmation discipline\n",
+    "\n",
+    "After the tuning run, copy the printed winner into `CANDIDATE`, change `STAGE` to `\"confirm\"`, keep the same stride, and rerun the notebook. Confirmation deliberately evaluates only that candidate and historical frequency. If you inspect multiple candidates on 2018–2024 and then choose among them, the period is no longer a protected confirmation window.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "agentic-forecasting-bootcamp (3.12.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_parameter_sweep_workbench_2.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_parameter_sweep_workbench_2.ipynb
new file mode 100644
index 000000000..e7c4950ad
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_parameter_sweep_workbench_2.ipynb
@@ -0,0 +1,2045 @@
+{
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "f1b13f2c",
+            "metadata": {
+                "id": "f1b13f2c",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Running: uv sync --all-extras --dev --all-packages\n",
+                        "\n",
+                        "Environment synchronized successfully.\n",
+                        "If packages changed, restart the notebook kernel before continuing.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# Cell 0: synchronize the repository environment before running the workbench.\n",
+                "import subprocess\n",
+                "from pathlib import Path\n",
+                "\n",
+                "\n",
+                "def find_repo_root(start: Path) -> Path:\n",
+                "    \"\"\"Find the workspace root without confusing member pyproject files for it.\"\"\"\n",
+                "    for candidate in (start, *start.parents):\n",
+                "        if (\n",
+                "            (candidate / \"uv.lock\").exists()\n",
+                "            and (candidate / \"implementations\").is_dir()\n",
+                "            and (candidate / \"aieng-forecasting\").is_dir()\n",
+                "        ):\n",
+                "            return candidate\n",
+                "    raise FileNotFoundError(\"Could not find the agentic-forecasting repository root.\")\n",
+                "\n",
+                "\n",
+                "SETUP_ROOT = find_repo_root(Path.cwd().resolve())\n",
+                "print(\"Running: uv sync --all-extras --dev --all-packages\")\n",
+                "setup_result = subprocess.run(\n",
+                "    [\"uv\", \"sync\", \"--all-extras\", \"--dev\", \"--all-packages\"],\n",
+                "    cwd=SETUP_ROOT,\n",
+                "    check=False,\n",
+                "    text=True,\n",
+                "    capture_output=True,\n",
+                ")\n",
+                "print(setup_result.stdout)\n",
+                "if setup_result.returncode != 0:\n",
+                "    print(setup_result.stderr)\n",
+                "    raise RuntimeError(\"uv sync failed. Install uv or restart the notebook with the repository environment selected.\")\n",
+                "print(\"Environment synchronized successfully.\")\n",
+                "print(\"If packages changed, restart the notebook kernel before continuing.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "e90728ca",
+            "metadata": {
+                "id": "e90728ca",
+                "language": "markdown"
+            },
+            "source": [
+                "## Purpose and controls\n",
+                "\n",
+                "The tuning stage compares historical frequency, six logistic regularization settings, and eight small XGBoost settings across three chronological folds within 2000-2017. The XGBoost candidates include shallower trees, slower learning rates, and a regularized depth-2 model. Each forecast is still fit using only data available at its own origin.\n",
+                "\n",
+                "The selected fixed configuration for downstream workbenches is `logistic_c_0_001` for logistic regression and `xgb_50_depth2_lr0_03_minchild3_l2_5` for XGBoost. The hybrid numerical anchor uses the logistic candidate only; the full sweep remains available for reproducible model comparison.\n",
+                "\n",
+                "The fixed 2018-2024 confirmation window was examined by an earlier run. Treat it as a historical diagnostic, not as an untouched holdout or a basis for claiming that the selected configuration beats the historical-frequency baseline. A clean next confirmation needs future or prospectively recorded outcomes."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "id": "79b2d5db",
+            "metadata": {
+                "id": "79b2d5db",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "{'stage': 'tune', 'candidate': None, 'backtest_stride': 3, 'refresh_input_data': False, 'run_sweep': True, 'show_origin_details': False}\n"
+                    ]
+                }
+            ],
+            "source": [
+                "# Main controls: change these values, then run the notebook from top to bottom.\n",
+                "STAGE = \"tune\"\n",
+                "CANDIDATE = None\n",
+                "BACKTEST_STRIDE = 3\n",
+                "REFRESH_INPUT_DATA = False\n",
+                "RUN_SWEEP = True\n",
+                "SHOW_ORIGIN_DETAILS = False\n",
+                "\n",
+                "# For confirmation, use only a candidate that passes the tuning stability rule.\n",
+                "# Keep BACKTEST_STRIDE unchanged between stages.\n",
+                "print(\n",
+                "    {\n",
+                "        \"stage\": STAGE,\n",
+                "        \"candidate\": CANDIDATE,\n",
+                "        \"backtest_stride\": BACKTEST_STRIDE,\n",
+                "        \"refresh_input_data\": REFRESH_INPUT_DATA,\n",
+                "        \"run_sweep\": RUN_SWEEP,\n",
+                "        \"show_origin_details\": SHOW_ORIGIN_DETAILS,\n",
+                "    }\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "3b599794",
+            "metadata": {
+                "id": "3b599794",
+                "language": "markdown"
+            },
+            "source": [
+                "## Imports and active features\n",
+                "\n",
+                "The notebook imports the candidate definitions and evaluation helpers from `run_parameter_smoke.py`; it does not duplicate the sweep logic. The active feature list is printed so changes in `features.py` are visible before the run starts.\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "ae1fcbe6",
+            "metadata": {
+                "id": "ae1fcbe6",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Repository: C:\\Users\\Mina Attari\\BMO-C2-agentic-forecasting\n",
+                        "Active statistical features (15):\n",
+                        "  - ipman_change_1m_pct\n",
+                        "  - ipman_change_3m_pct\n",
+                        "  - ipman_change_6m_pct\n",
+                        "  - FEDFUNDS\n",
+                        "  - YC_SPREAD\n",
+                        "  - CPIAUCSL\n",
+                        "  - CPI_YOY\n",
+                        "  - UNRATE\n",
+                        "  - ICSA\n",
+                        "  - VIXCLS\n",
+                        "  - CREDIT_SPREAD\n",
+                        "  - SPY_RETURN_3M\n",
+                        "  - SPY_RETURN_12M\n",
+                        "  - XLI_RETURN_3M\n",
+                        "  - XLI_RETURN_12M\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from importlib import reload\n",
+                "\n",
+                "import manufacturing_stress_forecasting.run_parameter_smoke as parameter_smoke\n",
+                "import yaml\n",
+                "from aieng.forecasting.evaluation import BacktestSpec\n",
+                "from dotenv import load_dotenv\n",
+                "from IPython.display import display as ipython_display\n",
+                "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+                "from manufacturing_stress_forecasting.features import FEATURE_SERIES_IDS\n",
+                "\n",
+                "\n",
+                "parameter_smoke = reload(parameter_smoke)\n",
+                "REPO_ROOT = SETUP_ROOT\n",
+                "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+                "SPEC_PATH = (\n",
+                "    REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"specs\" / \"manufacturing_stress_smoke.yaml\"\n",
+                ")\n",
+                "print(f\"Repository: {REPO_ROOT}\")\n",
+                "print(f\"Active statistical features ({len(FEATURE_SERIES_IDS)}):\")\n",
+                "for feature_name in FEATURE_SERIES_IDS:\n",
+                "    print(f\"  - {feature_name}\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "b4c83b4b",
+            "metadata": {
+                "id": "b4c83b4b",
+                "language": "markdown"
+            },
+            "source": [
+                "## Create or refresh the data service\n",
+                "\n",
+                "The default reads the existing local FRED and Yahoo Finance caches. Set `REFRESH_INPUT_DATA = True` only when you intentionally want to download fresh source data.\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "id": "a0ad297b",
+            "metadata": {
+                "id": "a0ad297b",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>series_id</th>\n",
+                            "      <th>description</th>\n",
+                            "      <th>source</th>\n",
+                            "      <th>units</th>\n",
+                            "      <th>frequency</th>\n",
+                            "      <th>n_obs</th>\n",
+                            "      <th>start</th>\n",
+                            "      <th>end</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>CPIAUCSL</td>\n",
+                            "      <td>Consumer Price Index for All Urban Consumers</td>\n",
+                            "      <td>FRED (CPIAUCSL)</td>\n",
+                            "      <td>Index</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>955</td>\n",
+                            "      <td>1947-01-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>CPI_YOY</td>\n",
+                            "      <td>Consumer Price Index year-over-year change</td>\n",
+                            "      <td>Derived from FRED (CPIAUCSL)</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>943</td>\n",
+                            "      <td>1948-01-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>CREDIT_SPREAD</td>\n",
+                            "      <td>Moody's Baa corporate bond yield minus 10-year...</td>\n",
+                            "      <td>FRED (BAA10Y)</td>\n",
+                            "      <td>Percentage points</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>489</td>\n",
+                            "      <td>1986-01-01</td>\n",
+                            "      <td>2026-09-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>FEDFUNDS</td>\n",
+                            "      <td>Effective federal funds rate</td>\n",
+                            "      <td>FRED (FEDFUNDS)</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>866</td>\n",
+                            "      <td>1954-07-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>ICSA</td>\n",
+                            "      <td>Initial claims for unemployment insurance</td>\n",
+                            "      <td>FRED (ICSA)</td>\n",
+                            "      <td>Number</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>717</td>\n",
+                            "      <td>1967-01-01</td>\n",
+                            "      <td>2026-09-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>5</th>\n",
+                            "      <td>SPY_RETURN_12M</td>\n",
+                            "      <td>SPY trailing 12-month adjusted-close return</td>\n",
+                            "      <td>Yahoo Finance (SPY), derived</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>334</td>\n",
+                            "      <td>1999-01-01</td>\n",
+                            "      <td>2026-10-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>6</th>\n",
+                            "      <td>SPY_RETURN_3M</td>\n",
+                            "      <td>SPY trailing 3-month adjusted-close return</td>\n",
+                            "      <td>Yahoo Finance (SPY), derived</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>343</td>\n",
+                            "      <td>1998-04-01</td>\n",
+                            "      <td>2026-10-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>7</th>\n",
+                            "      <td>UNRATE</td>\n",
+                            "      <td>U.S. unemployment rate</td>\n",
+                            "      <td>FRED (UNRATE)</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>943</td>\n",
+                            "      <td>1948-01-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>8</th>\n",
+                            "      <td>VIXCLS</td>\n",
+                            "      <td>CBOE volatility index</td>\n",
+                            "      <td>FRED (VIXCLS)</td>\n",
+                            "      <td>Index</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>441</td>\n",
+                            "      <td>1990-01-01</td>\n",
+                            "      <td>2026-09-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>9</th>\n",
+                            "      <td>XLI_RETURN_12M</td>\n",
+                            "      <td>XLI trailing 12-month adjusted-close return</td>\n",
+                            "      <td>Yahoo Finance (XLI), derived</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>323</td>\n",
+                            "      <td>1999-12-01</td>\n",
+                            "      <td>2026-10-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>10</th>\n",
+                            "      <td>XLI_RETURN_3M</td>\n",
+                            "      <td>XLI trailing 3-month adjusted-close return</td>\n",
+                            "      <td>Yahoo Finance (XLI), derived</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>332</td>\n",
+                            "      <td>1999-03-01</td>\n",
+                            "      <td>2026-10-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>11</th>\n",
+                            "      <td>YC_SPREAD</td>\n",
+                            "      <td>10-year minus 2-year Treasury yield spread</td>\n",
+                            "      <td>FRED (DGS10 minus DGS2)</td>\n",
+                            "      <td>Percentage points</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>604</td>\n",
+                            "      <td>1976-06-01</td>\n",
+                            "      <td>2026-09-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>12</th>\n",
+                            "      <td>fed_funds_rate_pct</td>\n",
+                            "      <td>Month-end effective federal funds rate</td>\n",
+                            "      <td>FRED (FEDFUNDS), derived monthly</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>866</td>\n",
+                            "      <td>1954-07-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>13</th>\n",
+                            "      <td>ipman_change_1m_pct</td>\n",
+                            "      <td>Trailing 1-month percentage change in IPMAN</td>\n",
+                            "      <td>Derived from FRED IPMAN</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>655</td>\n",
+                            "      <td>1972-02-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>14</th>\n",
+                            "      <td>ipman_change_3m_pct</td>\n",
+                            "      <td>Trailing 3-month percentage change in IPMAN</td>\n",
+                            "      <td>Derived from FRED IPMAN</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>653</td>\n",
+                            "      <td>1972-04-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>15</th>\n",
+                            "      <td>ipman_change_6m_pct</td>\n",
+                            "      <td>Trailing 6-month percentage change in IPMAN</td>\n",
+                            "      <td>Derived from FRED IPMAN</td>\n",
+                            "      <td>Percent</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>650</td>\n",
+                            "      <td>1972-07-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>16</th>\n",
+                            "      <td>ipman_us_manufacturing_production</td>\n",
+                            "      <td>U.S. manufacturing industrial production index...</td>\n",
+                            "      <td>FRED (IPMAN)</td>\n",
+                            "      <td>Index</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>656</td>\n",
+                            "      <td>1972-01-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>17</th>\n",
+                            "      <td>manufacturing_stress</td>\n",
+                            "      <td>Binary U.S. manufacturing stress label: 1 when...</td>\n",
+                            "      <td>Derived from FRED IPMAN</td>\n",
+                            "      <td>Binary event (0=no stress, 1=stress)</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>653</td>\n",
+                            "      <td>1972-04-01</td>\n",
+                            "      <td>2026-08-01</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>18</th>\n",
+                            "      <td>treasury_10y_minus_2y_pct_points</td>\n",
+                            "      <td>Month-end 10-year minus 2-year Treasury yield ...</td>\n",
+                            "      <td>FRED (DGS10 minus DGS2), derived monthly</td>\n",
+                            "      <td>Percentage points</td>\n",
+                            "      <td>MS</td>\n",
+                            "      <td>604</td>\n",
+                            "      <td>1976-06-01</td>\n",
+                            "      <td>2026-09-01</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                            series_id  \\\n",
+                            "0                            CPIAUCSL   \n",
+                            "1                             CPI_YOY   \n",
+                            "2                       CREDIT_SPREAD   \n",
+                            "3                            FEDFUNDS   \n",
+                            "4                                ICSA   \n",
+                            "5                      SPY_RETURN_12M   \n",
+                            "6                       SPY_RETURN_3M   \n",
+                            "7                              UNRATE   \n",
+                            "8                              VIXCLS   \n",
+                            "9                      XLI_RETURN_12M   \n",
+                            "10                      XLI_RETURN_3M   \n",
+                            "11                          YC_SPREAD   \n",
+                            "12                 fed_funds_rate_pct   \n",
+                            "13                ipman_change_1m_pct   \n",
+                            "14                ipman_change_3m_pct   \n",
+                            "15                ipman_change_6m_pct   \n",
+                            "16  ipman_us_manufacturing_production   \n",
+                            "17               manufacturing_stress   \n",
+                            "18   treasury_10y_minus_2y_pct_points   \n",
+                            "\n",
+                            "                                          description  \\\n",
+                            "0        Consumer Price Index for All Urban Consumers   \n",
+                            "1          Consumer Price Index year-over-year change   \n",
+                            "2   Moody's Baa corporate bond yield minus 10-year...   \n",
+                            "3                        Effective federal funds rate   \n",
+                            "4           Initial claims for unemployment insurance   \n",
+                            "5         SPY trailing 12-month adjusted-close return   \n",
+                            "6          SPY trailing 3-month adjusted-close return   \n",
+                            "7                              U.S. unemployment rate   \n",
+                            "8                               CBOE volatility index   \n",
+                            "9         XLI trailing 12-month adjusted-close return   \n",
+                            "10         XLI trailing 3-month adjusted-close return   \n",
+                            "11         10-year minus 2-year Treasury yield spread   \n",
+                            "12             Month-end effective federal funds rate   \n",
+                            "13        Trailing 1-month percentage change in IPMAN   \n",
+                            "14        Trailing 3-month percentage change in IPMAN   \n",
+                            "15        Trailing 6-month percentage change in IPMAN   \n",
+                            "16  U.S. manufacturing industrial production index...   \n",
+                            "17  Binary U.S. manufacturing stress label: 1 when...   \n",
+                            "18  Month-end 10-year minus 2-year Treasury yield ...   \n",
+                            "\n",
+                            "                                      source  \\\n",
+                            "0                            FRED (CPIAUCSL)   \n",
+                            "1               Derived from FRED (CPIAUCSL)   \n",
+                            "2                              FRED (BAA10Y)   \n",
+                            "3                            FRED (FEDFUNDS)   \n",
+                            "4                                FRED (ICSA)   \n",
+                            "5               Yahoo Finance (SPY), derived   \n",
+                            "6               Yahoo Finance (SPY), derived   \n",
+                            "7                              FRED (UNRATE)   \n",
+                            "8                              FRED (VIXCLS)   \n",
+                            "9               Yahoo Finance (XLI), derived   \n",
+                            "10              Yahoo Finance (XLI), derived   \n",
+                            "11                   FRED (DGS10 minus DGS2)   \n",
+                            "12          FRED (FEDFUNDS), derived monthly   \n",
+                            "13                   Derived from FRED IPMAN   \n",
+                            "14                   Derived from FRED IPMAN   \n",
+                            "15                   Derived from FRED IPMAN   \n",
+                            "16                              FRED (IPMAN)   \n",
+                            "17                   Derived from FRED IPMAN   \n",
+                            "18  FRED (DGS10 minus DGS2), derived monthly   \n",
+                            "\n",
+                            "                                   units frequency  n_obs      start  \\\n",
+                            "0                                  Index        MS    955 1947-01-01   \n",
+                            "1                                Percent        MS    943 1948-01-01   \n",
+                            "2                      Percentage points        MS    489 1986-01-01   \n",
+                            "3                                Percent        MS    866 1954-07-01   \n",
+                            "4                                 Number        MS    717 1967-01-01   \n",
+                            "5                                Percent        MS    334 1999-01-01   \n",
+                            "6                                Percent        MS    343 1998-04-01   \n",
+                            "7                                Percent        MS    943 1948-01-01   \n",
+                            "8                                  Index        MS    441 1990-01-01   \n",
+                            "9                                Percent        MS    323 1999-12-01   \n",
+                            "10                               Percent        MS    332 1999-03-01   \n",
+                            "11                     Percentage points        MS    604 1976-06-01   \n",
+                            "12                               Percent        MS    866 1954-07-01   \n",
+                            "13                               Percent        MS    655 1972-02-01   \n",
+                            "14                               Percent        MS    653 1972-04-01   \n",
+                            "15                               Percent        MS    650 1972-07-01   \n",
+                            "16                                 Index        MS    656 1972-01-01   \n",
+                            "17  Binary event (0=no stress, 1=stress)        MS    653 1972-04-01   \n",
+                            "18                     Percentage points        MS    604 1976-06-01   \n",
+                            "\n",
+                            "          end  \n",
+                            "0  2026-08-01  \n",
+                            "1  2026-08-01  \n",
+                            "2  2026-09-01  \n",
+                            "3  2026-08-01  \n",
+                            "4  2026-09-01  \n",
+                            "5  2026-10-01  \n",
+                            "6  2026-10-01  \n",
+                            "7  2026-08-01  \n",
+                            "8  2026-09-01  \n",
+                            "9  2026-10-01  \n",
+                            "10 2026-10-01  \n",
+                            "11 2026-09-01  \n",
+                            "12 2026-08-01  \n",
+                            "13 2026-08-01  \n",
+                            "14 2026-08-01  \n",
+                            "15 2026-08-01  \n",
+                            "16 2026-08-01  \n",
+                            "17 2026-08-01  \n",
+                            "18 2026-09-01  "
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "service = build_manufacturing_stress_service(\n",
+                "    cache_dir=REPO_ROOT / \"data\" / \"fred\",\n",
+                "    yahoo_cache_dir=REPO_ROOT / \"data\" / \"yfinance\",\n",
+                "    refresh=REFRESH_INPUT_DATA,\n",
+                ")\n",
+                "ipython_display(service.summary())"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "587935f2",
+            "metadata": {
+                "id": "587935f2",
+                "language": "markdown"
+            },
+            "source": [
+                "## Build the tuning or confirmation experiment\n",
+                "\n",
+                "The stage determines the date window and candidate set. Tuning runs the historical-frequency baseline plus every configured candidate. Confirmation runs the baseline plus exactly one selected candidate.\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "id": "0ea58bb2",
+            "metadata": {
+                "id": "0ea58bb2",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Stage=tune; origins=2000-01-01 to 2017-12-01; stride=3; horizon=3 month(s)\n",
+                        "Predictors:\n",
+                        "  - historical_frequency\n",
+                        "  - logistic_c_0_001\n",
+                        "  - logistic_c_0_003\n",
+                        "  - logistic_c_0_01\n",
+                        "  - logistic_c_0_03\n",
+                        "  - logistic_c_0_1\n",
+                        "  - logistic_c_1_0\n",
+                        "  - xgb_25_depth1_lr0_05\n",
+                        "  - xgb_50_depth1_lr0_05\n",
+                        "  - xgb_50_depth2_lr0_03\n",
+                        "  - xgb_100_depth2_lr0_05\n",
+                        "  - xgb_25_depth1_lr0_03\n",
+                        "  - xgb_50_depth1_lr0_03\n",
+                        "  - xgb_100_depth2_lr0_02\n",
+                        "  - xgb_50_depth2_lr0_03_minchild3_l2_5\n",
+                        "Configured candidates:\n",
+                        "  - logistic_c_0_001\n",
+                        "  - logistic_c_0_003\n",
+                        "  - logistic_c_0_01\n",
+                        "  - logistic_c_0_03\n",
+                        "  - logistic_c_0_1\n",
+                        "  - logistic_c_1_0\n",
+                        "  - xgb_25_depth1_lr0_05\n",
+                        "  - xgb_50_depth1_lr0_05\n",
+                        "  - xgb_50_depth2_lr0_03\n",
+                        "  - xgb_100_depth2_lr0_05\n",
+                        "  - xgb_25_depth1_lr0_03\n",
+                        "  - xgb_50_depth1_lr0_03\n",
+                        "  - xgb_100_depth2_lr0_02\n",
+                        "  - xgb_50_depth2_lr0_03_minchild3_l2_5\n"
+                    ]
+                }
+            ],
+            "source": [
+                "if STAGE not in {\"tune\", \"confirm\"}:\n",
+                "    raise ValueError(\"STAGE must be 'tune' or 'confirm'.\")\n",
+                "if BACKTEST_STRIDE not in {1, 3}:\n",
+                "    raise ValueError(\"BACKTEST_STRIDE must be 1 or 3.\")\n",
+                "if STAGE == \"confirm\" and CANDIDATE is None:\n",
+                "    raise ValueError(\"Set CANDIDATE to the tuning winner before confirmation.\")\n",
+                "if STAGE == \"tune\" and CANDIDATE is not None:\n",
+                "    raise ValueError(\"Leave CANDIDATE as None during tuning.\")\n",
+                "\n",
+                "with SPEC_PATH.open() as file:\n",
+                "    base_spec = BacktestSpec.model_validate(yaml.safe_load(file))\n",
+                "\n",
+                "spec = parameter_smoke.build_experiment_spec(base_spec, stage=STAGE, stride=BACKTEST_STRIDE)\n",
+                "named_predictors = parameter_smoke.predictors_for_stage(STAGE, CANDIDATE)\n",
+                "\n",
+                "print(\n",
+                "    f\"Stage={STAGE}; origins={spec.start.date()} to {spec.end.date()}; \"\n",
+                "    f\"stride={spec.stride}; horizon={spec.task.horizons[0]} month(s)\"\n",
+                ")\n",
+                "print(\"Predictors:\")\n",
+                "for predictor_name, _ in named_predictors:\n",
+                "    print(f\"  - {predictor_name}\")\n",
+                "\n",
+                "if STAGE == \"tune\":\n",
+                "    print(\"Configured candidates:\")\n",
+                "    for candidate in parameter_smoke.CANDIDATES:\n",
+                "        print(f\"  - {candidate.name}\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "0f0e6d93",
+            "metadata": {
+                "id": "0f0e6d93",
+                "language": "markdown"
+            },
+            "source": [
+                "## Run the parameter sweep\n",
+                "\n",
+                "Tuning runs 15 predictors across three folds, so it can take a while. Set `RUN_SWEEP = False` to inspect the setup without running models. Set `SHOW_ORIGIN_DETAILS = True` only when you need every forecast probability and resolved label."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "id": "4967490b",
+            "metadata": {
+                "id": "4967490b",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Tuning fold 2000-2005...\n",
+                        "Running historical_frequency...\n",
+                        "Running logistic_c_0_001...\n",
+                        "Running logistic_c_0_003...\n",
+                        "Running logistic_c_0_01...\n",
+                        "Running logistic_c_0_03...\n",
+                        "Running logistic_c_0_1...\n",
+                        "Running logistic_c_1_0...\n",
+                        "Running xgb_25_depth1_lr0_05...\n",
+                        "Running xgb_50_depth1_lr0_05...\n",
+                        "Running xgb_50_depth2_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_05...\n",
+                        "Running xgb_25_depth1_lr0_03...\n",
+                        "Running xgb_50_depth1_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_02...\n",
+                        "Running xgb_50_depth2_lr0_03_minchild3_l2_5...\n",
+                        "Tuning fold 2006-2011...\n",
+                        "Running historical_frequency...\n",
+                        "Running logistic_c_0_001...\n",
+                        "Running logistic_c_0_003...\n",
+                        "Running logistic_c_0_01...\n",
+                        "Running logistic_c_0_03...\n",
+                        "Running logistic_c_0_1...\n",
+                        "Running logistic_c_1_0...\n",
+                        "Running xgb_25_depth1_lr0_05...\n",
+                        "Running xgb_50_depth1_lr0_05...\n",
+                        "Running xgb_50_depth2_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_05...\n",
+                        "Running xgb_25_depth1_lr0_03...\n",
+                        "Running xgb_50_depth1_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_02...\n",
+                        "Running xgb_50_depth2_lr0_03_minchild3_l2_5...\n",
+                        "Tuning fold 2012-2017...\n",
+                        "Running historical_frequency...\n",
+                        "Running logistic_c_0_001...\n",
+                        "Running logistic_c_0_003...\n",
+                        "Running logistic_c_0_01...\n",
+                        "Running logistic_c_0_03...\n",
+                        "Running logistic_c_0_1...\n",
+                        "Running logistic_c_1_0...\n",
+                        "Running xgb_25_depth1_lr0_05...\n",
+                        "Running xgb_50_depth1_lr0_05...\n",
+                        "Running xgb_50_depth2_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_05...\n",
+                        "Running xgb_25_depth1_lr0_03...\n",
+                        "Running xgb_50_depth1_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_02...\n",
+                        "Running xgb_50_depth2_lr0_03_minchild3_l2_5...\n"
+                    ]
+                }
+            ],
+            "source": [
+                "results = None\n",
+                "fold_results = None\n",
+                "fold_table = None\n",
+                "pooled_table = None\n",
+                "origin_details = None\n",
+                "table = None\n",
+                "if RUN_SWEEP:\n",
+                "    if STAGE == \"tune\":\n",
+                "        fold_results = parameter_smoke.run_tuning_folds(\n",
+                "            base_spec=base_spec,\n",
+                "            stride=BACKTEST_STRIDE,\n",
+                "            service=service,\n",
+                "        )\n",
+                "        fold_table, pooled_table, origin_details = parameter_smoke.tuning_summary_tables(\n",
+                "            fold_results,\n",
+                "            service=service,\n",
+                "        )\n",
+                "    else:\n",
+                "        results = parameter_smoke.run_candidates(named_predictors, spec=spec, service=service)\n",
+                "        table = parameter_smoke.comparison_table(results)\n",
+                "else:\n",
+                "    print(\"Sweep skipped because RUN_SWEEP is False.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "da97c625",
+            "metadata": {
+                "id": "da97c625",
+                "language": "markdown"
+            },
+            "source": [
+                "## Compare Brier scores\n",
+                "\n",
+                "The first table reports Brier score, skill against historical frequency, and stress-event counts by fold. The second pools the tuning folds and reports how many folds, including event-bearing folds, each candidate beats the baseline in. Lower Brier is better.\n",
+                "\n",
+                "A candidate is suggested only when it improves pooled Brier, beats the baseline in at least two folds, and improves on an event-bearing fold. If stress events appear in fewer than two folds, treat any selected candidate as provisional. `SHOW_ORIGIN_DETAILS` reveals the forecast-level probabilities, labels, and Brier scores."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "id": "17e5f7a3",
+            "metadata": {
+                "id": "17e5f7a3",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<style type=\"text/css\">\n",
+                            "</style>\n",
+                            "<table id=\"T_682e2\">\n",
+                            "  <thead>\n",
+                            "    <tr>\n",
+                            "      <th class=\"blank level0\" >&nbsp;</th>\n",
+                            "      <th id=\"T_682e2_level0_col0\" class=\"col_heading level0 col0\" >candidate</th>\n",
+                            "      <th id=\"T_682e2_level0_col1\" class=\"col_heading level0 col1\" >fold</th>\n",
+                            "      <th id=\"T_682e2_level0_col2\" class=\"col_heading level0 col2\" >mean_brier</th>\n",
+                            "      <th id=\"T_682e2_level0_col3\" class=\"col_heading level0 col3\" >delta_vs_baseline</th>\n",
+                            "      <th id=\"T_682e2_level0_col4\" class=\"col_heading level0 col4\" >brier_skill</th>\n",
+                            "      <th id=\"T_682e2_level0_col5\" class=\"col_heading level0 col5\" >scored</th>\n",
+                            "      <th id=\"T_682e2_level0_col6\" class=\"col_heading level0 col6\" >skipped</th>\n",
+                            "      <th id=\"T_682e2_level0_col7\" class=\"col_heading level0 col7\" >stress_events</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+                            "      <td id=\"T_682e2_row0_col0\" class=\"data row0 col0\" >historical_frequency</td>\n",
+                            "      <td id=\"T_682e2_row0_col1\" class=\"data row0 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row0_col2\" class=\"data row0 col2\" >0.0033</td>\n",
+                            "      <td id=\"T_682e2_row0_col3\" class=\"data row0 col3\" >+0.0000</td>\n",
+                            "      <td id=\"T_682e2_row0_col4\" class=\"data row0 col4\" >+0.0%</td>\n",
+                            "      <td id=\"T_682e2_row0_col5\" class=\"data row0 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row0_col6\" class=\"data row0 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row0_col7\" class=\"data row0 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+                            "      <td id=\"T_682e2_row1_col0\" class=\"data row1 col0\" >logistic_c_0_001</td>\n",
+                            "      <td id=\"T_682e2_row1_col1\" class=\"data row1 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row1_col2\" class=\"data row1 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row1_col3\" class=\"data row1 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row1_col4\" class=\"data row1 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row1_col5\" class=\"data row1 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row1_col6\" class=\"data row1 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row1_col7\" class=\"data row1 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+                            "      <td id=\"T_682e2_row2_col0\" class=\"data row2 col0\" >logistic_c_0_003</td>\n",
+                            "      <td id=\"T_682e2_row2_col1\" class=\"data row2 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row2_col2\" class=\"data row2 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row2_col3\" class=\"data row2 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row2_col4\" class=\"data row2 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row2_col5\" class=\"data row2 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row2_col6\" class=\"data row2 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row2_col7\" class=\"data row2 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+                            "      <td id=\"T_682e2_row3_col0\" class=\"data row3 col0\" >logistic_c_0_01</td>\n",
+                            "      <td id=\"T_682e2_row3_col1\" class=\"data row3 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row3_col2\" class=\"data row3 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row3_col3\" class=\"data row3 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row3_col4\" class=\"data row3 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row3_col5\" class=\"data row3 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row3_col6\" class=\"data row3 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row3_col7\" class=\"data row3 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+                            "      <td id=\"T_682e2_row4_col0\" class=\"data row4 col0\" >logistic_c_0_03</td>\n",
+                            "      <td id=\"T_682e2_row4_col1\" class=\"data row4 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row4_col2\" class=\"data row4 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row4_col3\" class=\"data row4 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row4_col4\" class=\"data row4 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row4_col5\" class=\"data row4 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row4_col6\" class=\"data row4 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row4_col7\" class=\"data row4 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+                            "      <td id=\"T_682e2_row5_col0\" class=\"data row5 col0\" >logistic_c_0_1</td>\n",
+                            "      <td id=\"T_682e2_row5_col1\" class=\"data row5 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row5_col2\" class=\"data row5 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row5_col3\" class=\"data row5 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row5_col4\" class=\"data row5 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row5_col5\" class=\"data row5 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row5_col6\" class=\"data row5 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row5_col7\" class=\"data row5 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+                            "      <td id=\"T_682e2_row6_col0\" class=\"data row6 col0\" >logistic_c_1_0</td>\n",
+                            "      <td id=\"T_682e2_row6_col1\" class=\"data row6 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row6_col2\" class=\"data row6 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row6_col3\" class=\"data row6 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row6_col4\" class=\"data row6 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row6_col5\" class=\"data row6 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row6_col6\" class=\"data row6 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row6_col7\" class=\"data row6 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+                            "      <td id=\"T_682e2_row7_col0\" class=\"data row7 col0\" >xgb_25_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row7_col1\" class=\"data row7 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row7_col2\" class=\"data row7 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row7_col3\" class=\"data row7 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row7_col4\" class=\"data row7 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row7_col5\" class=\"data row7 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row7_col6\" class=\"data row7 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row7_col7\" class=\"data row7 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+                            "      <td id=\"T_682e2_row8_col0\" class=\"data row8 col0\" >xgb_50_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row8_col1\" class=\"data row8 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row8_col2\" class=\"data row8 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row8_col3\" class=\"data row8 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row8_col4\" class=\"data row8 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row8_col5\" class=\"data row8 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row8_col6\" class=\"data row8 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row8_col7\" class=\"data row8 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+                            "      <td id=\"T_682e2_row9_col0\" class=\"data row9 col0\" >xgb_50_depth2_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row9_col1\" class=\"data row9 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row9_col2\" class=\"data row9 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row9_col3\" class=\"data row9 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row9_col4\" class=\"data row9 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row9_col5\" class=\"data row9 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row9_col6\" class=\"data row9 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row9_col7\" class=\"data row9 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+                            "      <td id=\"T_682e2_row10_col0\" class=\"data row10 col0\" >xgb_100_depth2_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row10_col1\" class=\"data row10 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row10_col2\" class=\"data row10 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row10_col3\" class=\"data row10 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row10_col4\" class=\"data row10 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row10_col5\" class=\"data row10 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row10_col6\" class=\"data row10 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row10_col7\" class=\"data row10 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+                            "      <td id=\"T_682e2_row11_col0\" class=\"data row11 col0\" >xgb_25_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row11_col1\" class=\"data row11 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row11_col2\" class=\"data row11 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row11_col3\" class=\"data row11 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row11_col4\" class=\"data row11 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row11_col5\" class=\"data row11 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row11_col6\" class=\"data row11 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row11_col7\" class=\"data row11 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+                            "      <td id=\"T_682e2_row12_col0\" class=\"data row12 col0\" >xgb_50_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row12_col1\" class=\"data row12 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row12_col2\" class=\"data row12 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row12_col3\" class=\"data row12 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row12_col4\" class=\"data row12 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row12_col5\" class=\"data row12 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row12_col6\" class=\"data row12 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row12_col7\" class=\"data row12 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+                            "      <td id=\"T_682e2_row13_col0\" class=\"data row13 col0\" >xgb_100_depth2_lr0_02</td>\n",
+                            "      <td id=\"T_682e2_row13_col1\" class=\"data row13 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row13_col2\" class=\"data row13 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row13_col3\" class=\"data row13 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row13_col4\" class=\"data row13 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row13_col5\" class=\"data row13 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row13_col6\" class=\"data row13 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row13_col7\" class=\"data row13 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+                            "      <td id=\"T_682e2_row14_col0\" class=\"data row14 col0\" >xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td id=\"T_682e2_row14_col1\" class=\"data row14 col1\" >2000-2005</td>\n",
+                            "      <td id=\"T_682e2_row14_col2\" class=\"data row14 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row14_col3\" class=\"data row14 col3\" >-0.0025</td>\n",
+                            "      <td id=\"T_682e2_row14_col4\" class=\"data row14 col4\" >+74.7%</td>\n",
+                            "      <td id=\"T_682e2_row14_col5\" class=\"data row14 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row14_col6\" class=\"data row14 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row14_col7\" class=\"data row14 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+                            "      <td id=\"T_682e2_row15_col0\" class=\"data row15 col0\" >historical_frequency</td>\n",
+                            "      <td id=\"T_682e2_row15_col1\" class=\"data row15 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row15_col2\" class=\"data row15 col2\" >0.1895</td>\n",
+                            "      <td id=\"T_682e2_row15_col3\" class=\"data row15 col3\" >+0.0000</td>\n",
+                            "      <td id=\"T_682e2_row15_col4\" class=\"data row15 col4\" >+0.0%</td>\n",
+                            "      <td id=\"T_682e2_row15_col5\" class=\"data row15 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row15_col6\" class=\"data row15 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row15_col7\" class=\"data row15 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+                            "      <td id=\"T_682e2_row16_col0\" class=\"data row16 col0\" >logistic_c_0_001</td>\n",
+                            "      <td id=\"T_682e2_row16_col1\" class=\"data row16 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row16_col2\" class=\"data row16 col2\" >0.2001</td>\n",
+                            "      <td id=\"T_682e2_row16_col3\" class=\"data row16 col3\" >+0.0106</td>\n",
+                            "      <td id=\"T_682e2_row16_col4\" class=\"data row16 col4\" >-5.6%</td>\n",
+                            "      <td id=\"T_682e2_row16_col5\" class=\"data row16 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row16_col6\" class=\"data row16 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row16_col7\" class=\"data row16 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+                            "      <td id=\"T_682e2_row17_col0\" class=\"data row17 col0\" >logistic_c_0_003</td>\n",
+                            "      <td id=\"T_682e2_row17_col1\" class=\"data row17 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row17_col2\" class=\"data row17 col2\" >0.1951</td>\n",
+                            "      <td id=\"T_682e2_row17_col3\" class=\"data row17 col3\" >+0.0056</td>\n",
+                            "      <td id=\"T_682e2_row17_col4\" class=\"data row17 col4\" >-2.9%</td>\n",
+                            "      <td id=\"T_682e2_row17_col5\" class=\"data row17 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row17_col6\" class=\"data row17 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row17_col7\" class=\"data row17 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+                            "      <td id=\"T_682e2_row18_col0\" class=\"data row18 col0\" >logistic_c_0_01</td>\n",
+                            "      <td id=\"T_682e2_row18_col1\" class=\"data row18 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row18_col2\" class=\"data row18 col2\" >0.1778</td>\n",
+                            "      <td id=\"T_682e2_row18_col3\" class=\"data row18 col3\" >-0.0117</td>\n",
+                            "      <td id=\"T_682e2_row18_col4\" class=\"data row18 col4\" >+6.2%</td>\n",
+                            "      <td id=\"T_682e2_row18_col5\" class=\"data row18 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row18_col6\" class=\"data row18 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row18_col7\" class=\"data row18 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+                            "      <td id=\"T_682e2_row19_col0\" class=\"data row19 col0\" >logistic_c_0_03</td>\n",
+                            "      <td id=\"T_682e2_row19_col1\" class=\"data row19 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row19_col2\" class=\"data row19 col2\" >0.1578</td>\n",
+                            "      <td id=\"T_682e2_row19_col3\" class=\"data row19 col3\" >-0.0316</td>\n",
+                            "      <td id=\"T_682e2_row19_col4\" class=\"data row19 col4\" >+16.7%</td>\n",
+                            "      <td id=\"T_682e2_row19_col5\" class=\"data row19 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row19_col6\" class=\"data row19 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row19_col7\" class=\"data row19 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
+                            "      <td id=\"T_682e2_row20_col0\" class=\"data row20 col0\" >logistic_c_0_1</td>\n",
+                            "      <td id=\"T_682e2_row20_col1\" class=\"data row20 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row20_col2\" class=\"data row20 col2\" >0.1546</td>\n",
+                            "      <td id=\"T_682e2_row20_col3\" class=\"data row20 col3\" >-0.0349</td>\n",
+                            "      <td id=\"T_682e2_row20_col4\" class=\"data row20 col4\" >+18.4%</td>\n",
+                            "      <td id=\"T_682e2_row20_col5\" class=\"data row20 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row20_col6\" class=\"data row20 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row20_col7\" class=\"data row20 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
+                            "      <td id=\"T_682e2_row21_col0\" class=\"data row21 col0\" >logistic_c_1_0</td>\n",
+                            "      <td id=\"T_682e2_row21_col1\" class=\"data row21 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row21_col2\" class=\"data row21 col2\" >0.1390</td>\n",
+                            "      <td id=\"T_682e2_row21_col3\" class=\"data row21 col3\" >-0.0505</td>\n",
+                            "      <td id=\"T_682e2_row21_col4\" class=\"data row21 col4\" >+26.7%</td>\n",
+                            "      <td id=\"T_682e2_row21_col5\" class=\"data row21 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row21_col6\" class=\"data row21 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row21_col7\" class=\"data row21 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
+                            "      <td id=\"T_682e2_row22_col0\" class=\"data row22 col0\" >xgb_25_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row22_col1\" class=\"data row22 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row22_col2\" class=\"data row22 col2\" >0.2250</td>\n",
+                            "      <td id=\"T_682e2_row22_col3\" class=\"data row22 col3\" >+0.0355</td>\n",
+                            "      <td id=\"T_682e2_row22_col4\" class=\"data row22 col4\" >-18.7%</td>\n",
+                            "      <td id=\"T_682e2_row22_col5\" class=\"data row22 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row22_col6\" class=\"data row22 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row22_col7\" class=\"data row22 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
+                            "      <td id=\"T_682e2_row23_col0\" class=\"data row23 col0\" >xgb_50_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row23_col1\" class=\"data row23 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row23_col2\" class=\"data row23 col2\" >0.2332</td>\n",
+                            "      <td id=\"T_682e2_row23_col3\" class=\"data row23 col3\" >+0.0437</td>\n",
+                            "      <td id=\"T_682e2_row23_col4\" class=\"data row23 col4\" >-23.1%</td>\n",
+                            "      <td id=\"T_682e2_row23_col5\" class=\"data row23 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row23_col6\" class=\"data row23 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row23_col7\" class=\"data row23 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
+                            "      <td id=\"T_682e2_row24_col0\" class=\"data row24 col0\" >xgb_50_depth2_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row24_col1\" class=\"data row24 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row24_col2\" class=\"data row24 col2\" >0.2177</td>\n",
+                            "      <td id=\"T_682e2_row24_col3\" class=\"data row24 col3\" >+0.0282</td>\n",
+                            "      <td id=\"T_682e2_row24_col4\" class=\"data row24 col4\" >-14.9%</td>\n",
+                            "      <td id=\"T_682e2_row24_col5\" class=\"data row24 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row24_col6\" class=\"data row24 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row24_col7\" class=\"data row24 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
+                            "      <td id=\"T_682e2_row25_col0\" class=\"data row25 col0\" >xgb_100_depth2_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row25_col1\" class=\"data row25 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row25_col2\" class=\"data row25 col2\" >0.2212</td>\n",
+                            "      <td id=\"T_682e2_row25_col3\" class=\"data row25 col3\" >+0.0318</td>\n",
+                            "      <td id=\"T_682e2_row25_col4\" class=\"data row25 col4\" >-16.8%</td>\n",
+                            "      <td id=\"T_682e2_row25_col5\" class=\"data row25 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row25_col6\" class=\"data row25 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row25_col7\" class=\"data row25 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
+                            "      <td id=\"T_682e2_row26_col0\" class=\"data row26 col0\" >xgb_25_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row26_col1\" class=\"data row26 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row26_col2\" class=\"data row26 col2\" >0.2132</td>\n",
+                            "      <td id=\"T_682e2_row26_col3\" class=\"data row26 col3\" >+0.0237</td>\n",
+                            "      <td id=\"T_682e2_row26_col4\" class=\"data row26 col4\" >-12.5%</td>\n",
+                            "      <td id=\"T_682e2_row26_col5\" class=\"data row26 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row26_col6\" class=\"data row26 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row26_col7\" class=\"data row26 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
+                            "      <td id=\"T_682e2_row27_col0\" class=\"data row27 col0\" >xgb_50_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row27_col1\" class=\"data row27 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row27_col2\" class=\"data row27 col2\" >0.2283</td>\n",
+                            "      <td id=\"T_682e2_row27_col3\" class=\"data row27 col3\" >+0.0388</td>\n",
+                            "      <td id=\"T_682e2_row27_col4\" class=\"data row27 col4\" >-20.5%</td>\n",
+                            "      <td id=\"T_682e2_row27_col5\" class=\"data row27 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row27_col6\" class=\"data row27 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row27_col7\" class=\"data row27 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
+                            "      <td id=\"T_682e2_row28_col0\" class=\"data row28 col0\" >xgb_100_depth2_lr0_02</td>\n",
+                            "      <td id=\"T_682e2_row28_col1\" class=\"data row28 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row28_col2\" class=\"data row28 col2\" >0.2157</td>\n",
+                            "      <td id=\"T_682e2_row28_col3\" class=\"data row28 col3\" >+0.0262</td>\n",
+                            "      <td id=\"T_682e2_row28_col4\" class=\"data row28 col4\" >-13.8%</td>\n",
+                            "      <td id=\"T_682e2_row28_col5\" class=\"data row28 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row28_col6\" class=\"data row28 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row28_col7\" class=\"data row28 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
+                            "      <td id=\"T_682e2_row29_col0\" class=\"data row29 col0\" >xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td id=\"T_682e2_row29_col1\" class=\"data row29 col1\" >2006-2011</td>\n",
+                            "      <td id=\"T_682e2_row29_col2\" class=\"data row29 col2\" >0.2166</td>\n",
+                            "      <td id=\"T_682e2_row29_col3\" class=\"data row29 col3\" >+0.0271</td>\n",
+                            "      <td id=\"T_682e2_row29_col4\" class=\"data row29 col4\" >-14.3%</td>\n",
+                            "      <td id=\"T_682e2_row29_col5\" class=\"data row29 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row29_col6\" class=\"data row29 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row29_col7\" class=\"data row29 col7\" >5</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
+                            "      <td id=\"T_682e2_row30_col0\" class=\"data row30 col0\" >historical_frequency</td>\n",
+                            "      <td id=\"T_682e2_row30_col1\" class=\"data row30 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row30_col2\" class=\"data row30 col2\" >0.0047</td>\n",
+                            "      <td id=\"T_682e2_row30_col3\" class=\"data row30 col3\" >+0.0000</td>\n",
+                            "      <td id=\"T_682e2_row30_col4\" class=\"data row30 col4\" >+0.0%</td>\n",
+                            "      <td id=\"T_682e2_row30_col5\" class=\"data row30 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row30_col6\" class=\"data row30 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row30_col7\" class=\"data row30 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
+                            "      <td id=\"T_682e2_row31_col0\" class=\"data row31 col0\" >logistic_c_0_001</td>\n",
+                            "      <td id=\"T_682e2_row31_col1\" class=\"data row31 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row31_col2\" class=\"data row31 col2\" >0.0063</td>\n",
+                            "      <td id=\"T_682e2_row31_col3\" class=\"data row31 col3\" >+0.0016</td>\n",
+                            "      <td id=\"T_682e2_row31_col4\" class=\"data row31 col4\" >-33.5%</td>\n",
+                            "      <td id=\"T_682e2_row31_col5\" class=\"data row31 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row31_col6\" class=\"data row31 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row31_col7\" class=\"data row31 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
+                            "      <td id=\"T_682e2_row32_col0\" class=\"data row32 col0\" >logistic_c_0_003</td>\n",
+                            "      <td id=\"T_682e2_row32_col1\" class=\"data row32 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row32_col2\" class=\"data row32 col2\" >0.0054</td>\n",
+                            "      <td id=\"T_682e2_row32_col3\" class=\"data row32 col3\" >+0.0007</td>\n",
+                            "      <td id=\"T_682e2_row32_col4\" class=\"data row32 col4\" >-13.9%</td>\n",
+                            "      <td id=\"T_682e2_row32_col5\" class=\"data row32 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row32_col6\" class=\"data row32 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row32_col7\" class=\"data row32 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
+                            "      <td id=\"T_682e2_row33_col0\" class=\"data row33 col0\" >logistic_c_0_01</td>\n",
+                            "      <td id=\"T_682e2_row33_col1\" class=\"data row33 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row33_col2\" class=\"data row33 col2\" >0.0036</td>\n",
+                            "      <td id=\"T_682e2_row33_col3\" class=\"data row33 col3\" >-0.0011</td>\n",
+                            "      <td id=\"T_682e2_row33_col4\" class=\"data row33 col4\" >+23.9%</td>\n",
+                            "      <td id=\"T_682e2_row33_col5\" class=\"data row33 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row33_col6\" class=\"data row33 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row33_col7\" class=\"data row33 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
+                            "      <td id=\"T_682e2_row34_col0\" class=\"data row34 col0\" >logistic_c_0_03</td>\n",
+                            "      <td id=\"T_682e2_row34_col1\" class=\"data row34 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row34_col2\" class=\"data row34 col2\" >0.0023</td>\n",
+                            "      <td id=\"T_682e2_row34_col3\" class=\"data row34 col3\" >-0.0024</td>\n",
+                            "      <td id=\"T_682e2_row34_col4\" class=\"data row34 col4\" >+51.1%</td>\n",
+                            "      <td id=\"T_682e2_row34_col5\" class=\"data row34 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row34_col6\" class=\"data row34 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row34_col7\" class=\"data row34 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
+                            "      <td id=\"T_682e2_row35_col0\" class=\"data row35 col0\" >logistic_c_0_1</td>\n",
+                            "      <td id=\"T_682e2_row35_col1\" class=\"data row35 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row35_col2\" class=\"data row35 col2\" >0.0015</td>\n",
+                            "      <td id=\"T_682e2_row35_col3\" class=\"data row35 col3\" >-0.0032</td>\n",
+                            "      <td id=\"T_682e2_row35_col4\" class=\"data row35 col4\" >+68.7%</td>\n",
+                            "      <td id=\"T_682e2_row35_col5\" class=\"data row35 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row35_col6\" class=\"data row35 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row35_col7\" class=\"data row35 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
+                            "      <td id=\"T_682e2_row36_col0\" class=\"data row36 col0\" >logistic_c_1_0</td>\n",
+                            "      <td id=\"T_682e2_row36_col1\" class=\"data row36 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row36_col2\" class=\"data row36 col2\" >0.0007</td>\n",
+                            "      <td id=\"T_682e2_row36_col3\" class=\"data row36 col3\" >-0.0040</td>\n",
+                            "      <td id=\"T_682e2_row36_col4\" class=\"data row36 col4\" >+84.3%</td>\n",
+                            "      <td id=\"T_682e2_row36_col5\" class=\"data row36 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row36_col6\" class=\"data row36 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row36_col7\" class=\"data row36 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
+                            "      <td id=\"T_682e2_row37_col0\" class=\"data row37 col0\" >xgb_25_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row37_col1\" class=\"data row37 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row37_col2\" class=\"data row37 col2\" >0.0018</td>\n",
+                            "      <td id=\"T_682e2_row37_col3\" class=\"data row37 col3\" >-0.0029</td>\n",
+                            "      <td id=\"T_682e2_row37_col4\" class=\"data row37 col4\" >+62.1%</td>\n",
+                            "      <td id=\"T_682e2_row37_col5\" class=\"data row37 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row37_col6\" class=\"data row37 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row37_col7\" class=\"data row37 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
+                            "      <td id=\"T_682e2_row38_col0\" class=\"data row38 col0\" >xgb_50_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row38_col1\" class=\"data row38 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row38_col2\" class=\"data row38 col2\" >0.0007</td>\n",
+                            "      <td id=\"T_682e2_row38_col3\" class=\"data row38 col3\" >-0.0040</td>\n",
+                            "      <td id=\"T_682e2_row38_col4\" class=\"data row38 col4\" >+84.6%</td>\n",
+                            "      <td id=\"T_682e2_row38_col5\" class=\"data row38 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row38_col6\" class=\"data row38 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row38_col7\" class=\"data row38 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
+                            "      <td id=\"T_682e2_row39_col0\" class=\"data row39 col0\" >xgb_50_depth2_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row39_col1\" class=\"data row39 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row39_col2\" class=\"data row39 col2\" >0.0022</td>\n",
+                            "      <td id=\"T_682e2_row39_col3\" class=\"data row39 col3\" >-0.0026</td>\n",
+                            "      <td id=\"T_682e2_row39_col4\" class=\"data row39 col4\" >+54.2%</td>\n",
+                            "      <td id=\"T_682e2_row39_col5\" class=\"data row39 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row39_col6\" class=\"data row39 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row39_col7\" class=\"data row39 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
+                            "      <td id=\"T_682e2_row40_col0\" class=\"data row40 col0\" >xgb_100_depth2_lr0_05</td>\n",
+                            "      <td id=\"T_682e2_row40_col1\" class=\"data row40 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row40_col2\" class=\"data row40 col2\" >0.0008</td>\n",
+                            "      <td id=\"T_682e2_row40_col3\" class=\"data row40 col3\" >-0.0039</td>\n",
+                            "      <td id=\"T_682e2_row40_col4\" class=\"data row40 col4\" >+82.6%</td>\n",
+                            "      <td id=\"T_682e2_row40_col5\" class=\"data row40 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row40_col6\" class=\"data row40 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row40_col7\" class=\"data row40 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
+                            "      <td id=\"T_682e2_row41_col0\" class=\"data row41 col0\" >xgb_25_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row41_col1\" class=\"data row41 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row41_col2\" class=\"data row41 col2\" >0.0028</td>\n",
+                            "      <td id=\"T_682e2_row41_col3\" class=\"data row41 col3\" >-0.0019</td>\n",
+                            "      <td id=\"T_682e2_row41_col4\" class=\"data row41 col4\" >+39.6%</td>\n",
+                            "      <td id=\"T_682e2_row41_col5\" class=\"data row41 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row41_col6\" class=\"data row41 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row41_col7\" class=\"data row41 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
+                            "      <td id=\"T_682e2_row42_col0\" class=\"data row42 col0\" >xgb_50_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_682e2_row42_col1\" class=\"data row42 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row42_col2\" class=\"data row42 col2\" >0.0014</td>\n",
+                            "      <td id=\"T_682e2_row42_col3\" class=\"data row42 col3\" >-0.0033</td>\n",
+                            "      <td id=\"T_682e2_row42_col4\" class=\"data row42 col4\" >+70.2%</td>\n",
+                            "      <td id=\"T_682e2_row42_col5\" class=\"data row42 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row42_col6\" class=\"data row42 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row42_col7\" class=\"data row42 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
+                            "      <td id=\"T_682e2_row43_col0\" class=\"data row43 col0\" >xgb_100_depth2_lr0_02</td>\n",
+                            "      <td id=\"T_682e2_row43_col1\" class=\"data row43 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row43_col2\" class=\"data row43 col2\" >0.0019</td>\n",
+                            "      <td id=\"T_682e2_row43_col3\" class=\"data row43 col3\" >-0.0028</td>\n",
+                            "      <td id=\"T_682e2_row43_col4\" class=\"data row43 col4\" >+58.6%</td>\n",
+                            "      <td id=\"T_682e2_row43_col5\" class=\"data row43 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row43_col6\" class=\"data row43 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row43_col7\" class=\"data row43 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_682e2_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
+                            "      <td id=\"T_682e2_row44_col0\" class=\"data row44 col0\" >xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td id=\"T_682e2_row44_col1\" class=\"data row44 col1\" >2012-2017</td>\n",
+                            "      <td id=\"T_682e2_row44_col2\" class=\"data row44 col2\" >0.0056</td>\n",
+                            "      <td id=\"T_682e2_row44_col3\" class=\"data row44 col3\" >+0.0009</td>\n",
+                            "      <td id=\"T_682e2_row44_col4\" class=\"data row44 col4\" >-18.2%</td>\n",
+                            "      <td id=\"T_682e2_row44_col5\" class=\"data row44 col5\" >24</td>\n",
+                            "      <td id=\"T_682e2_row44_col6\" class=\"data row44 col6\" >0</td>\n",
+                            "      <td id=\"T_682e2_row44_col7\" class=\"data row44 col7\" >0</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n"
+                        ],
+                        "text/plain": [
+                            "<pandas.io.formats.style.Styler at 0x24fdbf05640>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<style type=\"text/css\">\n",
+                            "</style>\n",
+                            "<table id=\"T_c2720\">\n",
+                            "  <thead>\n",
+                            "    <tr>\n",
+                            "      <th class=\"blank level0\" >&nbsp;</th>\n",
+                            "      <th id=\"T_c2720_level0_col0\" class=\"col_heading level0 col0\" >candidate</th>\n",
+                            "      <th id=\"T_c2720_level0_col1\" class=\"col_heading level0 col1\" >mean_brier</th>\n",
+                            "      <th id=\"T_c2720_level0_col2\" class=\"col_heading level0 col2\" >delta_vs_baseline</th>\n",
+                            "      <th id=\"T_c2720_level0_col3\" class=\"col_heading level0 col3\" >brier_skill</th>\n",
+                            "      <th id=\"T_c2720_level0_col4\" class=\"col_heading level0 col4\" >scored</th>\n",
+                            "      <th id=\"T_c2720_level0_col5\" class=\"col_heading level0 col5\" >skipped</th>\n",
+                            "      <th id=\"T_c2720_level0_col6\" class=\"col_heading level0 col6\" >stress_events</th>\n",
+                            "      <th id=\"T_c2720_level0_col7\" class=\"col_heading level0 col7\" >folds_beating_baseline</th>\n",
+                            "      <th id=\"T_c2720_level0_col8\" class=\"col_heading level0 col8\" >event_folds</th>\n",
+                            "      <th id=\"T_c2720_level0_col9\" class=\"col_heading level0 col9\" >event_folds_beating_baseline</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+                            "      <td id=\"T_c2720_row0_col0\" class=\"data row0 col0\" >logistic_c_1_0</td>\n",
+                            "      <td id=\"T_c2720_row0_col1\" class=\"data row0 col1\" >0.0468</td>\n",
+                            "      <td id=\"T_c2720_row0_col2\" class=\"data row0 col2\" >-0.0190</td>\n",
+                            "      <td id=\"T_c2720_row0_col3\" class=\"data row0 col3\" >+28.8%</td>\n",
+                            "      <td id=\"T_c2720_row0_col4\" class=\"data row0 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row0_col5\" class=\"data row0 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row0_col6\" class=\"data row0 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row0_col7\" class=\"data row0 col7\" >3</td>\n",
+                            "      <td id=\"T_c2720_row0_col8\" class=\"data row0 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row0_col9\" class=\"data row0 col9\" >1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+                            "      <td id=\"T_c2720_row1_col0\" class=\"data row1 col0\" >logistic_c_0_1</td>\n",
+                            "      <td id=\"T_c2720_row1_col1\" class=\"data row1 col1\" >0.0523</td>\n",
+                            "      <td id=\"T_c2720_row1_col2\" class=\"data row1 col2\" >-0.0135</td>\n",
+                            "      <td id=\"T_c2720_row1_col3\" class=\"data row1 col3\" >+20.5%</td>\n",
+                            "      <td id=\"T_c2720_row1_col4\" class=\"data row1 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row1_col5\" class=\"data row1 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row1_col6\" class=\"data row1 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row1_col7\" class=\"data row1 col7\" >3</td>\n",
+                            "      <td id=\"T_c2720_row1_col8\" class=\"data row1 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row1_col9\" class=\"data row1 col9\" >1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+                            "      <td id=\"T_c2720_row2_col0\" class=\"data row2 col0\" >logistic_c_0_03</td>\n",
+                            "      <td id=\"T_c2720_row2_col1\" class=\"data row2 col1\" >0.0537</td>\n",
+                            "      <td id=\"T_c2720_row2_col2\" class=\"data row2 col2\" >-0.0122</td>\n",
+                            "      <td id=\"T_c2720_row2_col3\" class=\"data row2 col3\" >+18.5%</td>\n",
+                            "      <td id=\"T_c2720_row2_col4\" class=\"data row2 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row2_col5\" class=\"data row2 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row2_col6\" class=\"data row2 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row2_col7\" class=\"data row2 col7\" >3</td>\n",
+                            "      <td id=\"T_c2720_row2_col8\" class=\"data row2 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row2_col9\" class=\"data row2 col9\" >1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+                            "      <td id=\"T_c2720_row3_col0\" class=\"data row3 col0\" >logistic_c_0_01</td>\n",
+                            "      <td id=\"T_c2720_row3_col1\" class=\"data row3 col1\" >0.0607</td>\n",
+                            "      <td id=\"T_c2720_row3_col2\" class=\"data row3 col2\" >-0.0051</td>\n",
+                            "      <td id=\"T_c2720_row3_col3\" class=\"data row3 col3\" >+7.7%</td>\n",
+                            "      <td id=\"T_c2720_row3_col4\" class=\"data row3 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row3_col5\" class=\"data row3 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row3_col6\" class=\"data row3 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row3_col7\" class=\"data row3 col7\" >3</td>\n",
+                            "      <td id=\"T_c2720_row3_col8\" class=\"data row3 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row3_col9\" class=\"data row3 col9\" >1</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+                            "      <td id=\"T_c2720_row4_col0\" class=\"data row4 col0\" >historical_frequency</td>\n",
+                            "      <td id=\"T_c2720_row4_col1\" class=\"data row4 col1\" >0.0658</td>\n",
+                            "      <td id=\"T_c2720_row4_col2\" class=\"data row4 col2\" >+0.0000</td>\n",
+                            "      <td id=\"T_c2720_row4_col3\" class=\"data row4 col3\" >+0.0%</td>\n",
+                            "      <td id=\"T_c2720_row4_col4\" class=\"data row4 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row4_col5\" class=\"data row4 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row4_col6\" class=\"data row4 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row4_col7\" class=\"data row4 col7\" >0</td>\n",
+                            "      <td id=\"T_c2720_row4_col8\" class=\"data row4 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row4_col9\" class=\"data row4 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+                            "      <td id=\"T_c2720_row5_col0\" class=\"data row5 col0\" >logistic_c_0_003</td>\n",
+                            "      <td id=\"T_c2720_row5_col1\" class=\"data row5 col1\" >0.0671</td>\n",
+                            "      <td id=\"T_c2720_row5_col2\" class=\"data row5 col2\" >+0.0013</td>\n",
+                            "      <td id=\"T_c2720_row5_col3\" class=\"data row5 col3\" >-1.9%</td>\n",
+                            "      <td id=\"T_c2720_row5_col4\" class=\"data row5 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row5_col5\" class=\"data row5 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row5_col6\" class=\"data row5 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row5_col7\" class=\"data row5 col7\" >1</td>\n",
+                            "      <td id=\"T_c2720_row5_col8\" class=\"data row5 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row5_col9\" class=\"data row5 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+                            "      <td id=\"T_c2720_row6_col0\" class=\"data row6 col0\" >logistic_c_0_001</td>\n",
+                            "      <td id=\"T_c2720_row6_col1\" class=\"data row6 col1\" >0.0691</td>\n",
+                            "      <td id=\"T_c2720_row6_col2\" class=\"data row6 col2\" >+0.0032</td>\n",
+                            "      <td id=\"T_c2720_row6_col3\" class=\"data row6 col3\" >-4.9%</td>\n",
+                            "      <td id=\"T_c2720_row6_col4\" class=\"data row6 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row6_col5\" class=\"data row6 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row6_col6\" class=\"data row6 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row6_col7\" class=\"data row6 col7\" >1</td>\n",
+                            "      <td id=\"T_c2720_row6_col8\" class=\"data row6 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row6_col9\" class=\"data row6 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+                            "      <td id=\"T_c2720_row7_col0\" class=\"data row7 col0\" >xgb_25_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_c2720_row7_col1\" class=\"data row7 col1\" >0.0723</td>\n",
+                            "      <td id=\"T_c2720_row7_col2\" class=\"data row7 col2\" >+0.0065</td>\n",
+                            "      <td id=\"T_c2720_row7_col3\" class=\"data row7 col3\" >-9.8%</td>\n",
+                            "      <td id=\"T_c2720_row7_col4\" class=\"data row7 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row7_col5\" class=\"data row7 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row7_col6\" class=\"data row7 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row7_col7\" class=\"data row7 col7\" >2</td>\n",
+                            "      <td id=\"T_c2720_row7_col8\" class=\"data row7 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row7_col9\" class=\"data row7 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+                            "      <td id=\"T_c2720_row8_col0\" class=\"data row8 col0\" >xgb_100_depth2_lr0_02</td>\n",
+                            "      <td id=\"T_c2720_row8_col1\" class=\"data row8 col1\" >0.0728</td>\n",
+                            "      <td id=\"T_c2720_row8_col2\" class=\"data row8 col2\" >+0.0070</td>\n",
+                            "      <td id=\"T_c2720_row8_col3\" class=\"data row8 col3\" >-10.6%</td>\n",
+                            "      <td id=\"T_c2720_row8_col4\" class=\"data row8 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row8_col5\" class=\"data row8 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row8_col6\" class=\"data row8 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row8_col7\" class=\"data row8 col7\" >2</td>\n",
+                            "      <td id=\"T_c2720_row8_col8\" class=\"data row8 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row8_col9\" class=\"data row8 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+                            "      <td id=\"T_c2720_row9_col0\" class=\"data row9 col0\" >xgb_50_depth2_lr0_03</td>\n",
+                            "      <td id=\"T_c2720_row9_col1\" class=\"data row9 col1\" >0.0736</td>\n",
+                            "      <td id=\"T_c2720_row9_col2\" class=\"data row9 col2\" >+0.0077</td>\n",
+                            "      <td id=\"T_c2720_row9_col3\" class=\"data row9 col3\" >-11.7%</td>\n",
+                            "      <td id=\"T_c2720_row9_col4\" class=\"data row9 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row9_col5\" class=\"data row9 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row9_col6\" class=\"data row9 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row9_col7\" class=\"data row9 col7\" >2</td>\n",
+                            "      <td id=\"T_c2720_row9_col8\" class=\"data row9 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row9_col9\" class=\"data row9 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+                            "      <td id=\"T_c2720_row10_col0\" class=\"data row10 col0\" >xgb_100_depth2_lr0_05</td>\n",
+                            "      <td id=\"T_c2720_row10_col1\" class=\"data row10 col1\" >0.0743</td>\n",
+                            "      <td id=\"T_c2720_row10_col2\" class=\"data row10 col2\" >+0.0085</td>\n",
+                            "      <td id=\"T_c2720_row10_col3\" class=\"data row10 col3\" >-12.9%</td>\n",
+                            "      <td id=\"T_c2720_row10_col4\" class=\"data row10 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row10_col5\" class=\"data row10 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row10_col6\" class=\"data row10 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row10_col7\" class=\"data row10 col7\" >2</td>\n",
+                            "      <td id=\"T_c2720_row10_col8\" class=\"data row10 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row10_col9\" class=\"data row10 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+                            "      <td id=\"T_c2720_row11_col0\" class=\"data row11 col0\" >xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td id=\"T_c2720_row11_col1\" class=\"data row11 col1\" >0.0743</td>\n",
+                            "      <td id=\"T_c2720_row11_col2\" class=\"data row11 col2\" >+0.0085</td>\n",
+                            "      <td id=\"T_c2720_row11_col3\" class=\"data row11 col3\" >-12.9%</td>\n",
+                            "      <td id=\"T_c2720_row11_col4\" class=\"data row11 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row11_col5\" class=\"data row11 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row11_col6\" class=\"data row11 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row11_col7\" class=\"data row11 col7\" >1</td>\n",
+                            "      <td id=\"T_c2720_row11_col8\" class=\"data row11 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row11_col9\" class=\"data row11 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+                            "      <td id=\"T_c2720_row12_col0\" class=\"data row12 col0\" >xgb_25_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_c2720_row12_col1\" class=\"data row12 col1\" >0.0759</td>\n",
+                            "      <td id=\"T_c2720_row12_col2\" class=\"data row12 col2\" >+0.0100</td>\n",
+                            "      <td id=\"T_c2720_row12_col3\" class=\"data row12 col3\" >-15.2%</td>\n",
+                            "      <td id=\"T_c2720_row12_col4\" class=\"data row12 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row12_col5\" class=\"data row12 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row12_col6\" class=\"data row12 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row12_col7\" class=\"data row12 col7\" >2</td>\n",
+                            "      <td id=\"T_c2720_row12_col8\" class=\"data row12 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row12_col9\" class=\"data row12 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+                            "      <td id=\"T_c2720_row13_col0\" class=\"data row13 col0\" >xgb_50_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_c2720_row13_col1\" class=\"data row13 col1\" >0.0768</td>\n",
+                            "      <td id=\"T_c2720_row13_col2\" class=\"data row13 col2\" >+0.0110</td>\n",
+                            "      <td id=\"T_c2720_row13_col3\" class=\"data row13 col3\" >-16.7%</td>\n",
+                            "      <td id=\"T_c2720_row13_col4\" class=\"data row13 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row13_col5\" class=\"data row13 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row13_col6\" class=\"data row13 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row13_col7\" class=\"data row13 col7\" >2</td>\n",
+                            "      <td id=\"T_c2720_row13_col8\" class=\"data row13 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row13_col9\" class=\"data row13 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_c2720_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+                            "      <td id=\"T_c2720_row14_col0\" class=\"data row14 col0\" >xgb_50_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_c2720_row14_col1\" class=\"data row14 col1\" >0.0783</td>\n",
+                            "      <td id=\"T_c2720_row14_col2\" class=\"data row14 col2\" >+0.0124</td>\n",
+                            "      <td id=\"T_c2720_row14_col3\" class=\"data row14 col3\" >-18.9%</td>\n",
+                            "      <td id=\"T_c2720_row14_col4\" class=\"data row14 col4\" >72</td>\n",
+                            "      <td id=\"T_c2720_row14_col5\" class=\"data row14 col5\" >0</td>\n",
+                            "      <td id=\"T_c2720_row14_col6\" class=\"data row14 col6\" >5</td>\n",
+                            "      <td id=\"T_c2720_row14_col7\" class=\"data row14 col7\" >2</td>\n",
+                            "      <td id=\"T_c2720_row14_col8\" class=\"data row14 col8\" >1</td>\n",
+                            "      <td id=\"T_c2720_row14_col9\" class=\"data row14 col9\" >0</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n"
+                        ],
+                        "text/plain": [
+                            "<pandas.io.formats.style.Styler at 0x24fdc4036e0>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Tuning candidate passing the stability rule: logistic_c_1_0\n",
+                        "Caution: stress events appear in fewer than two folds; treat this candidate as provisional.\n",
+                        "To reproduce the historical diagnostic, set:\n",
+                        "STAGE = \"confirm\"\n",
+                        "CANDIDATE = \"logistic_c_1_0\"\n",
+                        "BACKTEST_STRIDE = 3\n",
+                        "The 2018–2024 window has already been inspected and is not an untouched confirmation set.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "if STAGE == \"tune\" and fold_results is not None:\n",
+                "    fold_table, pooled_table, origin_details = parameter_smoke.tuning_summary_tables(fold_results, service=service)\n",
+                "    ipython_display(\n",
+                "        fold_table.style.format(\n",
+                "            {\n",
+                "                \"mean_brier\": \"{:.4f}\",\n",
+                "                \"delta_vs_baseline\": \"{:+.4f}\",\n",
+                "                \"brier_skill\": \"{:+.1%}\",\n",
+                "            }\n",
+                "        )\n",
+                "    )\n",
+                "    ipython_display(\n",
+                "        pooled_table.style.format(\n",
+                "            {\n",
+                "                \"mean_brier\": \"{:.4f}\",\n",
+                "                \"delta_vs_baseline\": \"{:+.4f}\",\n",
+                "                \"brier_skill\": \"{:+.1%}\",\n",
+                "            }\n",
+                "        )\n",
+                "    )\n",
+                "\n",
+                "    selected_model = parameter_smoke.select_tuning_candidate(pooled_table)\n",
+                "    if selected_model is None:\n",
+                "        print(\n",
+                "            \"No candidate beat historical frequency overall, in at least two folds, \"\n",
+                "            \"and in an event-bearing fold. Keep historical frequency.\"\n",
+                "        )\n",
+                "    else:\n",
+                "        selected_candidate = str(selected_model[\"candidate\"])\n",
+                "        print(f\"Tuning candidate passing the stability rule: {selected_candidate}\")\n",
+                "        if int(selected_model[\"event_folds\"]) < 2:\n",
+                "            print(\n",
+                "                \"Caution: stress events appear in fewer than two folds; treat this candidate as provisional.\"\n",
+                "            )\n",
+                "        print(\"To reproduce the historical diagnostic, set:\")\n",
+                "        print('STAGE = \"confirm\"')\n",
+                "        print(f'CANDIDATE = \"{selected_candidate}\"')\n",
+                "        print(f\"BACKTEST_STRIDE = {BACKTEST_STRIDE}\")\n",
+                "        print(\"The 2018–2024 window has already been inspected and is not an untouched confirmation set.\")\n",
+                "\n",
+                "    if SHOW_ORIGIN_DETAILS:\n",
+                "        ipython_display(origin_details)\n",
+                "elif STAGE == \"confirm\" and table is not None:\n",
+                "    ipython_display(\n",
+                "        table.style.format(\n",
+                "            {\n",
+                "                \"mean_brier\": \"{:.4f}\",\n",
+                "                \"delta_vs_baseline\": \"{:+.4f}\",\n",
+                "                \"brier_skill\": \"{:+.1%}\",\n",
+                "            }\n",
+                "        )\n",
+                "    )\n",
+                "    print(f\"Historical diagnostic completed for {CANDIDATE}; do not tune against this window.\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "8dd6d1a3",
+            "metadata": {
+                "id": "8dd6d1a3",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Saved plot: C:\\Users\\Mina Attari\\BMO-C2-agentic-forecasting\\implementations\\manufacturing_stress_forecasting\\implementations\\manufacturing_stress_forecasting\\reports\\parameter_sweep_results.png\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 2200x700 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "import matplotlib.pyplot as plt\n",
+                "from pathlib import Path\n",
+                "\n",
+                "if pooled_table is None or fold_table is None:\n",
+                "    raise RuntimeError(\"Run the tuning sweep and comparison cell before creating plots.\")\n",
+                "\n",
+                "plot_pooled = pooled_table.sort_values(\"mean_brier\", ascending=True).copy()\n",
+                "plot_folds = fold_table.copy()\n",
+                "\n",
+                "fig, axes = plt.subplots(1, 3, figsize=(22, 7), constrained_layout=True)\n",
+                "\n",
+                "plot_pooled.plot.barh(\n",
+                "    x=\"candidate\",\n",
+                "    y=\"mean_brier\",\n",
+                "    ax=axes[0],\n",
+                "    legend=False,\n",
+                "    color=\"#2f6f8f\",\n",
+                ")\n",
+                "axes[0].invert_yaxis()\n",
+                "axes[0].set_title(\"Pooled Brier score\")\n",
+                "axes[0].set_xlabel(\"Mean Brier score; lower is better\")\n",
+                "axes[0].set_ylabel(\"\")\n",
+                "\n",
+                "fold_pivot = plot_folds.pivot(index=\"candidate\", columns=\"fold\", values=\"mean_brier\")\n",
+                "fold_pivot = fold_pivot.reindex(plot_pooled[\"candidate\"])\n",
+                "fold_pivot.plot.barh(ax=axes[1], width=0.8)\n",
+                "axes[1].invert_yaxis()\n",
+                "axes[1].set_title(\"Brier score by chronological fold\")\n",
+                "axes[1].set_xlabel(\"Mean Brier score; lower is better\")\n",
+                "axes[1].set_ylabel(\"\")\n",
+                "axes[1].legend(title=\"Fold\", fontsize=8)\n",
+                "\n",
+                "plot_pooled.plot.barh(\n",
+                "    x=\"candidate\",\n",
+                "    y=\"brier_skill\",\n",
+                "    ax=axes[2],\n",
+                "    legend=False,\n",
+                "    color=plot_pooled[\"brier_skill\"].ge(0).map({True: \"#3a8f6b\", False: \"#b85c5c\"}),\n",
+                ")\n",
+                "axes[2].axvline(0.0, color=\"black\", linewidth=1)\n",
+                "axes[2].invert_yaxis()\n",
+                "axes[2].set_title(\"Pooled Brier skill vs baseline\")\n",
+                "axes[2].set_xlabel(\"Skill; positive is better\")\n",
+                "axes[2].set_ylabel(\"\")\n",
+                "\n",
+                "for axis in axes:\n",
+                "    axis.tick_params(axis=\"y\", labelsize=8)\n",
+                "    axis.grid(axis=\"x\", alpha=0.25)\n",
+                "\n",
+                "report_dir = Path(REPO_ROOT) / \"implementations\" / \"manufacturing_stress_forecasting\" / \"reports\" / \"param_sweep\"\n",
+                "report_dir.mkdir(parents=True, exist_ok=True)\n",
+                "figure_path = report_dir / \"parameter_sweep_results.png\"\n",
+                "fig.savefig(figure_path, dpi=180, bbox_inches=\"tight\")\n",
+                "print(f\"Saved plot: {figure_path.resolve()}\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "500c9b75",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Evaluating 15 models on the historical confirmation window 2018-01-01 to 2024-12-01\n",
+                        "Running historical_frequency...\n",
+                        "Running logistic_c_0_001...\n",
+                        "Running logistic_c_0_003...\n",
+                        "Running logistic_c_0_01...\n",
+                        "Running logistic_c_0_03...\n",
+                        "Running logistic_c_0_1...\n",
+                        "Running logistic_c_1_0...\n",
+                        "Running xgb_25_depth1_lr0_05...\n",
+                        "Running xgb_50_depth1_lr0_05...\n",
+                        "Running xgb_50_depth2_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_05...\n",
+                        "Running xgb_25_depth1_lr0_03...\n",
+                        "Running xgb_50_depth1_lr0_03...\n",
+                        "Running xgb_100_depth2_lr0_02...\n",
+                        "Running xgb_50_depth2_lr0_03_minchild3_l2_5...\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<style type=\"text/css\">\n",
+                            "</style>\n",
+                            "<table id=\"T_4415b\">\n",
+                            "  <thead>\n",
+                            "    <tr>\n",
+                            "      <th class=\"blank level0\" >&nbsp;</th>\n",
+                            "      <th id=\"T_4415b_level0_col0\" class=\"col_heading level0 col0\" >candidate</th>\n",
+                            "      <th id=\"T_4415b_level0_col1\" class=\"col_heading level0 col1\" >mean_brier</th>\n",
+                            "      <th id=\"T_4415b_level0_col2\" class=\"col_heading level0 col2\" >delta_vs_baseline</th>\n",
+                            "      <th id=\"T_4415b_level0_col3\" class=\"col_heading level0 col3\" >brier_skill</th>\n",
+                            "      <th id=\"T_4415b_level0_col4\" class=\"col_heading level0 col4\" >scored</th>\n",
+                            "      <th id=\"T_4415b_level0_col5\" class=\"col_heading level0 col5\" >skipped</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+                            "      <td id=\"T_4415b_row0_col0\" class=\"data row0 col0\" >historical_frequency</td>\n",
+                            "      <td id=\"T_4415b_row0_col1\" class=\"data row0 col1\" >0.0356</td>\n",
+                            "      <td id=\"T_4415b_row0_col2\" class=\"data row0 col2\" >+0.0000</td>\n",
+                            "      <td id=\"T_4415b_row0_col3\" class=\"data row0 col3\" >+0.0%</td>\n",
+                            "      <td id=\"T_4415b_row0_col4\" class=\"data row0 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row0_col5\" class=\"data row0 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+                            "      <td id=\"T_4415b_row1_col0\" class=\"data row1 col0\" >logistic_c_0_001</td>\n",
+                            "      <td id=\"T_4415b_row1_col1\" class=\"data row1 col1\" >0.0380</td>\n",
+                            "      <td id=\"T_4415b_row1_col2\" class=\"data row1 col2\" >+0.0024</td>\n",
+                            "      <td id=\"T_4415b_row1_col3\" class=\"data row1 col3\" >-6.7%</td>\n",
+                            "      <td id=\"T_4415b_row1_col4\" class=\"data row1 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row1_col5\" class=\"data row1 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+                            "      <td id=\"T_4415b_row2_col0\" class=\"data row2 col0\" >xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td id=\"T_4415b_row2_col1\" class=\"data row2 col1\" >0.0422</td>\n",
+                            "      <td id=\"T_4415b_row2_col2\" class=\"data row2 col2\" >+0.0066</td>\n",
+                            "      <td id=\"T_4415b_row2_col3\" class=\"data row2 col3\" >-18.4%</td>\n",
+                            "      <td id=\"T_4415b_row2_col4\" class=\"data row2 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row2_col5\" class=\"data row2 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+                            "      <td id=\"T_4415b_row3_col0\" class=\"data row3 col0\" >xgb_25_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_4415b_row3_col1\" class=\"data row3 col1\" >0.0426</td>\n",
+                            "      <td id=\"T_4415b_row3_col2\" class=\"data row3 col2\" >+0.0070</td>\n",
+                            "      <td id=\"T_4415b_row3_col3\" class=\"data row3 col3\" >-19.7%</td>\n",
+                            "      <td id=\"T_4415b_row3_col4\" class=\"data row3 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row3_col5\" class=\"data row3 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+                            "      <td id=\"T_4415b_row4_col0\" class=\"data row4 col0\" >xgb_25_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_4415b_row4_col1\" class=\"data row4 col1\" >0.0485</td>\n",
+                            "      <td id=\"T_4415b_row4_col2\" class=\"data row4 col2\" >+0.0129</td>\n",
+                            "      <td id=\"T_4415b_row4_col3\" class=\"data row4 col3\" >-36.2%</td>\n",
+                            "      <td id=\"T_4415b_row4_col4\" class=\"data row4 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row4_col5\" class=\"data row4 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+                            "      <td id=\"T_4415b_row5_col0\" class=\"data row5 col0\" >logistic_c_0_003</td>\n",
+                            "      <td id=\"T_4415b_row5_col1\" class=\"data row5 col1\" >0.0516</td>\n",
+                            "      <td id=\"T_4415b_row5_col2\" class=\"data row5 col2\" >+0.0160</td>\n",
+                            "      <td id=\"T_4415b_row5_col3\" class=\"data row5 col3\" >-45.0%</td>\n",
+                            "      <td id=\"T_4415b_row5_col4\" class=\"data row5 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row5_col5\" class=\"data row5 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+                            "      <td id=\"T_4415b_row6_col0\" class=\"data row6 col0\" >xgb_50_depth1_lr0_03</td>\n",
+                            "      <td id=\"T_4415b_row6_col1\" class=\"data row6 col1\" >0.0521</td>\n",
+                            "      <td id=\"T_4415b_row6_col2\" class=\"data row6 col2\" >+0.0165</td>\n",
+                            "      <td id=\"T_4415b_row6_col3\" class=\"data row6 col3\" >-46.3%</td>\n",
+                            "      <td id=\"T_4415b_row6_col4\" class=\"data row6 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row6_col5\" class=\"data row6 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+                            "      <td id=\"T_4415b_row7_col0\" class=\"data row7 col0\" >xgb_50_depth2_lr0_03</td>\n",
+                            "      <td id=\"T_4415b_row7_col1\" class=\"data row7 col1\" >0.0643</td>\n",
+                            "      <td id=\"T_4415b_row7_col2\" class=\"data row7 col2\" >+0.0287</td>\n",
+                            "      <td id=\"T_4415b_row7_col3\" class=\"data row7 col3\" >-80.7%</td>\n",
+                            "      <td id=\"T_4415b_row7_col4\" class=\"data row7 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row7_col5\" class=\"data row7 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+                            "      <td id=\"T_4415b_row8_col0\" class=\"data row8 col0\" >xgb_50_depth1_lr0_05</td>\n",
+                            "      <td id=\"T_4415b_row8_col1\" class=\"data row8 col1\" >0.0682</td>\n",
+                            "      <td id=\"T_4415b_row8_col2\" class=\"data row8 col2\" >+0.0326</td>\n",
+                            "      <td id=\"T_4415b_row8_col3\" class=\"data row8 col3\" >-91.5%</td>\n",
+                            "      <td id=\"T_4415b_row8_col4\" class=\"data row8 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row8_col5\" class=\"data row8 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+                            "      <td id=\"T_4415b_row9_col0\" class=\"data row9 col0\" >logistic_c_0_01</td>\n",
+                            "      <td id=\"T_4415b_row9_col1\" class=\"data row9 col1\" >0.0716</td>\n",
+                            "      <td id=\"T_4415b_row9_col2\" class=\"data row9 col2\" >+0.0360</td>\n",
+                            "      <td id=\"T_4415b_row9_col3\" class=\"data row9 col3\" >-101.0%</td>\n",
+                            "      <td id=\"T_4415b_row9_col4\" class=\"data row9 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row9_col5\" class=\"data row9 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+                            "      <td id=\"T_4415b_row10_col0\" class=\"data row10 col0\" >xgb_100_depth2_lr0_02</td>\n",
+                            "      <td id=\"T_4415b_row10_col1\" class=\"data row10 col1\" >0.0749</td>\n",
+                            "      <td id=\"T_4415b_row10_col2\" class=\"data row10 col2\" >+0.0393</td>\n",
+                            "      <td id=\"T_4415b_row10_col3\" class=\"data row10 col3\" >-110.3%</td>\n",
+                            "      <td id=\"T_4415b_row10_col4\" class=\"data row10 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row10_col5\" class=\"data row10 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+                            "      <td id=\"T_4415b_row11_col0\" class=\"data row11 col0\" >logistic_c_0_03</td>\n",
+                            "      <td id=\"T_4415b_row11_col1\" class=\"data row11 col1\" >0.0866</td>\n",
+                            "      <td id=\"T_4415b_row11_col2\" class=\"data row11 col2\" >+0.0510</td>\n",
+                            "      <td id=\"T_4415b_row11_col3\" class=\"data row11 col3\" >-143.2%</td>\n",
+                            "      <td id=\"T_4415b_row11_col4\" class=\"data row11 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row11_col5\" class=\"data row11 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+                            "      <td id=\"T_4415b_row12_col0\" class=\"data row12 col0\" >xgb_100_depth2_lr0_05</td>\n",
+                            "      <td id=\"T_4415b_row12_col1\" class=\"data row12 col1\" >0.1036</td>\n",
+                            "      <td id=\"T_4415b_row12_col2\" class=\"data row12 col2\" >+0.0680</td>\n",
+                            "      <td id=\"T_4415b_row12_col3\" class=\"data row12 col3\" >-191.0%</td>\n",
+                            "      <td id=\"T_4415b_row12_col4\" class=\"data row12 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row12_col5\" class=\"data row12 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+                            "      <td id=\"T_4415b_row13_col0\" class=\"data row13 col0\" >logistic_c_0_1</td>\n",
+                            "      <td id=\"T_4415b_row13_col1\" class=\"data row13 col1\" >0.1314</td>\n",
+                            "      <td id=\"T_4415b_row13_col2\" class=\"data row13 col2\" >+0.0958</td>\n",
+                            "      <td id=\"T_4415b_row13_col3\" class=\"data row13 col3\" >-269.1%</td>\n",
+                            "      <td id=\"T_4415b_row13_col4\" class=\"data row13 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row13_col5\" class=\"data row13 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th id=\"T_4415b_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+                            "      <td id=\"T_4415b_row14_col0\" class=\"data row14 col0\" >logistic_c_1_0</td>\n",
+                            "      <td id=\"T_4415b_row14_col1\" class=\"data row14 col1\" >0.2327</td>\n",
+                            "      <td id=\"T_4415b_row14_col2\" class=\"data row14 col2\" >+0.1971</td>\n",
+                            "      <td id=\"T_4415b_row14_col3\" class=\"data row14 col3\" >-553.7%</td>\n",
+                            "      <td id=\"T_4415b_row14_col4\" class=\"data row14 col4\" >28</td>\n",
+                            "      <td id=\"T_4415b_row14_col5\" class=\"data row14 col5\" >0</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n"
+                        ],
+                        "text/plain": [
+                            "<pandas.io.formats.style.Styler at 0x24fe2ad6a80>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Saved confirmation table to C:\\Users\\Mina Attari\\BMO-C2-agentic-forecasting\\reports\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from pathlib import Path\n",
+                "\n",
+                "confirmation_spec = parameter_smoke.build_experiment_spec(\n",
+                "    base_spec,\n",
+                "    stage=\"confirm\",\n",
+                "    stride=BACKTEST_STRIDE,\n",
+                ")\n",
+                "confirmation_predictors = parameter_smoke.predictors_for_stage(\"tune\")\n",
+                "print(\n",
+                "    f\"Evaluating {len(confirmation_predictors)} models on the historical confirmation window \"\n",
+                "    f\"{confirmation_spec.start.date()} to {confirmation_spec.end.date()}\"\n",
+                ")\n",
+                "confirmation_results = parameter_smoke.run_candidates(\n",
+                "    confirmation_predictors,\n",
+                "    spec=confirmation_spec,\n",
+                "    service=service,\n",
+                ")\n",
+                "confirmation_table = parameter_smoke.comparison_table(confirmation_results)\n",
+                "\n",
+                "report_dir = Path(REPO_ROOT) / \"implementations\" / \"manufacturing_stress_forecasting\" / \"reports\"\n",
+                "report_dir.mkdir(parents=True, exist_ok=True)\n",
+                "confirmation_table.to_csv(report_dir / \"confirmation_2018_2024_results.csv\", index=False)\n",
+                "confirmation_table.to_markdown(\n",
+                "    report_dir / \"confirmation_2018_2024_results.md\",\n",
+                "    index=False,\n",
+                "    floatfmt=\".4f\",\n",
+                ")\n",
+                "ipython_display(\n",
+                "    confirmation_table.style.format(\n",
+                "        {\n",
+                "            \"mean_brier\": \"{:.4f}\",\n",
+                "            \"delta_vs_baseline\": \"{:+.4f}\",\n",
+                "            \"brier_skill\": \"{:+.1%}\",\n",
+                "        }\n",
+                "    )\n",
+                ")\n",
+                "print(f\"Saved confirmation table to {report_dir}\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 17,
+            "id": "cf4a6efe",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Saved confirmation plot: C:\\Users\\Mina Attari\\BMO-C2-agentic-forecasting\\implementations\\manufacturing_stress_forecasting\\reports\\confirmation_2018_2024_results.png\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1800x800 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "import matplotlib.pyplot as plt\n",
+                "from pathlib import Path\n",
+                "\n",
+                "if \"confirmation_table\" not in globals() or confirmation_table is None:\n",
+                "    raise RuntimeError(\"Run the confirmation-results cell before creating confirmation plots.\")\n",
+                "\n",
+                "plot_confirmation = confirmation_table.sort_values(\"mean_brier\", ascending=True).copy()\n",
+                "plot_confirmation[\"color\"] = plot_confirmation[\"brier_skill\"].ge(0).map(\n",
+                "    {True: \"#3a8f6b\", False: \"#b85c5c\"}\n",
+                ")\n",
+                "\n",
+                "fig, axes = plt.subplots(1, 2, figsize=(18, 8), constrained_layout=True)\n",
+                "\n",
+                "plot_confirmation.plot.barh(\n",
+                "    x=\"candidate\",\n",
+                "    y=\"mean_brier\",\n",
+                "    ax=axes[0],\n",
+                "    legend=False,\n",
+                "    color=plot_confirmation[\"color\"],\n",
+                ")\n",
+                "axes[0].invert_yaxis()\n",
+                "axes[0].set_title(\"2018-2024 confirmation Brier score\")\n",
+                "axes[0].set_xlabel(\"Mean Brier score; lower is better\")\n",
+                "axes[0].set_ylabel(\"\")\n",
+                "\n",
+                "plot_confirmation.plot.barh(\n",
+                "    x=\"candidate\",\n",
+                "    y=\"brier_skill\",\n",
+                "    ax=axes[1],\n",
+                "    legend=False,\n",
+                "    color=plot_confirmation[\"color\"],\n",
+                ")\n",
+                "axes[1].axvline(0.0, color=\"black\", linewidth=1)\n",
+                "axes[1].invert_yaxis()\n",
+                "axes[1].set_title(\"2018-2024 Brier skill vs baseline\")\n",
+                "axes[1].set_xlabel(\"Skill; positive is better\")\n",
+                "axes[1].set_ylabel(\"\")\n",
+                "\n",
+                "for axis in axes:\n",
+                "    axis.tick_params(axis=\"y\", labelsize=8)\n",
+                "    axis.grid(axis=\"x\", alpha=0.25)\n",
+                "\n",
+                "report_dir = Path(REPO_ROOT) / \"implementations\" / \"manufacturing_stress_forecasting\" / \"reports\"\n",
+                "report_dir.mkdir(parents=True, exist_ok=True)\n",
+                "figure_path = report_dir / \"confirmation_2018_2024_results.png\"\n",
+                "fig.savefig(figure_path, dpi=180, bbox_inches=\"tight\")\n",
+                "print(f\"Saved confirmation plot: {figure_path}\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "f60bb12c",
+            "metadata": {
+                "id": "f60bb12c",
+                "language": "markdown"
+            },
+            "source": [
+                "## Confirmation discipline\n",
+                "\n",
+                "The existing `confirm` stage evaluates one candidate on 2018–2024, but that period has already been inspected. Use it only to reproduce or diagnose the historical result; do not choose parameters from it. For a fresh confirmation, freeze the candidate first and evaluate it only on future or prospectively recorded outcomes."
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "agentic-forecasting-bootcamp (3.12.14.final.0)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.12.14"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_protected_evaluation_workbench.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_protected_evaluation_workbench.ipynb
new file mode 100644
index 000000000..357f33852
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_protected_evaluation_workbench.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8682c20c",
+   "metadata": {},
+   "source": [
+    "# Manufacturing Stress: Protected Evaluation\n",
+    "\n",
+    "Limited held-out evaluation. Every successful predictor run consumes one tracked budget unit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0a4d78e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "import yaml\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "REPO_ROOT = next((p for p in (Path.cwd().resolve(), *Path.cwd().resolve().parents) if (p / 'uv.lock').exists() and (p / 'implementations').is_dir()), None)\n",
+    "if REPO_ROOT is None:\n",
+    "    raise FileNotFoundError('Repository root not found.')\n",
+    "for package_root in (REPO_ROOT / 'aieng-forecasting', REPO_ROOT / 'implementations'):\n",
+    "    if str(package_root) not in sys.path:\n",
+    "        sys.path.insert(0, str(package_root))\n",
+    "load_dotenv(REPO_ROOT / '.env', override=False)\n",
+    "\n",
+    "from aieng.forecasting.evaluation import EvalSpec, EvalTracker, EvalBudgetExceededError, evaluate\n",
+    "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+    "from manufacturing_stress_forecasting.hybrid_agent.agent import build_hybrid_agent_predictor\n",
+    "from manufacturing_stress_forecasting.predictors import ManufacturingStressLogisticPredictor\n",
+    "from manufacturing_stress_forecasting.adaptive_agent.agent import build_manufacturing_adaptive_agent_predictor\n",
+    "\n",
+    "RUN_PROTECTED_EVAL = False\n",
+    "PREDICTOR_CHOICE = 'logistic'  # logistic, hybrid, adaptive_lite, or adaptive_advanced\n",
+    "SPEC_PATH = REPO_ROOT / 'implementations' / 'manufacturing_stress_forecasting' / 'specs' / 'manufacturing_stress_protected_eval.yaml'\n",
+    "TRACKER_PATH = REPO_ROOT / 'data' / 'predictions' / 'manufacturing_stress_protected_evaluation' / 'eval_runs.yaml'\n",
+    "TRACKER_PATH.parent.mkdir(parents=True, exist_ok=True)\n",
+    "eval_spec = EvalSpec.model_validate(yaml.safe_load(SPEC_PATH.read_text(encoding='utf-8')))\n",
+    "tracker = EvalTracker(TRACKER_PATH)\n",
+    "service = build_manufacturing_stress_service()\n",
+    "runs_used = tracker.runs_for(eval_spec.spec_id)\n",
+    "print({'spec_id': eval_spec.spec_id, 'runs_used': runs_used, 'remaining': max(0, (eval_spec.max_runs or 0) - runs_used), 'run': RUN_PROTECTED_EVAL})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71639943",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if RUN_PROTECTED_EVAL:\n",
+    "    if PREDICTOR_CHOICE == 'logistic':\n",
+    "        predictor = ManufacturingStressLogisticPredictor()\n",
+    "        trace_status = 'not applicable: deterministic predictor'\n",
+    "    elif PREDICTOR_CHOICE == 'hybrid':\n",
+    "        predictor = build_hybrid_agent_predictor()\n",
+    "        trace_status = 'decision metadata retained; Langfuse tracing is disabled for hybrid'\n",
+    "    elif PREDICTOR_CHOICE == 'adaptive_lite':\n",
+    "        predictor = build_manufacturing_adaptive_agent_predictor(model='gemini-3.1-flash-lite-preview')\n",
+    "        trace_status = 'Langfuse tracing enabled when credentials are configured'\n",
+    "    elif PREDICTOR_CHOICE == 'adaptive_advanced':\n",
+    "        predictor = build_manufacturing_adaptive_agent_predictor(model='gemini-3.5-flash')\n",
+    "        trace_status = 'Langfuse tracing enabled when credentials are configured'\n",
+    "    else:\n",
+    "        raise ValueError(f'Unknown predictor choice: {PREDICTOR_CHOICE}')\n",
+    "    result = evaluate(predictor, eval_spec, service, tracker)\n",
+    "    decision_rows = []\n",
+    "    for prediction, score in zip(result.predictions, result.scores):\n",
+    "        decision = dict(prediction.metadata)\n",
+    "        decision_rows.append({\n",
+    "            'as_of': prediction.as_of,\n",
+    "            'forecast_date': prediction.forecast_date,\n",
+    "            'probability': prediction.payload.probability,\n",
+    "            'brier': score,\n",
+    "            'rationale': decision.get('rationale'),\n",
+    "            'direction': decision.get('direction'),\n",
+    "            'anchor_probability': decision.get('anchor_probability'),\n",
+    "            'agent_probability': decision.get('agent_probability'),\n",
+    "            'adjusted_probability': decision.get('adjusted_probability'),\n",
+    "            'supporting_evidence': decision.get('supporting_evidence'),\n",
+    "            'countervailing_evidence': decision.get('countervailing_evidence'),\n",
+    "        })\n",
+    "    decision_trace = pd.DataFrame(decision_rows)\n",
+    "    print({'predictor_id': result.predictor_id, 'run_number': result.run_number, 'mean_score': result.mean_score, 'trace_status': trace_status})\n",
+    "    display(decision_trace)\n",
+    "else:\n",
+    "    print('RUN_PROTECTED_EVAL = False; no protected evaluation consumed.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c70c818f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "if 'result' in globals():\n",
+    "    score_frame = pd.DataFrame([{\n",
+    "        'predictor': result.predictor_id,\n",
+    "        'mean_brier': float(result.mean_score),\n",
+    "        'run_number': result.run_number,\n",
+    "    }])\n",
+    "    ax = score_frame.plot.bar(x='predictor', y='mean_brier', legend=False, figsize=(7, 4), color='#2f6f8f')\n",
+    "    ax.set_title('Protected evaluation score')\n",
+    "    ax.set_ylabel('Mean Brier score (lower is better)')\n",
+    "    ax.set_xlabel('')\n",
+    "    ax.grid(axis='y', alpha=0.25)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print('Run the protected evaluation to render the score plot.')"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_workbench.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_workbench.ipynb
new file mode 100644
index 000000000..908b3b1a3
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_workbench.ipynb
@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "988dada7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running: uv sync --all-extras --dev --all-packages\n",
+      "\n",
+      "Environment synchronized successfully.\n",
+      "If packages changed, restart the notebook kernel before continuing.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cell 0: synchronize the repository environment before running the workbench.\n",
+    "import subprocess\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "def find_repo_root(start: Path) -> Path:\n",
+    "    \"\"\"Find the workspace root without confusing member pyproject files for it.\"\"\"\n",
+    "    for candidate in (start, *start.parents):\n",
+    "        if (\n",
+    "            (candidate / \"uv.lock\").exists()\n",
+    "            and (candidate / \"implementations\").is_dir()\n",
+    "            and (candidate / \"aieng-forecasting\").is_dir()\n",
+    "        ):\n",
+    "            return candidate\n",
+    "    raise FileNotFoundError(\"Could not find the agentic-forecasting repository root.\")\n",
+    "\n",
+    "\n",
+    "SETUP_ROOT = find_repo_root(Path.cwd().resolve())\n",
+    "print(\"Running: uv sync --all-extras --dev --all-packages\")\n",
+    "setup_result = subprocess.run(\n",
+    "    [\"uv\", \"sync\", \"--all-extras\", \"--dev\", \"--all-packages\"],\n",
+    "    cwd=SETUP_ROOT,\n",
+    "    check=False,\n",
+    "    text=True,\n",
+    "    capture_output=True,\n",
+    ")\n",
+    "print(setup_result.stdout)\n",
+    "if setup_result.returncode != 0:\n",
+    "    print(setup_result.stderr)\n",
+    "    raise RuntimeError(\"uv sync failed. Install uv or restart the notebook with the repository environment selected.\")\n",
+    "print(\"Environment synchronized successfully.\")\n",
+    "print(\"If packages changed, restart the notebook kernel before continuing.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b696c087",
+   "metadata": {},
+   "source": [
+    "# Manufacturing Stress Forecasting Workbench\n",
+    "\n",
+    "This notebook is the main interactive entry point for the manufacturing-stress experiment. It creates or refreshes the FRED and Yahoo Finance data caches, runs the deterministic baselines, and optionally runs the token-limited LLMP backtest.\n",
+    "\n",
+    "All predictors use the same backtest specification and shared Brier-score implementation. Complete LLMP results are cached by backtest-specification fingerprint so repeated analysis does not make new model calls. Historical agent prompts use relative month offsets instead of calendar dates, but this remains a retrospective pseudo-backtest: model-training knowledge leakage cannot be eliminated completely.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d1b6955d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'refresh_input_data': True, 'backtest_stride': 3, 'run_llmp_backtest': False, 'force_refresh_llmp_cache': False, 'run_current_forecast': False}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Main controls: change these values, then run the notebook from top to bottom.\n",
+    "REFRESH_INPUT_DATA = True\n",
+    "BACKTEST_STRIDE = 3\n",
+    "RUN_LLMP_BACKTEST = False\n",
+    "FORCE_REFRESH_LLMP_CACHE = False\n",
+    "RUN_CURRENT_FORECAST = False\n",
+    "\n",
+    "# BACKTEST_STRIDE = 1 evaluates every month; 3 evaluates every third month.\n",
+    "# False: one smoke run for historical frequency, logistic regression, and XGBoost.\n",
+    "# True: the same smoke run also includes the cached, token-limited LLMP.\n",
+    "# RUN_CURRENT_FORECAST controls the separate latest-data forecast section.\n",
+    "print(\n",
+    "    {\n",
+    "        \"refresh_input_data\": REFRESH_INPUT_DATA,\n",
+    "        \"backtest_stride\": BACKTEST_STRIDE,\n",
+    "        \"run_llmp_backtest\": RUN_LLMP_BACKTEST,\n",
+    "        \"force_refresh_llmp_cache\": FORCE_REFRESH_LLMP_CACHE,\n",
+    "        \"run_current_forecast\": RUN_CURRENT_FORECAST,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e43fbfdd",
+   "metadata": {},
+   "source": [
+    "## Imports and project paths\n",
+    "\n",
+    "Run this notebook with the repository environment selected as the Jupyter kernel. The `.env` file is loaded for the FRED key and, when needed, the LLM proxy credentials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2e5a002b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Repository: /home/coder/BMO-C2-agentic-forecasting-1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd\n",
+    "import yaml\n",
+    "from aieng.forecasting.evaluation import BacktestSpec, backtest\n",
+    "from aieng.forecasting.methods import HistoricalFrequencyPredictor\n",
+    "from dotenv import load_dotenv\n",
+    "from manufacturing_stress_forecasting.analyst_agent import build_manufacturing_stress_agent_predictor\n",
+    "from manufacturing_stress_forecasting.data import IPMAN_SERIES_ID\n",
+    "from manufacturing_stress_forecasting.predictors import (\n",
+    "    ManufacturingStressLogisticPredictor,\n",
+    "    ManufacturingStressXGBoostPredictor,\n",
+    ")\n",
+    "from manufacturing_stress_forecasting.run_agent_backtest import (\n",
+    "    run_or_load_backtest,\n",
+    "    validate_comparable_results,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "REPO_ROOT = SETUP_ROOT\n",
+    "load_dotenv(REPO_ROOT / \".env\", override=False)\n",
+    "SPEC_PATH = (\n",
+    "    REPO_ROOT / \"implementations\" / \"manufacturing_stress_forecasting\" / \"specs\" / \"manufacturing_stress_smoke.yaml\"\n",
+    ")\n",
+    "STORE_DIR = REPO_ROOT / \"data\" / \"predictions\"\n",
+    "print(f\"Repository: {REPO_ROOT}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "549de858",
+   "metadata": {},
+   "source": [
+    "## Create or refresh the data\n",
+    "\n",
+    "The service loads the requested FRED fields and the `SPY` and `XLI` adjusted-close histories from the local FRED and Yahoo Finance caches. Set `REFRESH_INPUT_DATA = True` in the first cell when the caches should be updated from their data services.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7cd4ad0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                        series_id                                                                                       description                                   source                                units frequency  n_obs      start        end\n",
+      "                         CPIAUCSL                                                      Consumer Price Index for All Urban Consumers                          FRED (CPIAUCSL)                                Index        MS    955 1947-01-01 2026-08-01\n",
+      "                          CPI_YOY                                                        Consumer Price Index year-over-year change             Derived from FRED (CPIAUCSL)                              Percent        MS    943 1948-01-01 2026-08-01\n",
+      "                    CREDIT_SPREAD               Moody's Baa corporate bond yield minus 10-year Treasury yield (credit-spread proxy)                            FRED (BAA10Y)                    Percentage points        MS    489 1986-01-01 2026-09-01\n",
+      "                         FEDFUNDS                                                                      Effective federal funds rate                          FRED (FEDFUNDS)                              Percent        MS    867 1954-07-01 2026-09-01\n",
+      "                             ICSA                                                         Initial claims for unemployment insurance                              FRED (ICSA)                               Number        MS    717 1967-01-01 2026-09-01\n",
+      "                   SPY_RETURN_12M                                                       SPY trailing 12-month adjusted-close return             Yahoo Finance (SPY), derived                              Percent        MS    333 1999-01-01 2026-09-01\n",
+      "                    SPY_RETURN_3M                                                        SPY trailing 3-month adjusted-close return             Yahoo Finance (SPY), derived                              Percent        MS    342 1998-04-01 2026-09-01\n",
+      "                           UNRATE                                                                            U.S. unemployment rate                            FRED (UNRATE)                              Percent        MS    943 1948-01-01 2026-08-01\n",
+      "                           VIXCLS                                                                             CBOE volatility index                            FRED (VIXCLS)                                Index        MS    441 1990-01-01 2026-09-01\n",
+      "                   XLI_RETURN_12M                                                       XLI trailing 12-month adjusted-close return             Yahoo Finance (XLI), derived                              Percent        MS    322 1999-12-01 2026-09-01\n",
+      "                    XLI_RETURN_3M                                                        XLI trailing 3-month adjusted-close return             Yahoo Finance (XLI), derived                              Percent        MS    331 1999-03-01 2026-09-01\n",
+      "                        YC_SPREAD                                                        10-year minus 2-year Treasury yield spread                  FRED (DGS10 minus DGS2)                    Percentage points        MS    604 1976-06-01 2026-09-01\n",
+      "               fed_funds_rate_pct                                                            Month-end effective federal funds rate         FRED (FEDFUNDS), derived monthly                              Percent        MS    867 1954-07-01 2026-09-01\n",
+      "              ipman_change_1m_pct                                                       Trailing 1-month percentage change in IPMAN                  Derived from FRED IPMAN                              Percent        MS    655 1972-02-01 2026-08-01\n",
+      "              ipman_change_3m_pct                                                       Trailing 3-month percentage change in IPMAN                  Derived from FRED IPMAN                              Percent        MS    653 1972-04-01 2026-08-01\n",
+      "              ipman_change_6m_pct                                                       Trailing 6-month percentage change in IPMAN                  Derived from FRED IPMAN                              Percent        MS    650 1972-07-01 2026-08-01\n",
+      "ipman_us_manufacturing_production                                            U.S. manufacturing industrial production index (IPMAN)                             FRED (IPMAN)                                Index        MS    656 1972-01-01 2026-08-01\n",
+      "             manufacturing_stress Binary U.S. manufacturing stress label: 1 when trailing 3-month IPMAN change is at or below -2.0%                  Derived from FRED IPMAN Binary event (0=no stress, 1=stress)        MS    653 1972-04-01 2026-08-01\n",
+      " treasury_10y_minus_2y_pct_points                                              Month-end 10-year minus 2-year Treasury yield spread FRED (DGS10 minus DGS2), derived monthly                    Percentage points        MS    604 1976-06-01 2026-09-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "import importlib\n",
+    "\n",
+    "import manufacturing_stress_forecasting.data as manufacturing_stress_data\n",
+    "import manufacturing_stress_forecasting.features as manufacturing_stress_features\n",
+    "\n",
+    "\n",
+    "importlib.reload(manufacturing_stress_features)\n",
+    "manufacturing_stress_data = importlib.reload(manufacturing_stress_data)\n",
+    "\n",
+    "service = manufacturing_stress_data.build_manufacturing_stress_service(\n",
+    "    cache_dir=REPO_ROOT / \"data\" / \"fred\",\n",
+    "    yahoo_cache_dir=REPO_ROOT / \"data\" / \"yfinance\",\n",
+    "    refresh=REFRESH_INPUT_DATA,\n",
+    ")\n",
+    "print(service.summary().to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "095e4106",
+   "metadata": {},
+   "source": [
+    "## Load the common backtest specification\n",
+    "\n",
+    "Every predictor below receives this same target, horizon, forecast-origin schedule, warmup, and cutoff-aware data service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "36a12895",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability that U.S. manufacturing will be under stress three months ahead. A resolved month is stressed when IPMAN has declined by at least 2 percent over its preceding three months.\n",
+      "Origins: 2018-01-01 to 2024-12-01, stride=3, warmup=60\n",
+      "Target: manufacturing_stress; horizon=3 MS\n"
+     ]
+    }
+   ],
+   "source": [
+    "with SPEC_PATH.open() as file:\n",
+    "    spec = BacktestSpec.model_validate(yaml.safe_load(file))\n",
+    "spec = spec.model_copy(update={\"stride\": BACKTEST_STRIDE})\n",
+    "\n",
+    "print(spec.task.description)\n",
+    "print(f\"Origins: {spec.start.date()} to {spec.end.date()}, stride={spec.stride}, warmup={spec.warmup}\")\n",
+    "print(f\"Target: {spec.task.target_series_id}; horizon={spec.task.horizons[0]} {spec.task.frequency}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5d451c",
+   "metadata": {},
+   "source": [
+    "## Run one smoke test\n",
+    "\n",
+    "This is the single execution path. Before scoring, it verifies that the logistic and XGBoost predictors have all 15 registered model features: the three IPMAN momentum features plus the twelve FRED/Yahoo macro features, including 3-month and 12-month monthly-origin returns for both SPY and XLI. All are monthly model frames; daily and weekly source series are reduced to monthly observations first. The cell then runs the historical-frequency, logistic-regression, and XGBoost predictors. Set `RUN_LLMP_BACKTEST = True` in the first cell to include the token-limited LLMP in the same run. Compatible complete results are loaded from cache unless `FORCE_REFRESH_LLMP_CACHE = True`; refreshed data also forces a rerun.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "345f6b3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic/XGBoost feature contract verified: 15 features (12 FRED/Yahoo macro features) are registered at monthly frequency.\n",
+      "Daily/weekly source data, including Yahoo returns, is reduced to monthly observations before modeling.\n",
+      "historical_frequency: 0.0356 mean brier; scored=28 skipped=0\n",
+      "manufacturing_stress_logistic_ipman_rates: 0.2327 mean brier; scored=28 skipped=0\n",
+      "manufacturing_stress_xgboost_ipman_rates: 0.1001 mean brier; scored=28 skipped=0\n",
+      "All predictors were scored on identical forecast origins.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from manufacturing_stress_forecasting.features import FEATURE_SERIES_IDS\n",
+    "\n",
+    "\n",
+    "feature_summary = service.summary().set_index(\"series_id\")\n",
+    "missing_features = sorted(set(FEATURE_SERIES_IDS) - set(feature_summary.index))\n",
+    "if missing_features:\n",
+    "    raise RuntimeError(f\"Missing model features: {missing_features}\")\n",
+    "\n",
+    "non_monthly_features = [\n",
+    "    series_id for series_id in FEATURE_SERIES_IDS if feature_summary.loc[series_id, \"frequency\"] != \"MS\"\n",
+    "]\n",
+    "if non_monthly_features:\n",
+    "    raise RuntimeError(f\"Model features are not monthly: {non_monthly_features}\")\n",
+    "\n",
+    "print(\n",
+    "    \"Logistic/XGBoost feature contract verified: \"\n",
+    "    f\"{len(FEATURE_SERIES_IDS)} features ({len(FEATURE_SERIES_IDS) - 3} FRED/Yahoo macro features) \"\n",
+    "    \"are registered at monthly frequency.\"\n",
+    ")\n",
+    "print(\"Daily/weekly source data, including Yahoo returns, is reduced to monthly observations before modeling.\")\n",
+    "\n",
+    "predictors = [\n",
+    "    HistoricalFrequencyPredictor(),\n",
+    "    ManufacturingStressLogisticPredictor(),\n",
+    "    ManufacturingStressXGBoostPredictor(),\n",
+    "]\n",
+    "if RUN_LLMP_BACKTEST:\n",
+    "    predictors.append(build_manufacturing_stress_agent_predictor(anonymize_dates=True))\n",
+    "\n",
+    "results = {}\n",
+    "for predictor in predictors:\n",
+    "    if RUN_LLMP_BACKTEST:\n",
+    "        result = run_or_load_backtest(\n",
+    "            predictor,\n",
+    "            spec,\n",
+    "            service,\n",
+    "            force_refresh=FORCE_REFRESH_LLMP_CACHE or REFRESH_INPUT_DATA,\n",
+    "            store_dir=STORE_DIR,\n",
+    "        )\n",
+    "    else:\n",
+    "        result = backtest(predictor=predictor, spec=spec, data_service=service)\n",
+    "    results[predictor.predictor_id] = result\n",
+    "    print(\n",
+    "        f\"{result.predictor_id}: {result.mean_score:.4f} mean {result.metric}; \"\n",
+    "        f\"scored={len(result.scores)} skipped={result.skipped_origins}\"\n",
+    "    )\n",
+    "\n",
+    "validate_comparable_results(list(results.values()))\n",
+    "print(\"All predictors were scored on identical forecast origins.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9382397",
+   "metadata": {},
+   "source": [
+    "## Compare results\n",
+    "\n",
+    "Lower Brier score is better. The notebook refuses to compare results unless every predictor was scored on the exact same forecast-origin and forecast-date pairs.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7c1bd4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comparison = pd.DataFrame(\n",
+    "    [\n",
+    "        {\n",
+    "            \"predictor\": result.predictor_id,\n",
+    "            \"metric\": result.metric,\n",
+    "            \"mean_brier\": result.mean_score,\n",
+    "            \"scored\": len(result.scores),\n",
+    "            \"skipped\": result.skipped_origins,\n",
+    "        }\n",
+    "        for result in results.values()\n",
+    "    ]\n",
+    ")\n",
+    "comparison.sort_values(\"mean_brier\") if not comparison.empty else comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "266430ff",
+   "metadata": {},
+   "source": [
+    "## Optional current forecast\n",
+    "\n",
+    "Set `RUN_CURRENT_FORECAST = True` in the first cell to run the selected predictors against the latest released IPMAN data. This is a current forecast, not a historical backtest, so it does not produce a Brier score. `RUN_LLMP_BACKTEST` controls whether the LLMP is included here too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f360e6c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if RUN_CURRENT_FORECAST:\n",
+    "    with SPEC_PATH.open() as file:\n",
+    "        current_task = BacktestSpec.model_validate(yaml.safe_load(file)).task\n",
+    "\n",
+    "    full_ipman = service.get_series(\n",
+    "        IPMAN_SERIES_ID,\n",
+    "        as_of=pd.Timestamp(\"2100-01-01\").to_pydatetime(),\n",
+    "    )\n",
+    "    current_as_of = pd.Timestamp(full_ipman[\"released_at\"].max())\n",
+    "    current_context = service.context(as_of=current_as_of.to_pydatetime())\n",
+    "\n",
+    "    current_predictors = [\n",
+    "        HistoricalFrequencyPredictor(),\n",
+    "        ManufacturingStressLogisticPredictor(),\n",
+    "        ManufacturingStressXGBoostPredictor(),\n",
+    "    ]\n",
+    "    if RUN_LLMP_BACKTEST:\n",
+    "        current_predictors.append(build_manufacturing_stress_agent_predictor())\n",
+    "\n",
+    "    print(f\"Forecast origin: {current_as_of.date()}\")\n",
+    "    print(\n",
+    "        \"Latest visible IPMAN reference month: \"\n",
+    "        f\"{pd.Timestamp(current_context.get_series(IPMAN_SERIES_ID)['timestamp'].max()).date()}\"\n",
+    "    )\n",
+    "    for predictor in current_predictors:\n",
+    "        prediction = predictor.predict(current_task, current_context)[0]\n",
+    "        print(\n",
+    "            {\n",
+    "                \"predictor\": prediction.predictor_id,\n",
+    "                \"forecast_date\": str(pd.Timestamp(prediction.forecast_date).date()),\n",
+    "                \"stress_probability\": prediction.payload.probability,\n",
+    "                \"metadata\": prediction.metadata,\n",
+    "            }\n",
+    "        )\n",
+    "else:\n",
+    "    print(\"Current forecast is disabled. Set RUN_CURRENT_FORECAST = True to enable it.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "agentic-forecasting-bootcamp (3.12.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/manufacturing_stress_workbench_2.ipynb b/implementations/manufacturing_stress_forecasting/manufacturing_stress_workbench_2.ipynb
new file mode 100644
index 000000000..e6895aa2a
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/manufacturing_stress_workbench_2.ipynb
@@ -0,0 +1,2559 @@
+{
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "id": "33fc7fa8",
+            "metadata": {
+                "id": "33fc7fa8",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Running: uv sync --all-extras --dev --all-packages\n",
+                        "\n",
+                        "Environment synchronized successfully.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import subprocess\n",
+                "from pathlib import Path\n",
+                "\n",
+                "def find_repo_root(start: Path) -> Path:\n",
+                "    for candidate in (start, *start.parents):\n",
+                "        if ((candidate / 'uv.lock').exists() and (candidate / 'implementations').is_dir() and (candidate / 'aieng-forecasting').is_dir()):\n",
+                "            return candidate\n",
+                "    raise FileNotFoundError('Could not find the agentic-forecasting repository root.')\n",
+                "\n",
+                "SETUP_ROOT = find_repo_root(Path.cwd().resolve())\n",
+                "print('Running: uv sync --all-extras --dev --all-packages')\n",
+                "setup_result = subprocess.run(['uv', 'sync', '--all-extras', '--dev', '--all-packages'], cwd=SETUP_ROOT, check=False, text=True, capture_output=True)\n",
+                "print(setup_result.stdout)\n",
+                "if setup_result.returncode != 0:\n",
+                "    print(setup_result.stderr)\n",
+                "    raise RuntimeError('uv sync failed. Install uv or restart the notebook with the repository environment selected.')\n",
+                "print('Environment synchronized successfully.')"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "88979cb8",
+            "metadata": {
+                "id": "88979cb8",
+                "language": "markdown"
+            },
+            "source": [
+                "# Manufacturing Stress Model Comparison\n",
+                "\n",
+                "This notebook compares the existing manufacturing-stress forecasting models under one cutoff-aware binary backtest. Numerical models run by default. The stateless hybrid agent and stateful adaptive agent are explicit opt-ins because they may make LLM calls.\n",
+                "\n",
+                "The primary metric is the binary Brier score: lower is better. The 2018-2024 window is a historical diagnostic, not an untouched future holdout."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "id": "687d8ef1",
+            "metadata": {
+                "id": "687d8ef1",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Repository: /home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2\n",
+                        "FRED API key loaded: True\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import json\n",
+                "import os\n",
+                "import sys\n",
+                "from pathlib import Path\n",
+                "\n",
+                "for package_root in (SETUP_ROOT / 'aieng-forecasting', SETUP_ROOT / 'implementations'):\n",
+                "    if str(package_root) not in sys.path:\n",
+                "        sys.path.insert(0, str(package_root))\n",
+                "\n",
+                "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
+                "import pandas as pd\n",
+                "import yaml\n",
+                "from aieng.forecasting.evaluation import BacktestSpec, backtest\n",
+                "from aieng.forecasting.methods import HistoricalFrequencyPredictor\n",
+                "from dotenv import load_dotenv\n",
+                "from IPython.display import display\n",
+                "from manufacturing_stress_forecasting.data import build_manufacturing_stress_service\n",
+                "from manufacturing_stress_forecasting.features import FEATURE_SERIES_IDS\n",
+                "from manufacturing_stress_forecasting.predictors import ManufacturingStressLogisticPredictor, ManufacturingStressXGBoostPredictor\n",
+                "from manufacturing_stress_forecasting.run_agent_backtest import run_or_load_backtest, validate_comparable_results\n",
+                "\n",
+                "REPO_ROOT = SETUP_ROOT\n",
+                "load_dotenv(REPO_ROOT / '.env', override=False)\n",
+                "SPEC_PATH = REPO_ROOT / 'implementations' / 'manufacturing_stress_forecasting' / 'specs' / 'manufacturing_stress_smoke.yaml'\n",
+                "STORE_DIR = REPO_ROOT / 'data' / 'predictions'\n",
+                "print(f'Repository: {REPO_ROOT}')\n",
+                "print(f'FRED API key loaded: {bool(os.getenv(\"FRED_API_KEY\"))}')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "id": "2a1e8030",
+            "metadata": {
+                "id": "2a1e8030",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "{'refresh_input_data': False, 'backtest_stride': 1, 'run_analyst_agent': True, 'run_hybrid_agent': True, 'run_adaptive_agent': True, 'force_refresh_agent_cache': False, 'show_origin_details': True, 'show_plots': True}\n"
+                    ]
+                }
+            ],
+            "source": [
+                "REFRESH_INPUT_DATA = False\n",
+                "BACKTEST_STRIDE = 1\n",
+                "RUN_ANALYST_AGENT = True\n",
+                "RUN_HYBRID_AGENT = True\n",
+                "RUN_ADAPTIVE_AGENT = True\n",
+                "FORCE_REFRESH_AGENT_CACHE = False\n",
+                "SHOW_ORIGIN_DETAILS = True\n",
+                "SHOW_PLOTS = True\n",
+                "\n",
+                "print({\n",
+                "    'refresh_input_data': REFRESH_INPUT_DATA,\n",
+                "    'backtest_stride': BACKTEST_STRIDE,\n",
+                "    'run_analyst_agent': RUN_ANALYST_AGENT,\n",
+                "    'run_hybrid_agent': RUN_HYBRID_AGENT,\n",
+                "    'run_adaptive_agent': RUN_ADAPTIVE_AGENT,\n",
+                "    'force_refresh_agent_cache': FORCE_REFRESH_AGENT_CACHE,\n",
+                "    'show_origin_details': SHOW_ORIGIN_DETAILS,\n",
+                "    'show_plots': SHOW_PLOTS,\n",
+                "})"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "66eebbfa",
+            "metadata": {
+                "id": "66eebbfa",
+                "language": "markdown"
+            },
+            "source": [
+                "## Load data and specification\n",
+                "\n",
+                "The service is cache-first by default. Set `REFRESH_INPUT_DATA = True` only when intentionally refreshing FRED and Yahoo Finance inputs."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "id": "4bbf48db",
+            "metadata": {
+                "id": "4bbf48db",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Origins: 2018-01-01 to 2024-12-01, stride=1, warmup=60\n",
+                        "Target: manufacturing_stress; horizon=3 MS\n",
+                        "Validated 15 monthly model features.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "with SPEC_PATH.open() as file:\n",
+                "    base_spec = BacktestSpec.model_validate(yaml.safe_load(file))\n",
+                "spec = base_spec.model_copy(update={'stride': BACKTEST_STRIDE})\n",
+                "service = build_manufacturing_stress_service(cache_dir=REPO_ROOT / 'data' / 'fred', yahoo_cache_dir=REPO_ROOT / 'data' / 'yfinance', refresh=REFRESH_INPUT_DATA)\n",
+                "summary = service.summary()\n",
+                "feature_summary = summary.set_index('series_id')\n",
+                "missing_features = sorted(set(FEATURE_SERIES_IDS) - set(feature_summary.index))\n",
+                "if missing_features:\n",
+                "    raise RuntimeError(f'Missing model features: {missing_features}')\n",
+                "non_monthly_features = [series_id for series_id in FEATURE_SERIES_IDS if feature_summary.loc[series_id, 'frequency'] != 'MS']\n",
+                "if non_monthly_features:\n",
+                "    raise RuntimeError(f'Model features are not monthly: {non_monthly_features}')\n",
+                "print(f'Origins: {spec.start.date()} to {spec.end.date()}, stride={spec.stride}, warmup={spec.warmup}')\n",
+                "print(f'Target: {spec.task.target_series_id}; horizon={spec.task.horizons[0]} {spec.task.frequency}')\n",
+                "print(f'Validated {len(FEATURE_SERIES_IDS)} monthly model features.')"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "3f410d37",
+            "metadata": {
+                "id": "3f410d37",
+                "language": "markdown"
+            },
+            "source": [
+                "## Run comparable models\n",
+                "\n",
+                "The comparison includes historical frequency, fixed logistic regression (`C=0.001`), fixed regularized XGBoost (`50` trees, depth `2`, learning rate `0.03`, `min_child_weight=3`, `reg_lambda=5`), and the deterministic hybrid anchor. The hybrid anchor uses the logistic prediction only. When enabled, `hybrid_agent_adjusted` is the stateless hybrid agent row and `adaptive_agent` is the stateful adaptive agent row. Each agent starts from the logistic anchor and applies a bounded adjustment. Every result must contain the same scored origin and forecast-date pairs."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "0d54f0a5",
+            "metadata": {
+                "id": "0d54f0a5",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "historical_frequency: 0.0667 mean brier; scored=84 skipped=0\n",
+                        "logistic_c_0_001: 0.0689 mean brier; scored=84 skipped=0\n",
+                        "xgb_50_depth2_lr0_03_minchild3_l2_5: 0.0722 mean brier; scored=84 skipped=0\n",
+                        "hybrid_anchor: 0.0689 mean brier; scored=84 skipped=0\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/home/coder/agentic-forecasting/.venv/lib/python3.12/site-packages/google/adk/tools/function_tool.py:95: UserWarning: [EXPERIMENTAL] feature FeatureName.JSON_SCHEMA_FOR_FUNC_DECL is enabled.\n",
+                        "  build_function_declaration(\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "agent_predictor_manufacturing_stress_analyst_gemini-3.1-flash-lite-preview_discrete: saved /home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2/data/predictions/manufacturing_stress_smoke_llmp_v3_14b1deebe4/agent_predictor_manufacturing_stress_analyst_gemini-3.1-flash-lite-preview_discrete.yaml\n",
+                        "manufacturing_stress_hybrid_agent_v2: saved /home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2/data/predictions/manufacturing_stress_smoke_llmp_v3_14b1deebe4/manufacturing_stress_hybrid_agent_v2.yaml\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1d95fd0>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1e2f1c0>, 41803.896963573)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1e282c0>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1abd010>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8cc20>, 41805.432975632)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1abd310>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1abf2c0>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8d010>, 41806.615523711)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1abf1a0>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1ae1670>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8cbb0>, 41808.028440476)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1c915e0>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1ae3950>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8d550>, 41809.29157742)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1ae3350>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1ae5ac0>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8d860>, 41811.685242957)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1ae59a0>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1af8230>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8d710>, 41813.878119185)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1ae7e90>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1afa1e0>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8dbe0>, 41816.803292829)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1af8590>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1b083b0>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8dd30>, 41818.087678243)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1b08680>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1b0a390>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8e0b0>, 41820.805912849)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1b0a450>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1b54440>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8e190>, 41821.96745585)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1b54710>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1b56660>\n",
+                        "Unclosed connector\n",
+                        "connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x79f9a1a8e4a0>, 41823.485179499)])']\n",
+                        "connector: <aiohttp.connector.TCPConnector object at 0x79f9a1b56930>\n",
+                        "Unclosed client session\n",
+                        "client_session: <aiohttp.client.ClientSession object at 0x79f9a1b68830>\n",
+                        "Unclosed connector\n",
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+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "manufacturing_stress_adaptive_agent_v1: saved /home/coder/BMO-C2-agentic-forecasting-copilot-push-to-bmo-c2/data/predictions/manufacturing_stress_smoke_llmp_v3_14b1deebe4/manufacturing_stress_adaptive_agent_v1.yaml\n",
+                        "All enabled models were scored on identical forecast origins.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "from datetime import datetime, timezone\n",
+                "\n",
+                "from aieng.forecasting.evaluation.prediction import BinaryForecast, Prediction\n",
+                "from manufacturing_stress_forecasting.hybrid_agent import HybridAgentPredictor, build_hybrid_agent_predictor, build_numerical_anchor\n",
+                "\n",
+                "predictor_specs = [\n",
+                "    ('historical_frequency', lambda: HistoricalFrequencyPredictor()),\n",
+                "    ('logistic_c_0_001', lambda: ManufacturingStressLogisticPredictor(regularization_c=0.001)),\n",
+                "    ('xgb_50_depth2_lr0_03_minchild3_l2_5', lambda: ManufacturingStressXGBoostPredictor(\n",
+                "        n_estimators=50,\n",
+                "        max_depth=2,\n",
+                "        learning_rate=0.03,\n",
+                "        min_child_weight=3.0,\n",
+                "        reg_lambda=5.0,\n",
+                "    )),\n",
+                "]\n",
+                "results = {}\n",
+                "for name, factory in predictor_specs:\n",
+                "    predictor = factory()\n",
+                "    result = backtest(predictor=predictor, spec=spec, data_service=service)\n",
+                "    results[name] = result\n",
+                "    print(f'{name}: {result.mean_score:.4f} mean {result.metric}; scored={len(result.scores)} skipped={result.skipped_origins}')\n",
+                "\n",
+                "class AnchorPredictor:\n",
+                "    @property\n",
+                "    def predictor_id(self):\n",
+                "        return 'manufacturing_stress_hybrid_anchor_v1'\n",
+                "\n",
+                "    def predict(self, task, context):\n",
+                "        anchor = build_numerical_anchor(task, context)\n",
+                "        as_of = pd.Timestamp(context.as_of)\n",
+                "        lead = pd.tseries.frequencies.to_offset(task.frequency) * task.horizons[0]\n",
+                "        return [Prediction(\n",
+                "            predictor_id=self.predictor_id,\n",
+                "            task_id=task.task_id,\n",
+                "            issued_at=datetime.now(tz=timezone.utc).replace(tzinfo=None),\n",
+                "            as_of=context.as_of,\n",
+                "            forecast_date=(as_of + lead).to_pydatetime(),\n",
+                "            payload=BinaryForecast(probability=anchor.probability),\n",
+                "            metadata=anchor.as_dict(),\n",
+                "        )]\n",
+                "\n",
+                "anchor_result = backtest(predictor=AnchorPredictor(), spec=spec, data_service=service)\n",
+                "results['hybrid_anchor'] = anchor_result\n",
+                "print(f'hybrid_anchor: {anchor_result.mean_score:.4f} mean {anchor_result.metric}; scored={len(anchor_result.scores)} skipped={anchor_result.skipped_origins}')\n",
+                "\n",
+                "if RUN_ANALYST_AGENT:\n",
+                "    from manufacturing_stress_forecasting.analyst_agent import build_manufacturing_stress_agent_predictor\n",
+                "\n",
+                "    analyst_predictor = build_manufacturing_stress_agent_predictor(anonymize_dates=True)\n",
+                "    analyst_result = run_or_load_backtest(\n",
+                "        analyst_predictor,\n",
+                "        spec,\n",
+                "        service,\n",
+                "        force_refresh=FORCE_REFRESH_AGENT_CACHE or REFRESH_INPUT_DATA,\n",
+                "        store_dir=STORE_DIR,\n",
+                "    )\n",
+                "    results['analyst_agent'] = analyst_result\n",
+                "\n",
+                "if RUN_HYBRID_AGENT:\n",
+                "    hybrid_predictor = HybridAgentPredictor(build_hybrid_agent_predictor(anonymize_dates=True))\n",
+                "    hybrid_result = run_or_load_backtest(\n",
+                "        hybrid_predictor,\n",
+                "        spec,\n",
+                "        service,\n",
+                "        force_refresh=FORCE_REFRESH_AGENT_CACHE or REFRESH_INPUT_DATA,\n",
+                "        store_dir=STORE_DIR,\n",
+                "    )\n",
+                "    results['hybrid_agent_adjusted'] = hybrid_result\n",
+                "\n",
+                "if RUN_ADAPTIVE_AGENT:\n",
+                "    from manufacturing_stress_forecasting.adaptive_agent import build_manufacturing_adaptive_agent_predictor\n",
+                "\n",
+                "    adaptive_predictor = HybridAgentPredictor(\n",
+                "        build_manufacturing_adaptive_agent_predictor(anonymize_dates=True),\n",
+                "        predictor_id='manufacturing_stress_adaptive_agent_v1',\n",
+                "    )\n",
+                "    adaptive_result = run_or_load_backtest(\n",
+                "        adaptive_predictor,\n",
+                "        spec,\n",
+                "        service,\n",
+                "        force_refresh=FORCE_REFRESH_AGENT_CACHE or REFRESH_INPUT_DATA,\n",
+                "        store_dir=STORE_DIR,\n",
+                "    )\n",
+                "    results['adaptive_agent'] = adaptive_result\n",
+                "\n",
+                "validate_comparable_results(list(results.values()))\n",
+                "print('All enabled models were scored on identical forecast origins.')"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "ab4f6a25",
+            "metadata": {
+                "id": "ab4f6a25",
+                "language": "markdown"
+            },
+            "source": [
+                "## Review comparable-model results\n",
+                "\n",
+                "The comparison includes historical frequency, fixed logistic regression (`C=0.001`), fixed XGBoost (`50` trees, depth `2`, learning rate `0.03`), and the deterministic hybrid anchor. When enabled, the adaptive analyst's bounded adjustment is also evaluated as the named final forecast probability. Every result must contain the same scored origin and forecast-date pairs."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "id": "18f76d41",
+            "metadata": {
+                "id": "18f76d41",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "data": {
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+                            "      <th></th>\n",
+                            "      <th>model</th>\n",
+                            "      <th>metric</th>\n",
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+                            "      <th>1</th>\n",
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+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>brier</td>\n",
+                            "      <td>0.068921</td>\n",
+                            "      <td>84</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.002229</td>\n",
+                            "      <td>-0.033414</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>brier</td>\n",
+                            "      <td>0.069205</td>\n",
+                            "      <td>84</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.002512</td>\n",
+                            "      <td>-0.037669</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>brier</td>\n",
+                            "      <td>0.070689</td>\n",
+                            "      <td>84</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.003997</td>\n",
+                            "      <td>-0.059929</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>5</th>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>brier</td>\n",
+                            "      <td>0.072189</td>\n",
+                            "      <td>84</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.005496</td>\n",
+                            "      <td>-0.082407</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>6</th>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>brier</td>\n",
+                            "      <td>0.097926</td>\n",
+                            "      <td>84</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.031234</td>\n",
+                            "      <td>-0.468325</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                 model metric  mean_brier  scored_origins  \\\n",
+                            "0                 historical_frequency  brier    0.066693              84   \n",
+                            "1                     logistic_c_0_001  brier    0.068921              84   \n",
+                            "2                        hybrid_anchor  brier    0.068921              84   \n",
+                            "3                       adaptive_agent  brier    0.069205              84   \n",
+                            "4                hybrid_agent_adjusted  brier    0.070689              84   \n",
+                            "5  xgb_50_depth2_lr0_03_minchild3_l2_5  brier    0.072189              84   \n",
+                            "6                        analyst_agent  brier    0.097926              84   \n",
+                            "\n",
+                            "   skipped_origins  delta_vs_baseline  brier_skill  \n",
+                            "0                0           0.000000     0.000000  \n",
+                            "1                0           0.002229    -0.033414  \n",
+                            "2                0           0.002229    -0.033414  \n",
+                            "3                0           0.002512    -0.037669  \n",
+                            "4                0           0.003997    -0.059929  \n",
+                            "5                0           0.005496    -0.082407  \n",
+                            "6                0           0.031234    -0.468325  "
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>model</th>\n",
+                            "      <th>as_of</th>\n",
+                            "      <th>forecast_date</th>\n",
+                            "      <th>anchor_probability</th>\n",
+                            "      <th>agent_probability</th>\n",
+                            "      <th>raw_agent_adjustment</th>\n",
+                            "      <th>agent_adjustment</th>\n",
+                            "      <th>adjusted_probability</th>\n",
+                            "      <th>validation_status</th>\n",
+                            "      <th>rationale</th>\n",
+                            "      <th>probability</th>\n",
+                            "      <th>outcome</th>\n",
+                            "      <th>brier</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>420</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2018-01-01</td>\n",
+                            "      <td>2018-04-01</td>\n",
+                            "      <td>0.058279</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>-0.000279</td>\n",
+                            "      <td>-0.000279</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends remain positive over three and si...</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.003364</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>421</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2018-02-01</td>\n",
+                            "      <td>2018-05-01</td>\n",
+                            "      <td>0.057800</td>\n",
+                            "      <td>0.065</td>\n",
+                            "      <td>0.007200</td>\n",
+                            "      <td>0.007200</td>\n",
+                            "      <td>0.065</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends are slightly negative but remain ...</td>\n",
+                            "      <td>0.065</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.004225</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>422</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2018-03-01</td>\n",
+                            "      <td>2018-06-01</td>\n",
+                            "      <td>0.057961</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>0.000039</td>\n",
+                            "      <td>0.000039</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends remain positive over 1, 3, and 6 ...</td>\n",
+                            "      <td>0.058</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.003364</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>423</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2018-04-01</td>\n",
+                            "      <td>2018-07-01</td>\n",
+                            "      <td>0.061188</td>\n",
+                            "      <td>0.065</td>\n",
+                            "      <td>0.003812</td>\n",
+                            "      <td>0.003812</td>\n",
+                            "      <td>0.065</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>The 3-month IPMAN trend remains positive, and ...</td>\n",
+                            "      <td>0.065</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.004225</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>424</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2018-05-01</td>\n",
+                            "      <td>2018-08-01</td>\n",
+                            "      <td>0.059624</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>0.025376</td>\n",
+                            "      <td>0.025376</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends remain positive, but significant ...</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.007225</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>...</th>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>499</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2024-08-01</td>\n",
+                            "      <td>2024-11-01</td>\n",
+                            "      <td>0.064054</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>0.010946</td>\n",
+                            "      <td>0.010946</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends are slightly negative, and the yi...</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.005625</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>500</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2024-09-01</td>\n",
+                            "      <td>2024-12-01</td>\n",
+                            "      <td>0.063333</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>0.011667</td>\n",
+                            "      <td>0.011667</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends remain slightly negative over thr...</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.005625</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>501</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2024-10-01</td>\n",
+                            "      <td>2025-01-01</td>\n",
+                            "      <td>0.062221</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>0.012779</td>\n",
+                            "      <td>0.012779</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends remain slightly negative, suggest...</td>\n",
+                            "      <td>0.075</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.005625</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>502</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2024-11-01</td>\n",
+                            "      <td>2025-02-01</td>\n",
+                            "      <td>0.063603</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>0.021397</td>\n",
+                            "      <td>0.021397</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>IPMAN trends remain slightly negative, and ele...</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.007225</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>503</th>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>2024-12-01</td>\n",
+                            "      <td>2025-03-01</td>\n",
+                            "      <td>0.063051</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>0.021949</td>\n",
+                            "      <td>0.021949</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>passed</td>\n",
+                            "      <td>The three-month IPMAN decline is accelerating ...</td>\n",
+                            "      <td>0.085</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.007225</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "<p>84 rows × 13 columns</p>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                     model      as_of forecast_date  anchor_probability  \\\n",
+                            "420  hybrid_agent_adjusted 2018-01-01    2018-04-01            0.058279   \n",
+                            "421  hybrid_agent_adjusted 2018-02-01    2018-05-01            0.057800   \n",
+                            "422  hybrid_agent_adjusted 2018-03-01    2018-06-01            0.057961   \n",
+                            "423  hybrid_agent_adjusted 2018-04-01    2018-07-01            0.061188   \n",
+                            "424  hybrid_agent_adjusted 2018-05-01    2018-08-01            0.059624   \n",
+                            "..                     ...        ...           ...                 ...   \n",
+                            "499  hybrid_agent_adjusted 2024-08-01    2024-11-01            0.064054   \n",
+                            "500  hybrid_agent_adjusted 2024-09-01    2024-12-01            0.063333   \n",
+                            "501  hybrid_agent_adjusted 2024-10-01    2025-01-01            0.062221   \n",
+                            "502  hybrid_agent_adjusted 2024-11-01    2025-02-01            0.063603   \n",
+                            "503  hybrid_agent_adjusted 2024-12-01    2025-03-01            0.063051   \n",
+                            "\n",
+                            "     agent_probability  raw_agent_adjustment  agent_adjustment  \\\n",
+                            "420              0.058             -0.000279         -0.000279   \n",
+                            "421              0.065              0.007200          0.007200   \n",
+                            "422              0.058              0.000039          0.000039   \n",
+                            "423              0.065              0.003812          0.003812   \n",
+                            "424              0.085              0.025376          0.025376   \n",
+                            "..                 ...                   ...               ...   \n",
+                            "499              0.075              0.010946          0.010946   \n",
+                            "500              0.075              0.011667          0.011667   \n",
+                            "501              0.075              0.012779          0.012779   \n",
+                            "502              0.085              0.021397          0.021397   \n",
+                            "503              0.085              0.021949          0.021949   \n",
+                            "\n",
+                            "     adjusted_probability validation_status  \\\n",
+                            "420                 0.058            passed   \n",
+                            "421                 0.065            passed   \n",
+                            "422                 0.058            passed   \n",
+                            "423                 0.065            passed   \n",
+                            "424                 0.085            passed   \n",
+                            "..                    ...               ...   \n",
+                            "499                 0.075            passed   \n",
+                            "500                 0.075            passed   \n",
+                            "501                 0.075            passed   \n",
+                            "502                 0.085            passed   \n",
+                            "503                 0.085            passed   \n",
+                            "\n",
+                            "                                             rationale  probability  outcome  \\\n",
+                            "420  IPMAN trends remain positive over three and si...        0.058      0.0   \n",
+                            "421  IPMAN trends are slightly negative but remain ...        0.065      0.0   \n",
+                            "422  IPMAN trends remain positive over 1, 3, and 6 ...        0.058      0.0   \n",
+                            "423  The 3-month IPMAN trend remains positive, and ...        0.065      0.0   \n",
+                            "424  IPMAN trends remain positive, but significant ...        0.085      0.0   \n",
+                            "..                                                 ...          ...      ...   \n",
+                            "499  IPMAN trends are slightly negative, and the yi...        0.075      0.0   \n",
+                            "500  IPMAN trends remain slightly negative over thr...        0.075      0.0   \n",
+                            "501  IPMAN trends remain slightly negative, suggest...        0.075      0.0   \n",
+                            "502  IPMAN trends remain slightly negative, and ele...        0.085      0.0   \n",
+                            "503  The three-month IPMAN decline is accelerating ...        0.085      0.0   \n",
+                            "\n",
+                            "        brier  \n",
+                            "420  0.003364  \n",
+                            "421  0.004225  \n",
+                            "422  0.003364  \n",
+                            "423  0.004225  \n",
+                            "424  0.007225  \n",
+                            "..        ...  \n",
+                            "499  0.005625  \n",
+                            "500  0.005625  \n",
+                            "501  0.005625  \n",
+                            "502  0.007225  \n",
+                            "503  0.007225  \n",
+                            "\n",
+                            "[84 rows x 13 columns]"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "def result_rows(results):\n",
+                "    baseline = results['historical_frequency'].mean_score\n",
+                "    rows = []\n",
+                "    for name, result in results.items():\n",
+                "        rows.append({\n",
+                "            'model': name,\n",
+                "            'metric': result.metric,\n",
+                "            'mean_brier': result.mean_score,\n",
+                "            'scored_origins': len(result.scores),\n",
+                "            'skipped_origins': result.skipped_origins,\n",
+                "            'delta_vs_baseline': result.mean_score - baseline,\n",
+                "            'brier_skill': 1.0 - result.mean_score / baseline if baseline else np.nan,\n",
+                "        })\n",
+                "    return pd.DataFrame(rows).sort_values('mean_brier').reset_index(drop=True)\n",
+                "\n",
+                "comparison = result_rows(results)\n",
+                "display(comparison)\n",
+                "\n",
+                "target_values = service.get_series(spec.task.target_series_id, as_of=pd.Timestamp('2100-01-01').to_pydatetime())\n",
+                "outcomes_by_date = {\n",
+                "    pd.Timestamp(timestamp): float(value)\n",
+                "    for timestamp, value in zip(target_values['timestamp'], target_values['value'], strict=True)\n",
+                "}\n",
+                "origin_rows = []\n",
+                "for name, result in results.items():\n",
+                "    for prediction, score in zip(result.predictions, result.scores, strict=True):\n",
+                "        forecast_date = pd.Timestamp(prediction.forecast_date)\n",
+                "        outcome = outcomes_by_date[forecast_date]\n",
+                "        metadata = prediction.metadata or {}\n",
+                "        origin_rows.append({\n",
+                "            'model': name,\n",
+                "            'as_of': prediction.as_of,\n",
+                "            'forecast_date': prediction.forecast_date,\n",
+                "            'anchor_probability': metadata.get('anchor_probability'),\n",
+                "            'agent_probability': metadata.get('agent_probability'),\n",
+                "            'raw_agent_adjustment': metadata.get('raw_agent_adjustment'),\n",
+                "            'agent_adjustment': metadata.get('agent_adjustment'),\n",
+                "            'adjusted_probability': metadata.get('adjusted_probability'),\n",
+                "            'validation_status': metadata.get('validation_status'),\n",
+                "            'rationale': metadata.get('rationale'),\n",
+                "            'probability': float(prediction.payload.probability),\n",
+                "            'outcome': outcome,\n",
+                "            'brier': float(score),\n",
+                "        })\n",
+                "origin_details = pd.DataFrame(origin_rows)\n",
+                "if SHOW_ORIGIN_DETAILS:\n",
+                "    if RUN_HYBRID_AGENT:\n",
+                "        display(origin_details[origin_details['model'] == 'hybrid_agent_adjusted'])\n",
+                "    else:\n",
+                "        display(origin_details.head(20))"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "faa86f65",
+            "metadata": {
+                "id": "faa86f65",
+                "language": "markdown"
+            },
+            "source": [
+                "## Regime-conditional diagnostics\n",
+                "\n",
+                "Regime labels are descriptive, cutoff-safe slices of the fixed forecasts above. They do not retune models, change the hybrid anchor, or spend protected evaluation runs. Each threshold is calculated using only observations visible at that forecast origin.\n",
+                "\n",
+                "## Hybrid weight sweep\n",
+                "\n",
+                "The logistic and XGBoost probabilities are already available in `origin_details`. Candidate hybrid weights are evaluated offline from those rows, so this section performs no additional model fits or LLM calls. The active numerical anchor remains logistic-only; the sweep is diagnostic and does not change it."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "c09989f0",
+            "metadata": {
+                "id": "c09989f0",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Regime origin counts:\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
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+                            "\n",
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+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>origins</th>\n",
+                            "      <th>stress_events</th>\n",
+                            "      <th>observed_event_rate</th>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>momentum_regime</th>\n",
+                            "      <th></th>\n",
+                            "      <th></th>\n",
+                            "      <th></th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>expanding</th>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3.0</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>slowing</th>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3.0</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>stressed</th>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                 origins  stress_events  observed_event_rate\n",
+                            "momentum_regime                                             \n",
+                            "expanding             35            3.0             0.085714\n",
+                            "slowing               43            3.0             0.069767\n",
+                            "stressed               6            0.0             0.000000"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
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+                            "    }\n",
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+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>origins</th>\n",
+                            "      <th>stress_events</th>\n",
+                            "      <th>observed_event_rate</th>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>financial_regime</th>\n",
+                            "      <th></th>\n",
+                            "      <th></th>\n",
+                            "      <th></th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>restrictive</th>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2.0</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>supportive</th>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4.0</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                  origins  stress_events  observed_event_rate\n",
+                            "financial_regime                                             \n",
+                            "restrictive            43            2.0             0.046512\n",
+                            "supportive             41            4.0             0.097561"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
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+                            "    }\n",
+                            "\n",
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+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>origins</th>\n",
+                            "      <th>stress_events</th>\n",
+                            "      <th>observed_event_rate</th>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>volatility_regime</th>\n",
+                            "      <th></th>\n",
+                            "      <th></th>\n",
+                            "      <th></th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>elevated</th>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2.0</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>normal</th>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4.0</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                   origins  stress_events  observed_event_rate\n",
+                            "volatility_regime                                             \n",
+                            "elevated                27            2.0             0.074074\n",
+                            "normal                  57            4.0             0.070175"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
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+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>regime_dimension</th>\n",
+                            "      <th>regime</th>\n",
+                            "      <th>predictor</th>\n",
+                            "      <th>n_origins</th>\n",
+                            "      <th>n_events</th>\n",
+                            "      <th>event_rate</th>\n",
+                            "      <th>mean_probability</th>\n",
+                            "      <th>brier</th>\n",
+                            "      <th>mean_probability_error</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>expanding</td>\n",
+                            "      <td>historical_frequency</td>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "      <td>0.065601</td>\n",
+                            "      <td>0.079225</td>\n",
+                            "      <td>-0.020113</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>expanding</td>\n",
+                            "      <td>logistic_c_0_001</td>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "      <td>0.065439</td>\n",
+                            "      <td>0.080657</td>\n",
+                            "      <td>-0.020276</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>expanding</td>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "      <td>0.054925</td>\n",
+                            "      <td>0.084213</td>\n",
+                            "      <td>-0.030789</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>expanding</td>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "      <td>0.065439</td>\n",
+                            "      <td>0.080657</td>\n",
+                            "      <td>-0.020276</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>expanding</td>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "      <td>0.097000</td>\n",
+                            "      <td>0.083132</td>\n",
+                            "      <td>0.011286</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>5</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>expanding</td>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "      <td>0.072241</td>\n",
+                            "      <td>0.082445</td>\n",
+                            "      <td>-0.013474</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>6</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>expanding</td>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>35</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.085714</td>\n",
+                            "      <td>0.062331</td>\n",
+                            "      <td>0.080829</td>\n",
+                            "      <td>-0.023383</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>7</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>slowing</td>\n",
+                            "      <td>historical_frequency</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "      <td>0.064595</td>\n",
+                            "      <td>0.065192</td>\n",
+                            "      <td>-0.005173</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>8</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>slowing</td>\n",
+                            "      <td>logistic_c_0_001</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "      <td>0.065971</td>\n",
+                            "      <td>0.065465</td>\n",
+                            "      <td>-0.003797</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>9</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>slowing</td>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "      <td>0.079155</td>\n",
+                            "      <td>0.068032</td>\n",
+                            "      <td>0.009388</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>10</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>slowing</td>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "      <td>0.065971</td>\n",
+                            "      <td>0.065465</td>\n",
+                            "      <td>-0.003797</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>11</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>slowing</td>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "      <td>0.154884</td>\n",
+                            "      <td>0.073707</td>\n",
+                            "      <td>0.085116</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>12</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>slowing</td>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "      <td>0.085322</td>\n",
+                            "      <td>0.066181</td>\n",
+                            "      <td>0.015555</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>13</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>slowing</td>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>3</td>\n",
+                            "      <td>0.069767</td>\n",
+                            "      <td>0.067343</td>\n",
+                            "      <td>0.064596</td>\n",
+                            "      <td>-0.002425</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>14</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>stressed</td>\n",
+                            "      <td>historical_frequency</td>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.065865</td>\n",
+                            "      <td>0.004342</td>\n",
+                            "      <td>0.065865</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>15</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>stressed</td>\n",
+                            "      <td>logistic_c_0_001</td>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.138297</td>\n",
+                            "      <td>0.025228</td>\n",
+                            "      <td>0.138297</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>16</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>stressed</td>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.177890</td>\n",
+                            "      <td>0.031837</td>\n",
+                            "      <td>0.177890</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>17</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>stressed</td>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.138297</td>\n",
+                            "      <td>0.025228</td>\n",
+                            "      <td>0.138297</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>18</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>stressed</td>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.543333</td>\n",
+                            "      <td>0.357800</td>\n",
+                            "      <td>0.543333</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>19</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>stressed</td>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.168297</td>\n",
+                            "      <td>0.034426</td>\n",
+                            "      <td>0.168297</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>20</th>\n",
+                            "      <td>momentum_regime</td>\n",
+                            "      <td>stressed</td>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>6</td>\n",
+                            "      <td>0</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>0.168297</td>\n",
+                            "      <td>0.034426</td>\n",
+                            "      <td>0.168297</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "   regime_dimension     regime                            predictor  \\\n",
+                            "0   momentum_regime  expanding                 historical_frequency   \n",
+                            "1   momentum_regime  expanding                     logistic_c_0_001   \n",
+                            "2   momentum_regime  expanding  xgb_50_depth2_lr0_03_minchild3_l2_5   \n",
+                            "3   momentum_regime  expanding                        hybrid_anchor   \n",
+                            "4   momentum_regime  expanding                        analyst_agent   \n",
+                            "5   momentum_regime  expanding                hybrid_agent_adjusted   \n",
+                            "6   momentum_regime  expanding                       adaptive_agent   \n",
+                            "7   momentum_regime    slowing                 historical_frequency   \n",
+                            "8   momentum_regime    slowing                     logistic_c_0_001   \n",
+                            "9   momentum_regime    slowing  xgb_50_depth2_lr0_03_minchild3_l2_5   \n",
+                            "10  momentum_regime    slowing                        hybrid_anchor   \n",
+                            "11  momentum_regime    slowing                        analyst_agent   \n",
+                            "12  momentum_regime    slowing                hybrid_agent_adjusted   \n",
+                            "13  momentum_regime    slowing                       adaptive_agent   \n",
+                            "14  momentum_regime   stressed                 historical_frequency   \n",
+                            "15  momentum_regime   stressed                     logistic_c_0_001   \n",
+                            "16  momentum_regime   stressed  xgb_50_depth2_lr0_03_minchild3_l2_5   \n",
+                            "17  momentum_regime   stressed                        hybrid_anchor   \n",
+                            "18  momentum_regime   stressed                        analyst_agent   \n",
+                            "19  momentum_regime   stressed                hybrid_agent_adjusted   \n",
+                            "20  momentum_regime   stressed                       adaptive_agent   \n",
+                            "\n",
+                            "    n_origins  n_events  event_rate  mean_probability     brier  \\\n",
+                            "0          35         3    0.085714          0.065601  0.079225   \n",
+                            "1          35         3    0.085714          0.065439  0.080657   \n",
+                            "2          35         3    0.085714          0.054925  0.084213   \n",
+                            "3          35         3    0.085714          0.065439  0.080657   \n",
+                            "4          35         3    0.085714          0.097000  0.083132   \n",
+                            "5          35         3    0.085714          0.072241  0.082445   \n",
+                            "6          35         3    0.085714          0.062331  0.080829   \n",
+                            "7          43         3    0.069767          0.064595  0.065192   \n",
+                            "8          43         3    0.069767          0.065971  0.065465   \n",
+                            "9          43         3    0.069767          0.079155  0.068032   \n",
+                            "10         43         3    0.069767          0.065971  0.065465   \n",
+                            "11         43         3    0.069767          0.154884  0.073707   \n",
+                            "12         43         3    0.069767          0.085322  0.066181   \n",
+                            "13         43         3    0.069767          0.067343  0.064596   \n",
+                            "14          6         0    0.000000          0.065865  0.004342   \n",
+                            "15          6         0    0.000000          0.138297  0.025228   \n",
+                            "16          6         0    0.000000          0.177890  0.031837   \n",
+                            "17          6         0    0.000000          0.138297  0.025228   \n",
+                            "18          6         0    0.000000          0.543333  0.357800   \n",
+                            "19          6         0    0.000000          0.168297  0.034426   \n",
+                            "20          6         0    0.000000          0.168297  0.034426   \n",
+                            "\n",
+                            "    mean_probability_error  \n",
+                            "0                -0.020113  \n",
+                            "1                -0.020276  \n",
+                            "2                -0.030789  \n",
+                            "3                -0.020276  \n",
+                            "4                 0.011286  \n",
+                            "5                -0.013474  \n",
+                            "6                -0.023383  \n",
+                            "7                -0.005173  \n",
+                            "8                -0.003797  \n",
+                            "9                 0.009388  \n",
+                            "10               -0.003797  \n",
+                            "11                0.085116  \n",
+                            "12                0.015555  \n",
+                            "13               -0.002425  \n",
+                            "14                0.065865  \n",
+                            "15                0.138297  \n",
+                            "16                0.177890  \n",
+                            "17                0.138297  \n",
+                            "18                0.543333  \n",
+                            "19                0.168297  \n",
+                            "20                0.168297  "
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
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+                            "    }\n",
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+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
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+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>regime_dimension</th>\n",
+                            "      <th>regime</th>\n",
+                            "      <th>predictor</th>\n",
+                            "      <th>n_origins</th>\n",
+                            "      <th>n_events</th>\n",
+                            "      <th>event_rate</th>\n",
+                            "      <th>mean_probability</th>\n",
+                            "      <th>brier</th>\n",
+                            "      <th>mean_probability_error</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>restrictive</td>\n",
+                            "      <td>historical_frequency</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "      <td>0.065605</td>\n",
+                            "      <td>0.044632</td>\n",
+                            "      <td>0.019094</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>restrictive</td>\n",
+                            "      <td>logistic_c_0_001</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "      <td>0.077446</td>\n",
+                            "      <td>0.047573</td>\n",
+                            "      <td>0.030934</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>restrictive</td>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "      <td>0.090625</td>\n",
+                            "      <td>0.050553</td>\n",
+                            "      <td>0.044113</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>restrictive</td>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "      <td>0.077446</td>\n",
+                            "      <td>0.047573</td>\n",
+                            "      <td>0.030934</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>restrictive</td>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "      <td>0.205581</td>\n",
+                            "      <td>0.101391</td>\n",
+                            "      <td>0.159070</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>5</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>restrictive</td>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "      <td>0.096329</td>\n",
+                            "      <td>0.050368</td>\n",
+                            "      <td>0.049817</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>6</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>restrictive</td>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>43</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.046512</td>\n",
+                            "      <td>0.081270</td>\n",
+                            "      <td>0.047736</td>\n",
+                            "      <td>0.034759</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>7</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>supportive</td>\n",
+                            "      <td>historical_frequency</td>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "      <td>0.064580</td>\n",
+                            "      <td>0.089829</td>\n",
+                            "      <td>-0.032981</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>8</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>supportive</td>\n",
+                            "      <td>logistic_c_0_001</td>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "      <td>0.064066</td>\n",
+                            "      <td>0.091310</td>\n",
+                            "      <td>-0.033495</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>9</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>supportive</td>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "      <td>0.060891</td>\n",
+                            "      <td>0.094880</td>\n",
+                            "      <td>-0.036670</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>10</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>supportive</td>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "      <td>0.064066</td>\n",
+                            "      <td>0.091310</td>\n",
+                            "      <td>-0.033495</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>11</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>supportive</td>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "      <td>0.109146</td>\n",
+                            "      <td>0.094293</td>\n",
+                            "      <td>0.011585</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>12</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>supportive</td>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "      <td>0.074754</td>\n",
+                            "      <td>0.092003</td>\n",
+                            "      <td>-0.022807</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>13</th>\n",
+                            "      <td>financial_regime</td>\n",
+                            "      <td>supportive</td>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>41</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.097561</td>\n",
+                            "      <td>0.063232</td>\n",
+                            "      <td>0.091721</td>\n",
+                            "      <td>-0.034329</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "    regime_dimension       regime                            predictor  \\\n",
+                            "0   financial_regime  restrictive                 historical_frequency   \n",
+                            "1   financial_regime  restrictive                     logistic_c_0_001   \n",
+                            "2   financial_regime  restrictive  xgb_50_depth2_lr0_03_minchild3_l2_5   \n",
+                            "3   financial_regime  restrictive                        hybrid_anchor   \n",
+                            "4   financial_regime  restrictive                        analyst_agent   \n",
+                            "5   financial_regime  restrictive                hybrid_agent_adjusted   \n",
+                            "6   financial_regime  restrictive                       adaptive_agent   \n",
+                            "7   financial_regime   supportive                 historical_frequency   \n",
+                            "8   financial_regime   supportive                     logistic_c_0_001   \n",
+                            "9   financial_regime   supportive  xgb_50_depth2_lr0_03_minchild3_l2_5   \n",
+                            "10  financial_regime   supportive                        hybrid_anchor   \n",
+                            "11  financial_regime   supportive                        analyst_agent   \n",
+                            "12  financial_regime   supportive                hybrid_agent_adjusted   \n",
+                            "13  financial_regime   supportive                       adaptive_agent   \n",
+                            "\n",
+                            "    n_origins  n_events  event_rate  mean_probability     brier  \\\n",
+                            "0          43         2    0.046512          0.065605  0.044632   \n",
+                            "1          43         2    0.046512          0.077446  0.047573   \n",
+                            "2          43         2    0.046512          0.090625  0.050553   \n",
+                            "3          43         2    0.046512          0.077446  0.047573   \n",
+                            "4          43         2    0.046512          0.205581  0.101391   \n",
+                            "5          43         2    0.046512          0.096329  0.050368   \n",
+                            "6          43         2    0.046512          0.081270  0.047736   \n",
+                            "7          41         4    0.097561          0.064580  0.089829   \n",
+                            "8          41         4    0.097561          0.064066  0.091310   \n",
+                            "9          41         4    0.097561          0.060891  0.094880   \n",
+                            "10         41         4    0.097561          0.064066  0.091310   \n",
+                            "11         41         4    0.097561          0.109146  0.094293   \n",
+                            "12         41         4    0.097561          0.074754  0.092003   \n",
+                            "13         41         4    0.097561          0.063232  0.091721   \n",
+                            "\n",
+                            "    mean_probability_error  \n",
+                            "0                 0.019094  \n",
+                            "1                 0.030934  \n",
+                            "2                 0.044113  \n",
+                            "3                 0.030934  \n",
+                            "4                 0.159070  \n",
+                            "5                 0.049817  \n",
+                            "6                 0.034759  \n",
+                            "7                -0.032981  \n",
+                            "8                -0.033495  \n",
+                            "9                -0.036670  \n",
+                            "10               -0.033495  \n",
+                            "11                0.011585  \n",
+                            "12               -0.022807  \n",
+                            "13               -0.034329  "
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>regime_dimension</th>\n",
+                            "      <th>regime</th>\n",
+                            "      <th>predictor</th>\n",
+                            "      <th>n_origins</th>\n",
+                            "      <th>n_events</th>\n",
+                            "      <th>event_rate</th>\n",
+                            "      <th>mean_probability</th>\n",
+                            "      <th>brier</th>\n",
+                            "      <th>mean_probability_error</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>elevated</td>\n",
+                            "      <td>historical_frequency</td>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "      <td>0.065884</td>\n",
+                            "      <td>0.068565</td>\n",
+                            "      <td>-0.008190</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>elevated</td>\n",
+                            "      <td>logistic_c_0_001</td>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "      <td>0.085151</td>\n",
+                            "      <td>0.073298</td>\n",
+                            "      <td>0.011077</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>elevated</td>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "      <td>0.100956</td>\n",
+                            "      <td>0.076620</td>\n",
+                            "      <td>0.026882</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>elevated</td>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "      <td>0.085151</td>\n",
+                            "      <td>0.073298</td>\n",
+                            "      <td>0.011077</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>elevated</td>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "      <td>0.226296</td>\n",
+                            "      <td>0.142781</td>\n",
+                            "      <td>0.152222</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>5</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>elevated</td>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "      <td>0.106519</td>\n",
+                            "      <td>0.076467</td>\n",
+                            "      <td>0.032445</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>6</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>elevated</td>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>27</td>\n",
+                            "      <td>2</td>\n",
+                            "      <td>0.074074</td>\n",
+                            "      <td>0.092148</td>\n",
+                            "      <td>0.073652</td>\n",
+                            "      <td>0.018074</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>7</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>normal</td>\n",
+                            "      <td>historical_frequency</td>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "      <td>0.064736</td>\n",
+                            "      <td>0.065806</td>\n",
+                            "      <td>-0.005440</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>8</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>normal</td>\n",
+                            "      <td>logistic_c_0_001</td>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "      <td>0.064172</td>\n",
+                            "      <td>0.066848</td>\n",
+                            "      <td>-0.006003</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>9</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>normal</td>\n",
+                            "      <td>xgb_50_depth2_lr0_03_minchild3_l2_5</td>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "      <td>0.064343</td>\n",
+                            "      <td>0.070090</td>\n",
+                            "      <td>-0.005832</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>10</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>normal</td>\n",
+                            "      <td>hybrid_anchor</td>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "      <td>0.064172</td>\n",
+                            "      <td>0.066848</td>\n",
+                            "      <td>-0.006003</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>11</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>normal</td>\n",
+                            "      <td>analyst_agent</td>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "      <td>0.126404</td>\n",
+                            "      <td>0.076679</td>\n",
+                            "      <td>0.056228</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>12</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>normal</td>\n",
+                            "      <td>hybrid_agent_adjusted</td>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "      <td>0.075984</td>\n",
+                            "      <td>0.067953</td>\n",
+                            "      <td>0.005808</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>13</th>\n",
+                            "      <td>volatility_regime</td>\n",
+                            "      <td>normal</td>\n",
+                            "      <td>adaptive_agent</td>\n",
+                            "      <td>57</td>\n",
+                            "      <td>4</td>\n",
+                            "      <td>0.070175</td>\n",
+                            "      <td>0.063143</td>\n",
+                            "      <td>0.067099</td>\n",
+                            "      <td>-0.007033</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "     regime_dimension    regime                            predictor  \\\n",
+                            "0   volatility_regime  elevated                 historical_frequency   \n",
+                            "1   volatility_regime  elevated                     logistic_c_0_001   \n",
+                            "2   volatility_regime  elevated  xgb_50_depth2_lr0_03_minchild3_l2_5   \n",
+                            "3   volatility_regime  elevated                        hybrid_anchor   \n",
+                            "4   volatility_regime  elevated                        analyst_agent   \n",
+                            "5   volatility_regime  elevated                hybrid_agent_adjusted   \n",
+                            "6   volatility_regime  elevated                       adaptive_agent   \n",
+                            "7   volatility_regime    normal                 historical_frequency   \n",
+                            "8   volatility_regime    normal                     logistic_c_0_001   \n",
+                            "9   volatility_regime    normal  xgb_50_depth2_lr0_03_minchild3_l2_5   \n",
+                            "10  volatility_regime    normal                        hybrid_anchor   \n",
+                            "11  volatility_regime    normal                        analyst_agent   \n",
+                            "12  volatility_regime    normal                hybrid_agent_adjusted   \n",
+                            "13  volatility_regime    normal                       adaptive_agent   \n",
+                            "\n",
+                            "    n_origins  n_events  event_rate  mean_probability     brier  \\\n",
+                            "0          27         2    0.074074          0.065884  0.068565   \n",
+                            "1          27         2    0.074074          0.085151  0.073298   \n",
+                            "2          27         2    0.074074          0.100956  0.076620   \n",
+                            "3          27         2    0.074074          0.085151  0.073298   \n",
+                            "4          27         2    0.074074          0.226296  0.142781   \n",
+                            "5          27         2    0.074074          0.106519  0.076467   \n",
+                            "6          27         2    0.074074          0.092148  0.073652   \n",
+                            "7          57         4    0.070175          0.064736  0.065806   \n",
+                            "8          57         4    0.070175          0.064172  0.066848   \n",
+                            "9          57         4    0.070175          0.064343  0.070090   \n",
+                            "10         57         4    0.070175          0.064172  0.066848   \n",
+                            "11         57         4    0.070175          0.126404  0.076679   \n",
+                            "12         57         4    0.070175          0.075984  0.067953   \n",
+                            "13         57         4    0.070175          0.063143  0.067099   \n",
+                            "\n",
+                            "    mean_probability_error  \n",
+                            "0                -0.008190  \n",
+                            "1                 0.011077  \n",
+                            "2                 0.026882  \n",
+                            "3                 0.011077  \n",
+                            "4                 0.152222  \n",
+                            "5                 0.032445  \n",
+                            "6                 0.018074  \n",
+                            "7                -0.005440  \n",
+                            "8                -0.006003  \n",
+                            "9                -0.005832  \n",
+                            "10               -0.006003  \n",
+                            "11                0.056228  \n",
+                            "12                0.005808  \n",
+                            "13               -0.007033  "
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1800x500 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>logistic_weight</th>\n",
+                            "      <th>mean_brier</th>\n",
+                            "      <th>delta_vs_logistic</th>\n",
+                            "      <th>delta_vs_xgboost</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>0</th>\n",
+                            "      <td>0.0</td>\n",
+                            "      <td>0.072189</td>\n",
+                            "      <td>0.003267</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>1</th>\n",
+                            "      <td>0.1</td>\n",
+                            "      <td>0.071744</td>\n",
+                            "      <td>0.002822</td>\n",
+                            "      <td>-0.000445</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2</th>\n",
+                            "      <td>0.2</td>\n",
+                            "      <td>0.071325</td>\n",
+                            "      <td>0.002404</td>\n",
+                            "      <td>-0.000864</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>3</th>\n",
+                            "      <td>0.3</td>\n",
+                            "      <td>0.070932</td>\n",
+                            "      <td>0.002011</td>\n",
+                            "      <td>-0.001256</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>4</th>\n",
+                            "      <td>0.4</td>\n",
+                            "      <td>0.070566</td>\n",
+                            "      <td>0.001645</td>\n",
+                            "      <td>-0.001623</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>5</th>\n",
+                            "      <td>0.5</td>\n",
+                            "      <td>0.070226</td>\n",
+                            "      <td>0.001305</td>\n",
+                            "      <td>-0.001962</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>6</th>\n",
+                            "      <td>0.6</td>\n",
+                            "      <td>0.069913</td>\n",
+                            "      <td>0.000991</td>\n",
+                            "      <td>-0.002276</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>7</th>\n",
+                            "      <td>0.7</td>\n",
+                            "      <td>0.069625</td>\n",
+                            "      <td>0.000704</td>\n",
+                            "      <td>-0.002563</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>8</th>\n",
+                            "      <td>0.8</td>\n",
+                            "      <td>0.069364</td>\n",
+                            "      <td>0.000443</td>\n",
+                            "      <td>-0.002824</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>9</th>\n",
+                            "      <td>0.9</td>\n",
+                            "      <td>0.069130</td>\n",
+                            "      <td>0.000208</td>\n",
+                            "      <td>-0.003059</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>10</th>\n",
+                            "      <td>1.0</td>\n",
+                            "      <td>0.068921</td>\n",
+                            "      <td>0.000000</td>\n",
+                            "      <td>-0.003267</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "    logistic_weight  mean_brier  delta_vs_logistic  delta_vs_xgboost\n",
+                            "0               0.0    0.072189           0.003267          0.000000\n",
+                            "1               0.1    0.071744           0.002822         -0.000445\n",
+                            "2               0.2    0.071325           0.002404         -0.000864\n",
+                            "3               0.3    0.070932           0.002011         -0.001256\n",
+                            "4               0.4    0.070566           0.001645         -0.001623\n",
+                            "5               0.5    0.070226           0.001305         -0.001962\n",
+                            "6               0.6    0.069913           0.000991         -0.002276\n",
+                            "7               0.7    0.069625           0.000704         -0.002563\n",
+                            "8               0.8    0.069364           0.000443         -0.002824\n",
+                            "9               0.9    0.069130           0.000208         -0.003059\n",
+                            "10              1.0    0.068921           0.000000         -0.003267"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "from manufacturing_stress_forecasting.features import build_feature_snapshot\n",
+                "from manufacturing_stress_forecasting.regimes import (\n",
+                "    conditional_brier_table,\n",
+                "    classify_regimes,\n",
+                "    origin_only_threshold,\n",
+                ")\n",
+                "\n",
+                "\n",
+                "def cutoff_safe_regime_row(origin):\n",
+                "    origin = pd.Timestamp(origin)\n",
+                "    context = service.context(as_of=origin.to_pydatetime())\n",
+                "    feature_frames = {\n",
+                "        series_id: context.get_series(series_id)\n",
+                "        for series_id in FEATURE_SERIES_IDS\n",
+                "    }\n",
+                "    snapshot = build_feature_snapshot(origin, feature_frames)\n",
+                "    if snapshot is None:\n",
+                "        return {\n",
+                "            \"as_of\": origin,\n",
+                "            \"momentum_regime\": \"unknown\",\n",
+                "            \"financial_regime\": \"unknown\",\n",
+                "            \"volatility_regime\": \"unknown\",\n",
+                "        }\n",
+                "    credit_values = feature_frames[\"CREDIT_SPREAD\"]\n",
+                "    vix_values = feature_frames[\"VIXCLS\"]\n",
+                "    credit_visible = credit_values[pd.to_datetime(credit_values[\"released_at\"]) <= origin][\"value\"]\n",
+                "    vix_visible = vix_values[pd.to_datetime(vix_values[\"released_at\"]) <= origin][\"value\"]\n",
+                "    try:\n",
+                "        labels = classify_regimes(\n",
+                "            snapshot,\n",
+                "            historical_credit_median=origin_only_threshold(credit_visible, median=True),\n",
+                "            historical_vix_percentile=origin_only_threshold(vix_visible, percentile=0.66),\n",
+                "        )\n",
+                "    except (KeyError, ValueError):\n",
+                "        labels = {\n",
+                "            \"momentum_regime\": \"unknown\",\n",
+                "            \"financial_regime\": \"unknown\",\n",
+                "            \"volatility_regime\": \"unknown\",\n",
+                "        }\n",
+                "    return {\"as_of\": origin, **labels}\n",
+                "\n",
+                "\n",
+                "regime_rows = pd.DataFrame(\n",
+                "    [cutoff_safe_regime_row(origin) for origin in sorted(origin_details[\"as_of\"].unique())]\n",
+                ")\n",
+                "regime_details = origin_details.merge(regime_rows, on=\"as_of\", how=\"left\", validate=\"many_to_one\")\n",
+                "model_wide = (\n",
+                "    regime_details.pivot(index=[\"as_of\", \"forecast_date\", \"outcome\", \"momentum_regime\", \"financial_regime\", \"volatility_regime\"], columns=\"model\", values=\"probability\")\n",
+                "    .reset_index()\n",
+                ")\n",
+                "model_wide.columns.name = None\n",
+                "model_columns = [model for model in results if model in model_wide.columns]\n",
+                "expected_keys = set(zip(regime_details[\"as_of\"], regime_details[\"forecast_date\"], strict=True))\n",
+                "actual_keys = set(zip(model_wide[\"as_of\"], model_wide[\"forecast_date\"], strict=True))\n",
+                "assert expected_keys == actual_keys\n",
+                "assert len(model_wide) == len(expected_keys)\n",
+                "\n",
+                "print(\"Regime origin counts:\")\n",
+                "for regime_column in (\"momentum_regime\", \"financial_regime\", \"volatility_regime\"):\n",
+                "    counts = model_wide.groupby(regime_column, dropna=False).agg(\n",
+                "        origins=(\"outcome\", \"size\"),\n",
+                "        stress_events=(\"outcome\", \"sum\"),\n",
+                "        observed_event_rate=(\"outcome\", \"mean\"),\n",
+                "    )\n",
+                "    display(counts)\n",
+                "\n",
+                "for regime_column in (\"momentum_regime\", \"financial_regime\", \"volatility_regime\"):\n",
+                "    conditional = conditional_brier_table(\n",
+                "        model_wide,\n",
+                "        predictor_columns=model_columns,\n",
+                "        regime_column=regime_column,\n",
+                "        actual_column=\"outcome\",\n",
+                "    )\n",
+                "    conditional[\"regime_dimension\"] = regime_column\n",
+                "    display(conditional[[\"regime_dimension\", \"regime\", \"predictor\", \"n_origins\", \"n_events\", \"event_rate\", \"mean_probability\", \"brier\", \"mean_probability_error\"]])\n",
+                "\n",
+                "if SHOW_PLOTS:\n",
+                "    fig, axes = plt.subplots(1, 3, figsize=(18, 5), constrained_layout=True)\n",
+                "    for axis, regime_column in zip(axes, (\"momentum_regime\", \"financial_regime\", \"volatility_regime\"), strict=True):\n",
+                "        conditional = conditional_brier_table(\n",
+                "            model_wide,\n",
+                "            predictor_columns=model_columns,\n",
+                "            regime_column=regime_column,\n",
+                "            actual_column=\"outcome\",\n",
+                "        )\n",
+                "        if conditional.empty:\n",
+                "            axis.set_title(f\"{regime_column}: no data\")\n",
+                "            continue\n",
+                "        conditional.pivot(index=\"regime\", columns=\"predictor\", values=\"brier\").plot.bar(ax=axis)\n",
+                "        axis.set_title(f\"Brier by {regime_column.replace('_', ' ')}\")\n",
+                "        axis.set_ylabel(\"Brier score\")\n",
+                "        axis.tick_params(axis=\"x\", rotation=30)\n",
+                "    display(fig)\n",
+                "    plt.close(fig)\n",
+                "\n",
+                "\n",
+                "weight_rows = []\n",
+                "if {\"logistic_c_0_001\", \"xgb_50_depth2_lr0_03_minchild3_l2_5\"}.issubset(model_wide.columns):\n",
+                "    for weight in np.linspace(0.0, 1.0, 11):\n",
+                "        probability = weight * model_wide[\"logistic_c_0_001\"] + (1.0 - weight) * model_wide[\"xgb_50_depth2_lr0_03_minchild3_l2_5\"]\n",
+                "        weight_rows.append({\n",
+                "            \"logistic_weight\": round(float(weight), 1),\n",
+                "            \"mean_brier\": float(np.mean((probability - model_wide[\"outcome\"]) ** 2)),\n",
+                "            \"delta_vs_logistic\": float(np.mean((probability - model_wide[\"outcome\"]) ** 2) - comparison.loc[comparison[\"model\"] == \"logistic_c_0_001\", \"mean_brier\"].iloc[0]),\n",
+                "            \"delta_vs_xgboost\": float(np.mean((probability - model_wide[\"outcome\"]) ** 2) - comparison.loc[comparison[\"model\"] == \"xgb_50_depth2_lr0_03_minchild3_l2_5\", \"mean_brier\"].iloc[0]),\n",
+                "        })\n",
+                "    weight_results = pd.DataFrame(weight_rows)\n",
+                "    display(weight_results)\n",
+                "    if SHOW_PLOTS:\n",
+                "        weight_results.plot(x=\"logistic_weight\", y=\"mean_brier\", marker=\"o\", title=\"Offline hybrid weight sweep\")\n",
+                "        plt.show()\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "id": "50fadac4",
+            "metadata": {
+                "id": "50fadac4",
+                "language": "python"
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1800x1000 with 6 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "if SHOW_PLOTS:\n",
+                "    plot_data = origin_details.copy()\n",
+                "    plot_data['as_of'] = pd.to_datetime(plot_data['as_of'])\n",
+                "    plot_data['forecast_date'] = pd.to_datetime(plot_data['forecast_date'])\n",
+                "    fig, axes = plt.subplots(2, 3, figsize=(18, 10), constrained_layout=True)\n",
+                "\n",
+                "    comparison.sort_values('mean_brier').plot.bar(x='model', y='mean_brier', ax=axes[0, 0], legend=False, title='Mean Brier score')\n",
+                "    comparison.sort_values('brier_skill').plot.bar(x='model', y='brier_skill', ax=axes[0, 1], legend=False, title='Brier skill vs baseline')\n",
+                "\n",
+                "    for model, group in plot_data.groupby('model'):\n",
+                "        axes[0, 2].plot(group['as_of'], group['brier'], marker='o', label=model)\n",
+                "    axes[0, 2].set_title('Per-origin Brier score')\n",
+                "    axes[0, 2].legend()\n",
+                "\n",
+                "    for model, group in plot_data.groupby('model'):\n",
+                "        ordered = group.sort_values('as_of')\n",
+                "        axes[1, 0].plot(ordered['as_of'], ordered['brier'].expanding().mean(), label=model)\n",
+                "    axes[1, 0].set_title('Cumulative mean Brier')\n",
+                "    axes[1, 0].legend()\n",
+                "\n",
+                "    bins = np.linspace(0, 1, 6)\n",
+                "    centers = (bins[:-1] + bins[1:]) / 2\n",
+                "    for model, group in plot_data.groupby('model'):\n",
+                "        bucketed = group.assign(probability_bin=pd.cut(group['probability'], bins=bins, labels=False, include_lowest=True))\n",
+                "        calibration = bucketed.dropna(subset=['probability_bin']).groupby('probability_bin', observed=True).agg(\n",
+                "            predicted=('probability', 'mean'), observed=('outcome', 'mean')\n",
+                "        )\n",
+                "        axes[1, 1].plot(calibration.index.map(lambda index: centers[int(index)]), calibration['observed'], marker='o', label=model)\n",
+                "    axes[1, 1].plot([0, 1], [0, 1], 'k--', alpha=0.5)\n",
+                "    axes[1, 1].set_title('Reliability diagram')\n",
+                "    axes[1, 1].set_xlabel('Predicted probability')\n",
+                "    axes[1, 1].set_ylabel('Observed event rate')\n",
+                "    axes[1, 1].legend()\n",
+                "\n",
+                "    for model, group in plot_data.groupby('model'):\n",
+                "        axes[1, 2].hist(group['probability'], bins=bins, alpha=0.35, label=model)\n",
+                "    axes[1, 2].set_title('Probability distributions')\n",
+                "    axes[1, 2].legend()\n",
+                "\n",
+                "    for axis in axes.flat:\n",
+                "        axis.tick_params(axis='x', rotation=30)\n",
+                "    display(fig)\n",
+                "    plt.close(fig)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "ef7e67a5",
+            "metadata": {
+                "id": "ef7e67a5",
+                "language": "markdown"
+            },
+            "source": [
+                "## Interpretation\n",
+                "\n",
+                "A lower Brier score indicates better probabilistic accuracy. Positive Brier skill means improvement over historical frequency. A reliability curve near the diagonal indicates calibration. Large differences between per-origin and cumulative plots indicate regime sensitivity.\n",
+                "\n",
+                "The hybrid anchor is derived from logistic and XGBoost and is included as a baseline. When enabled, `hybrid_agent_adjusted` reports the stateless hybrid agent and `adaptive_agent` reports the stateful adaptive agent. Both use Python-bounded adjustments; their anchors, agent proposals, applied adjustments, and evidence remain available as metadata."
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": ".venv (3.12.3)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.12.3"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/implementations/manufacturing_stress_forecasting/predictors/__init__.py b/implementations/manufacturing_stress_forecasting/predictors/__init__.py
new file mode 100644
index 000000000..c5cbd12ef
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/predictors/__init__.py
@@ -0,0 +1,7 @@
+"""Predictors for the manufacturing-stress implementation."""
+
+from manufacturing_stress_forecasting.predictors.logistic import ManufacturingStressLogisticPredictor
+from manufacturing_stress_forecasting.predictors.xgboost import ManufacturingStressXGBoostPredictor
+
+
+__all__ = ["ManufacturingStressLogisticPredictor", "ManufacturingStressXGBoostPredictor"]
diff --git a/implementations/manufacturing_stress_forecasting/predictors/__pycache__/__init__.cpython-312.pyc b/implementations/manufacturing_stress_forecasting/predictors/__pycache__/__init__.cpython-312.pyc
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diff --git a/implementations/manufacturing_stress_forecasting/predictors/__pycache__/logistic.cpython-312.pyc b/implementations/manufacturing_stress_forecasting/predictors/__pycache__/logistic.cpython-312.pyc
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diff --git a/implementations/manufacturing_stress_forecasting/predictors/__pycache__/xgboost.cpython-312.pyc b/implementations/manufacturing_stress_forecasting/predictors/__pycache__/xgboost.cpython-312.pyc
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diff --git a/implementations/manufacturing_stress_forecasting/predictors/logistic.py b/implementations/manufacturing_stress_forecasting/predictors/logistic.py
new file mode 100644
index 000000000..fa6ef2f7b
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/predictors/logistic.py
@@ -0,0 +1,111 @@
+"""Logistic baseline for three-month-ahead manufacturing stress."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import numpy as np
+import pandas as pd
+from aieng.forecasting.data.context import ForecastContext
+from aieng.forecasting.evaluation.prediction import BinaryForecast, Prediction
+from aieng.forecasting.evaluation.predictor import Predictor
+from aieng.forecasting.evaluation.task import ForecastingTask
+from manufacturing_stress_forecasting.features import FEATURE_SERIES_IDS, build_feature_snapshot
+
+
+class ManufacturingStressLogisticPredictor(Predictor):
+    """Forecast manufacturing stress from IPMAN, macro, and market signals.
+
+    The model is rebuilt at every backtest origin. For each resolved historical
+    outcome at month ``r``, its feature vector is reconstructed at ``r - lead``
+    so the training examples obey the same three-month forecast horizon as the
+    current prediction.
+    """
+
+    def __init__(self, *, regularization_c: float = 1.0, min_training_examples: int = 24) -> None:
+        self._c = regularization_c
+        self._min_training_examples = min_training_examples
+
+    @property
+    def predictor_id(self) -> str:
+        """Return the stable artifact identifier."""
+        return "manufacturing_stress_logistic_ipman_rates"
+
+    def predict(self, task: ForecastingTask, context: ForecastContext) -> list[Prediction]:
+        """Fit on visible history and return one binary stress probability."""
+        if task.payload_type != "binary":
+            raise ValueError(f"{type(self).__name__} requires payload_type='binary'.")
+        if len(task.horizons) != 1:
+            raise ValueError(f"{type(self).__name__} supports exactly one horizon; got {task.horizons}.")
+
+        as_of = pd.Timestamp(context.as_of)
+        target = context.get_series(task.target_series_id)
+        feature_frames = {series_id: context.get_series(series_id) for series_id in FEATURE_SERIES_IDS}
+        lead = pd.tseries.frequencies.to_offset(task.frequency) * task.horizons[0]
+
+        rows, outcomes = self._training_data(target, feature_frames, lead)
+        current = build_feature_snapshot(as_of, feature_frames, series_ids=FEATURE_SERIES_IDS)
+        payload, model_metadata = self._fit_and_predict(rows, outcomes, current)
+
+        return [
+            Prediction(
+                predictor_id=self.predictor_id,
+                task_id=task.task_id,
+                issued_at=datetime.now(tz=timezone.utc).replace(tzinfo=None),
+                as_of=context.as_of,
+                forecast_date=(as_of + lead).to_pydatetime(),
+                payload=payload,
+                metadata={"n_train": len(outcomes), **model_metadata},
+            )
+        ]
+
+    def _training_data(
+        self,
+        target: pd.DataFrame,
+        feature_frames: dict[str, pd.DataFrame],
+        lead: pd.DateOffset,
+    ) -> tuple[list[list[float]], list[float]]:
+        rows: list[list[float]] = []
+        outcomes: list[float] = []
+        for resolution_date, outcome in zip(target["timestamp"], target["value"], strict=True):
+            past_origin = pd.Timestamp(resolution_date) - lead
+            snapshot = build_feature_snapshot(
+                past_origin,
+                feature_frames,
+                series_ids=FEATURE_SERIES_IDS,
+            )
+            if snapshot is None:
+                continue
+            rows.append([snapshot[series_id] for series_id in FEATURE_SERIES_IDS])
+            outcomes.append(float(outcome))
+        return rows, outcomes
+
+    def _fit_and_predict(
+        self,
+        rows: list[list[float]],
+        outcomes: list[float],
+        current: dict[str, float] | None,
+    ) -> tuple[BinaryForecast, dict[str, object]]:
+        base_rate = float(np.mean(outcomes)) if outcomes else 0.1
+        if current is None:
+            return BinaryForecast(probability=base_rate), {"model": "base_rate_fallback"}
+        if len(outcomes) < self._min_training_examples or len(set(outcomes)) < 2:
+            return BinaryForecast(probability=base_rate), {"model": "base_rate_fallback"}
+
+        from sklearn.linear_model import LogisticRegression  # noqa: PLC0415
+        from sklearn.pipeline import make_pipeline  # noqa: PLC0415
+        from sklearn.preprocessing import StandardScaler  # noqa: PLC0415
+
+        model = make_pipeline(StandardScaler(), LogisticRegression(C=self._c, max_iter=1000))
+        model.fit(np.asarray(rows), np.asarray(outcomes))
+        current_row = np.asarray([[current[series_id] for series_id in FEATURE_SERIES_IDS]])
+        probability = float(model.predict_proba(current_row)[0, 1])
+        coefficients = model.named_steps["logisticregression"].coef_[0]
+        return BinaryForecast(probability=probability), {
+            "model": "logistic_regression",
+            "features": dict(zip(FEATURE_SERIES_IDS, (float(value) for value in current_row[0]), strict=True)),
+            "coefficients": dict(zip(FEATURE_SERIES_IDS, (float(value) for value in coefficients), strict=True)),
+        }
+
+
+__all__ = ["ManufacturingStressLogisticPredictor"]
diff --git a/implementations/manufacturing_stress_forecasting/predictors/xgboost.py b/implementations/manufacturing_stress_forecasting/predictors/xgboost.py
new file mode 100644
index 000000000..2d7740875
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/predictors/xgboost.py
@@ -0,0 +1,79 @@
+"""XGBoost predictor for three-month-ahead manufacturing stress."""
+
+from __future__ import annotations
+
+import numpy as np
+from aieng.forecasting.evaluation.prediction import BinaryForecast
+from manufacturing_stress_forecasting.features import FEATURE_SERIES_IDS
+from manufacturing_stress_forecasting.predictors.logistic import ManufacturingStressLogisticPredictor
+
+
+class ManufacturingStressXGBoostPredictor(ManufacturingStressLogisticPredictor):
+    """Forecast manufacturing stress with a small gradient-boosted tree model."""
+
+    def __init__(
+        self,
+        *,
+        n_estimators: int = 100,
+        max_depth: int = 2,
+        learning_rate: float = 0.05,
+        min_child_weight: float = 1.0,
+        reg_lambda: float = 1.0,
+        min_training_examples: int = 24,
+    ) -> None:
+        super().__init__(min_training_examples=min_training_examples)
+        self._n_estimators = n_estimators
+        self._max_depth = max_depth
+        self._learning_rate = learning_rate
+        self._min_child_weight = min_child_weight
+        self._reg_lambda = reg_lambda
+
+    @property
+    def predictor_id(self) -> str:
+        """Return the stable artifact identifier."""
+        return "manufacturing_stress_xgboost_ipman_rates"
+
+    def _fit_and_predict(
+        self,
+        rows: list[list[float]],
+        outcomes: list[float],
+        current: dict[str, float] | None,
+    ) -> tuple[BinaryForecast, dict[str, object]]:
+        base_rate = float(np.mean(outcomes)) if outcomes else 0.1
+        if current is None:
+            return BinaryForecast(probability=base_rate), {"model": "base_rate_fallback"}
+        if len(outcomes) < self._min_training_examples or len(set(outcomes)) < 2:
+            return BinaryForecast(probability=base_rate), {"model": "base_rate_fallback"}
+
+        from xgboost import XGBClassifier  # noqa: PLC0415
+
+        model = XGBClassifier(
+            n_estimators=self._n_estimators,
+            max_depth=self._max_depth,
+            learning_rate=self._learning_rate,
+            min_child_weight=self._min_child_weight,
+            reg_lambda=self._reg_lambda,
+            objective="binary:logistic",
+            eval_metric="logloss",
+            subsample=0.8,
+            colsample_bytree=0.8,
+            random_state=42,
+            n_jobs=1,
+        )
+        model.fit(np.asarray(rows), np.asarray(outcomes))
+        current_row = np.asarray([[current[series_id] for series_id in FEATURE_SERIES_IDS]])
+        probability = float(model.predict_proba(current_row)[0, 1])
+        return BinaryForecast(probability=probability), {
+            "model": "xgboost_classifier",
+            "features": dict(zip(FEATURE_SERIES_IDS, (float(value) for value in current_row[0]), strict=True)),
+            "feature_importances": dict(
+                zip(
+                    FEATURE_SERIES_IDS,
+                    (float(value) for value in model.feature_importances_),
+                    strict=True,
+                )
+            ),
+        }
+
+
+__all__ = ["ManufacturingStressXGBoostPredictor"]
diff --git a/implementations/manufacturing_stress_forecasting/regimes.py b/implementations/manufacturing_stress_forecasting/regimes.py
new file mode 100644
index 000000000..6af3917e6
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/regimes.py
@@ -0,0 +1,109 @@
+"""Cutoff-safe regime labels and conditional evaluation helpers."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+
+import numpy as np
+import pandas as pd
+from manufacturing_stress_forecasting.features import (
+    CREDIT_SPREAD_SERIES_ID,
+    IPMAN_CHANGE_3M_SERIES_ID,
+    VIXCLS_SERIES_ID,
+    YC_SPREAD_SERIES_ID,
+)
+
+
+def classify_momentum(value: float) -> str:
+    """Classify three-month IPMAN momentum using the target threshold."""
+    if value > 0.0:
+        return "expanding"
+    if value > -2.0:
+        return "slowing"
+    return "stressed"
+
+
+def classify_financial_conditions(yield_curve: float, credit_spread: float, historical_credit_median: float) -> str:
+    """Classify financial conditions from contemporaneous observable spreads."""
+    if yield_curve >= 0.0 and credit_spread < historical_credit_median:
+        return "supportive"
+    return "restrictive"
+
+
+def classify_volatility(value: float, historical_vix_percentile: float) -> str:
+    """Classify VIX relative to an origin-only historical percentile."""
+    return "elevated" if value >= historical_vix_percentile else "normal"
+
+
+def classify_regimes(
+    snapshot: Mapping[str, float],
+    *,
+    historical_credit_median: float,
+    historical_vix_percentile: float,
+) -> dict[str, str]:
+    """Return the three independent regime labels for one origin."""
+    required = (IPMAN_CHANGE_3M_SERIES_ID, YC_SPREAD_SERIES_ID, CREDIT_SPREAD_SERIES_ID, VIXCLS_SERIES_ID)
+    missing = [series_id for series_id in required if series_id not in snapshot]
+    if missing:
+        raise KeyError(f"Missing regime features: {missing}")
+    return {
+        "momentum_regime": classify_momentum(snapshot[IPMAN_CHANGE_3M_SERIES_ID]),
+        "financial_regime": classify_financial_conditions(
+            snapshot[YC_SPREAD_SERIES_ID],
+            snapshot[CREDIT_SPREAD_SERIES_ID],
+            historical_credit_median,
+        ),
+        "volatility_regime": classify_volatility(snapshot[VIXCLS_SERIES_ID], historical_vix_percentile),
+    }
+
+
+def conditional_brier_table(
+    frame: pd.DataFrame,
+    *,
+    predictor_columns: Sequence[str],
+    regime_column: str,
+    actual_column: str = "actual",
+) -> pd.DataFrame:
+    """Calculate conditional Brier scores and calibration summaries."""
+    rows: list[dict[str, object]] = []
+    for regime, group in frame.dropna(subset=[regime_column, actual_column]).groupby(regime_column, sort=True):
+        actual = group[actual_column].astype(float)
+        for predictor in predictor_columns:
+            probability = group[predictor].astype(float)
+            rows.append(
+                {
+                    "regime": regime,
+                    "predictor": predictor,
+                    "n_origins": len(group),
+                    "n_events": int(actual.sum()),
+                    "event_rate": float(actual.mean()),
+                    "mean_probability": float(probability.mean()),
+                    "brier": float(np.mean((probability - actual) ** 2)),
+                    "mean_probability_error": float((probability - actual).mean()),
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def origin_only_threshold(values: pd.Series, *, percentile: float | None = None, median: bool = False) -> float:
+    """Return a finite threshold from values visible at one forecast origin."""
+    clean = pd.to_numeric(values, errors="coerce").dropna()
+    if clean.empty:
+        raise ValueError("Cannot calculate a regime threshold from empty values.")
+    if median:
+        return float(clean.median())
+    if percentile is None:
+        raise ValueError("Provide percentile or set median=True.")
+    if not 0.0 <= percentile <= 1.0:
+        raise ValueError(f"percentile must be between 0 and 1; got {percentile}.")
+    return float(clean.quantile(percentile))
+
+
+__all__ = [
+    "classify_financial_conditions",
+    "classify_momentum",
+    "classify_regimes",
+    "classify_volatility",
+    "conditional_brier_table",
+    "origin_only_threshold",
+]
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/adaptation_audit.csv b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/adaptation_audit.csv
new file mode 100644
index 000000000..3ec3fac37
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/adaptation_audit.csv
@@ -0,0 +1,10 @@
+timestamp,event,tool,strategy_path,strategy_version,result,state_before_sha256,state_after_sha256
+2026-10-05 02:54:07.456436+00:00,strategy_mutation,open_hypothesis,manufacturing-strategy,5ca0f5e2e3e9,Opened hyp-001; 3 confirmations are required before graduation.,3118aab6b065cc0c5339d828859e9c46c99fe51b6f7f27364888c4e7cb9e0c27,5ca0f5e2e3e91523872ec8861390931044e6d178a6fd3770e023ffaa7a26a73f
+2026-10-05 02:54:08.621283+00:00,strategy_mutation,record_observation,manufacturing-strategy,7fdc705e7171,Manufacturing observation recorded.,5ca0f5e2e3e91523872ec8861390931044e6d178a6fd3770e023ffaa7a26a73f,7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47bf49c3f8aa55dfefcc
+2026-10-05 02:54:09.462313+00:00,strategy_mutation,record_observation,manufacturing-strategy,89ab0cd60924,Manufacturing observation recorded.,7fdc705e71712f8a5ca0344cc1a5316521bbbab6f49e47bf49c3f8aa55dfefcc,89ab0cd60924f10c4fba9a874bc544131a5dee5432c5617a68ab01c4af3c352b
+2026-10-05 02:54:10.266227+00:00,strategy_mutation,record_observation,manufacturing-strategy,aba23959efdb,Manufacturing observation recorded.,89ab0cd60924f10c4fba9a874bc544131a5dee5432c5617a68ab01c4af3c352b,aba23959efdb5dc1dafc7e8c243f039ee458a725004a99d10aa60bc6ecfc1923
+2026-10-05 02:54:13.757865+00:00,strategy_mutation,record_hypothesis_outcome,manufacturing-strategy,8ce7d315ce31,Recorded confirmed outcome for hyp-001.,aba23959efdb5dc1dafc7e8c243f039ee458a725004a99d10aa60bc6ecfc1923,8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc90d2734272ad7178c7
+2026-10-05 02:54:14.363148+00:00,strategy_mutation,record_hypothesis_outcome,manufacturing-strategy,8d90e2d77d9b,Recorded confirmed outcome for hyp-001.,8ce7d315ce31f829da8c04afa2accc84b05cbd951928bc90d2734272ad7178c7,8d90e2d77d9b0516845227849de75069c993bfa6da9a5d6d9004180e955f3f04
+2026-10-05 02:54:15.129828+00:00,strategy_mutation,record_hypothesis_outcome,manufacturing-strategy,48997251b9f0,Recorded confirmed outcome for hyp-001.,8d90e2d77d9b0516845227849de75069c993bfa6da9a5d6d9004180e955f3f04,48997251b9f02b73386e9e2a426f2c2901161db838612b77b6b738a0bdf6dac7
+2026-10-05 02:54:16.158524+00:00,strategy_mutation,graduate_hypothesis,manufacturing-strategy,609d8b85ec48,Graduated hyp-001 into a calibration correction.,48997251b9f02b73386e9e2a426f2c2901161db838612b77b6b738a0bdf6dac7,609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737b07ccdc643d1151f78
+2026-10-05 02:54:17.079875+00:00,strategy_mutation,update_approach_narrative,manufacturing-strategy,ee7d4b090d88,Manufacturing approach narrative updated.,609d8b85ec48e191b01d20713b40aa6f5cf7c8d717d737b07ccdc643d1151f78,ee7d4b090d8898fca705b884e399d1a7df6d8cf6d3e3814f019b42be2f898497
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/adaptation_timeline.png b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/adaptation_timeline.png
new file mode 100644
index 000000000..44dea5e3d
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/adaptation_timeline.png differ
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/mutation_activity.png b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/mutation_activity.png
new file mode 100644
index 000000000..6cf5877a8
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/mutation_activity.png differ
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/mutation_summary.csv b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/mutation_summary.csv
new file mode 100644
index 000000000..42ebd1b25
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_agent_workbench/mutation_summary.csv
@@ -0,0 +1,6 @@
+tool,mutations
+record_observation,3
+record_hypothesis_outcome,3
+open_hypothesis,1
+graduate_hypothesis,1
+update_approach_narrative,1
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/README.md b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/README.md
new file mode 100644
index 000000000..e36931f48
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/README.md
@@ -0,0 +1,25 @@
+# Adaptive-model comparison report
+
+This directory contains the executive report for the controlled Lite-versus-Advanced
+manufacturing-stress adaptive-agent comparison.
+
+## Contents
+
+- `executive_report.md` — experiment setup, results, limitations, and figures.
+- `tables/model_summary.csv` — aggregate Brier scores and sample counts.
+- `tables/paired_origin_predictions.csv` — all 84 paired origins, probabilities,
+  outcomes, per-origin Brier scores, model-stated directions, rationales, and
+  evidence.
+- `tables/calibration_bins.csv` — the numeric source for the calibration chart,
+  including counts and values for empty bins.
+- `figures/` — presentation-ready PNG plots.
+- `run_manifest.json` — cached artifact IDs, run settings, timestamps, and
+  chart-to-source-table mappings.
+- `build_report.py` — rebuilds report tables and figures from the saved backtest
+  cache without making model calls.
+
+Regenerate from the repository root with:
+
+```bash
+uv run python implementations/manufacturing_stress_forecasting/reports/adaptive_compare/build_report.py
+```
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/build_report.py b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/build_report.py
new file mode 100644
index 000000000..634ec8f6c
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/build_report.py
@@ -0,0 +1,456 @@
+"""Build an executive comparison package from cached adaptive-agent backtests.
+
+This script reads saved Lite and Advanced backtest artifacts; it never calls an
+LLM. Run it from the repository root or invoke it by its absolute path.
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import matplotlib as mpl
+
+
+mpl.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import yaml
+
+
+REPO_ROOT = Path(__file__).resolve().parents[4]
+for package_root in (REPO_ROOT / "aieng-forecasting", REPO_ROOT / "implementations"):
+    if str(package_root) not in sys.path:
+        sys.path.insert(0, str(package_root))
+
+from aieng.forecasting.evaluation.backtest import BacktestResult
+
+
+CACHE_DIR = REPO_ROOT / "data" / "predictions" / "manufacturing_stress_adaptive_model_comparison"
+OUTPUT_DIR = Path(__file__).resolve().parent
+TABLES_DIR = OUTPUT_DIR / "tables"
+FIGURES_DIR = OUTPUT_DIR / "figures"
+
+MODEL_VARIANTS = {
+    "adaptive_lite": {
+        "model": "gemini-3.1-flash-lite-preview",
+        "label": "Lite",
+        "color": "#2f6f8f",
+    },
+    "adaptive_advanced": {
+        "model": "gemini-3.5-flash",
+        "label": "Advanced",
+        "color": "#d9822b",
+    },
+}
+CALIBRATION_EDGES = np.linspace(0.0, 1.0, 6)
+OUTCOME_TOLERANCE = 1e-8
+
+
+def _load_complete_pair() -> tuple[str, dict[str, BacktestResult]]:
+    """Select the fullest complete monthly Lite/Advanced cache pair."""
+    candidates: dict[str, dict[str, BacktestResult]] = {}
+    for path in CACHE_DIR.glob("*/*.yaml"):
+        try:
+            result = BacktestResult.model_validate(yaml.safe_load(path.read_text(encoding="utf-8")))
+        except (OSError, ValueError, TypeError):
+            continue
+        if result.metric != "brier" or result.skipped_origins:
+            continue
+        for variant, details in MODEL_VARIANTS.items():
+            if details["model"] in result.predictor_id and "read_only" in result.predictor_id:
+                candidates.setdefault(path.parent.name, {})[variant] = result
+
+    complete = []
+    for cache_id, results in candidates.items():
+        if set(results) != set(MODEL_VARIANTS):
+            continue
+        lite, advanced = (results[name] for name in MODEL_VARIANTS)
+        if lite.spec != advanced.spec or lite.spec.stride != 1:
+            continue
+        lite_keys = {(p.as_of, p.forecast_date) for p in lite.predictions}
+        advanced_keys = {(p.as_of, p.forecast_date) for p in advanced.predictions}
+        if lite_keys != advanced_keys:
+            continue
+        expected_origins = len(lite.spec.origins())
+        if len(lite.predictions) != expected_origins or len(advanced.predictions) != expected_origins:
+            continue
+        complete.append((len(lite.predictions), max(lite.ran_at, advanced.ran_at), cache_id, results))
+
+    if not complete:
+        raise FileNotFoundError(
+            "No complete paired monthly Lite/Advanced result was found in "
+            f"{CACHE_DIR}. The report requires matching 1-month-stride caches with no skipped origins."
+        )
+    _, _, cache_id, results = max(complete, key=lambda item: (item[0], item[1]))
+    return cache_id, results
+
+
+def _recover_outcome(probability: float, score: float, *, source: str) -> int:
+    """Recover the binary outcome encoded by the saved per-forecast Brier score."""
+    distances = {
+        0: abs(score - probability**2),
+        1: abs(score - (1.0 - probability) ** 2),
+    }
+    outcome = min(distances, key=distances.get)
+    if distances[outcome] > OUTCOME_TOLERANCE:
+        raise ValueError(f"Could not reconcile binary outcome with saved Brier score for {source}.")
+    if abs(distances[0] - distances[1]) <= OUTCOME_TOLERANCE:
+        raise ValueError(f"Saved score does not uniquely identify a binary outcome for {source}.")
+    return outcome
+
+
+def _metadata_text(value: Any) -> str:
+    if value is None:
+        return ""
+    if isinstance(value, list):
+        return "; ".join(str(item) for item in value)
+    return str(value)
+
+
+def _origin_rows(results: dict[str, BacktestResult]) -> pd.DataFrame:
+    rows: list[dict[str, Any]] = []
+    for variant, result in results.items():
+        for prediction, score in zip(result.predictions, result.scores, strict=True):
+            probability = float(prediction.payload.probability)
+            source = f"{variant} at {prediction.as_of:%Y-%m-%d}"
+            outcome = _recover_outcome(probability, float(score), source=source)
+            metadata = prediction.metadata
+            rows.append(
+                {
+                    "model": variant,
+                    "as_of": prediction.as_of.date().isoformat(),
+                    "forecast_date": prediction.forecast_date.date().isoformat(),
+                    "probability": probability,
+                    "observed_outcome": outcome,
+                    "brier_score": float(score),
+                    "risk_direction": metadata.get("direction", ""),
+                    "rationale": _metadata_text(metadata.get("rationale")),
+                    "supporting_evidence": _metadata_text(metadata.get("supporting_evidence")),
+                    "countervailing_evidence": _metadata_text(metadata.get("countervailing_evidence")),
+                }
+            )
+
+    long = pd.DataFrame(rows).sort_values(["forecast_date", "model"]).reset_index(drop=True)
+    outcomes_per_date = long.groupby("forecast_date")["observed_outcome"].nunique()
+    if (outcomes_per_date != 1).any():
+        raise ValueError("The paired model artifacts imply different observed outcomes.")
+
+    pivot_fields = [
+        "probability",
+        "brier_score",
+        "risk_direction",
+        "rationale",
+        "supporting_evidence",
+        "countervailing_evidence",
+    ]
+    wide = long.pivot(index="forecast_date", columns="model", values=pivot_fields)
+    wide.columns = [f"{model}_{field}" for field, model in wide.columns]
+    wide = wide.reset_index()
+    shared = long.drop_duplicates("forecast_date").set_index("forecast_date")
+    wide.insert(0, "as_of", shared.loc[wide["forecast_date"], "as_of"].to_numpy())
+    wide.insert(2, "observed_outcome", shared.loc[wide["forecast_date"], "observed_outcome"].to_numpy())
+    wide["probability_delta_advanced_minus_lite"] = (
+        wide["adaptive_advanced_probability"] - wide["adaptive_lite_probability"]
+    )
+    wide["risk_direction_agrees"] = wide["adaptive_advanced_risk_direction"] == wide["adaptive_lite_risk_direction"]
+    return wide
+
+
+def _model_summary(
+    results: dict[str, BacktestResult],
+    paired: pd.DataFrame,
+) -> pd.DataFrame:
+    event_count = int(paired["observed_outcome"].sum())
+    event_rate = float(paired["observed_outcome"].mean())
+    rows = []
+    for variant, details in MODEL_VARIANTS.items():
+        result = results[variant]
+        rows.append(
+            {
+                "model": variant,
+                "model_label": details["label"],
+                "model_id": details["model"],
+                "metric": result.metric,
+                "mean_brier": float(result.mean_score),
+                "scored_origins": len(result.predictions),
+                "skipped_origins": result.skipped_origins,
+                "observed_stress_events": event_count,
+                "observed_event_rate": event_rate,
+                "mean_probability": float(paired[f"{variant}_probability"].mean()),
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def _calibration_table(paired: pd.DataFrame) -> pd.DataFrame:
+    labels = [
+        f"{left:.1f}-{right:.1f}" for left, right in zip(CALIBRATION_EDGES[:-1], CALIBRATION_EDGES[1:], strict=True)
+    ]
+    rows = []
+    for variant, details in MODEL_VARIANTS.items():
+        probability = paired[f"{variant}_probability"]
+        bucket = pd.cut(
+            probability,
+            bins=CALIBRATION_EDGES,
+            labels=labels,
+            include_lowest=True,
+            right=True,
+        )
+        for label in labels:
+            mask = bucket == label
+            count = int(mask.sum())
+            rows.append(
+                {
+                    "model": variant,
+                    "model_label": details["label"],
+                    "probability_bin": label,
+                    "bin_lower": float(label.split("-")[0]),
+                    "bin_upper": float(label.split("-")[1]),
+                    "origins": count,
+                    "mean_predicted_probability": float(probability[mask].mean()) if count else np.nan,
+                    "observed_event_rate": float(paired.loc[mask, "observed_outcome"].mean()) if count else np.nan,
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def _save_figures(
+    summary: pd.DataFrame,
+    paired: pd.DataFrame,
+    calibration: pd.DataFrame,
+) -> None:
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    colors = [MODEL_VARIANTS[model]["color"] for model in summary["model"]]
+
+    fig, ax = plt.subplots(figsize=(7.5, 4.5), constrained_layout=True)
+    bars = ax.bar(summary["model_label"], summary["mean_brier"], color=colors)
+    ax.bar_label(bars, fmt="%.4f", padding=3)
+    ax.set_title("Mean Brier score (lower is better)")
+    ax.set_ylabel("Mean Brier score")
+    ax.set_ylim(0, float(summary["mean_brier"].max()) * 1.2)
+    ax.grid(axis="y", alpha=0.25)
+    fig.savefig(FIGURES_DIR / "mean_brier_scores.png", dpi=180)
+    plt.close(fig)
+
+    dates = pd.to_datetime(paired["forecast_date"])
+    fig, ax_probability = plt.subplots(figsize=(12, 5), constrained_layout=True)
+    for variant, details in MODEL_VARIANTS.items():
+        ax_probability.plot(
+            dates,
+            paired[f"{variant}_probability"],
+            marker=".",
+            markersize=4,
+            linewidth=1,
+            label=f"{details['label']} probability",
+            color=details["color"],
+        )
+    ax_outcome = ax_probability.twinx()
+    ax_outcome.step(
+        dates,
+        paired["observed_outcome"],
+        where="mid",
+        color="#333333",
+        alpha=0.45,
+        linewidth=1,
+        label="Observed stress outcome",
+    )
+    ax_probability.set_title("Forecast probabilities and resolved stress outcomes")
+    ax_probability.set_ylabel("Forecast probability")
+    ax_probability.set_ylim(0, min(1.0, max(0.15, float(paired.filter(like="_probability").max().max()) * 1.15)))
+    ax_outcome.set_ylabel("Observed outcome (0/1)")
+    ax_outcome.set_ylim(-0.05, 1.05)
+    ax_probability.grid(alpha=0.2)
+    handles_1, labels_1 = ax_probability.get_legend_handles_labels()
+    handles_2, labels_2 = ax_outcome.get_legend_handles_labels()
+    ax_probability.legend(handles_1 + handles_2, labels_1 + labels_2, loc="upper left", ncol=2, fontsize=8)
+    fig.savefig(FIGURES_DIR / "forecast_probabilities.png", dpi=180)
+    plt.close(fig)
+
+    fig, ax = plt.subplots(figsize=(12, 4.5), constrained_layout=True)
+    differences = paired["probability_delta_advanced_minus_lite"]
+    ax.bar(dates, differences, width=20, color="#b85c38")
+    ax.axhline(0, color="#333333", linewidth=0.8)
+    ax.set_title("Advanced minus Lite forecast probability")
+    ax.set_ylabel("Probability difference")
+    ax.grid(axis="y", alpha=0.2)
+    fig.savefig(FIGURES_DIR / "probability_difference.png", dpi=180)
+    plt.close(fig)
+
+    fig, ax = plt.subplots(figsize=(7, 5), constrained_layout=True)
+    for variant, details in MODEL_VARIANTS.items():
+        points = calibration.loc[(calibration["model"] == variant) & (calibration["origins"] > 0)]
+        ax.plot(
+            points["mean_predicted_probability"],
+            points["observed_event_rate"],
+            marker="o",
+            label=details["label"],
+            color=details["color"],
+        )
+    ax.plot([0, 1], [0, 1], linestyle="--", color="#777777", label="Perfect calibration")
+    ax.set_title("Calibration by probability bin")
+    ax.set_xlabel("Mean predicted probability")
+    ax.set_ylabel("Observed event rate")
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.grid(alpha=0.2)
+    ax.legend()
+    fig.savefig(FIGURES_DIR / "calibration.png", dpi=180)
+    plt.close(fig)
+
+
+def _write_report(
+    *,
+    cache_id: str,
+    results: dict[str, BacktestResult],
+    paired: pd.DataFrame,
+    summary: pd.DataFrame,
+    calibration: pd.DataFrame,
+) -> None:
+    lite_score = float(summary.loc[summary["model"] == "adaptive_lite", "mean_brier"].iloc[0])
+    advanced_score = float(summary.loc[summary["model"] == "adaptive_advanced", "mean_brier"].iloc[0])
+    score_delta = advanced_score - lite_score
+    direction_agreements = int(paired["risk_direction_agrees"].sum())
+    origin_count = len(paired)
+    event_count = int(paired["observed_outcome"].sum())
+    start_origin = paired["as_of"].min()
+    end_origin = paired["as_of"].max()
+    first_result = results["adaptive_lite"]
+    first_prediction = first_result.predictions[0]
+    last_prediction = first_result.predictions[-1]
+
+    summary_table = summary[
+        ["model_label", "model_id", "mean_brier", "scored_origins", "skipped_origins", "observed_stress_events"]
+    ].to_markdown(index=False, floatfmt=".6f")
+    report = f"""# Manufacturing stress adaptive-model comparison
+
+## Executive summary
+
+This report compares the Lite and Advanced adaptive agents on **{origin_count} paired monthly forecast origins**. The lower mean Brier score in this saved historical run was **{min(lite_score, advanced_score):.6f}** ({"Advanced" if advanced_score < lite_score else "Lite"}). The Advanced-minus-Lite Brier difference was **{score_delta:+.6f}**. Model-stated risk directions matched on **{direction_agreements} of {origin_count} origins ({direction_agreements / origin_count:.1%})**. Direction agreement is a descriptive comparison, not an accuracy measure: the experiment defines no binary decision threshold.
+
+| Model | Model ID | Mean Brier (lower is better) | Scored origins | Skipped origins | Observed stress events |
+|---|---|---:|---:|---:|---:|
+{summary_table.splitlines()[2]}
+{summary_table.splitlines()[3]}
+
+The scores summarize this historical sample only; they do not establish which model will perform better in future periods.
+
+## Experiment
+
+- **Question:** estimate the probability that U.S. manufacturing will be under IPMAN-defined stress three months after each forecast origin.
+- **Outcome:** stress is `1` when the three-month IPMAN percentage change is at or below `-2%`; otherwise it is `0`.
+- **Origins:** {start_origin} through {end_origin}, monthly stride (1 month); forecast dates run {first_prediction.forecast_date:%Y-%m-%d} through {last_prediction.forecast_date:%Y-%m-%d}.
+- **Horizon:** 3 months. Since origins are monthly but forecast horizons are three months, adjacent forecast targets overlap.
+- **Warm-up:** {first_result.spec.warmup} months, as specified in the saved backtest.
+- **Scoring:** binary Brier score, `mean((probability - observed_outcome) ** 2)`; lower is better.
+- **Observed events:** {event_count} of {origin_count} forecast outcomes ({event_count / origin_count:.1%}).
+- **Mutation:** disabled; both models used the read-only comparison variant.
+- **Data source:** cached paired backtest artifacts in `{cache_id}`. Report generation made no LLM calls.
+- **Outcome values:** the backtest artifact stores each prediction and its Brier score. The binary outcome in the origin table is recovered from that score and probability, validated against the binary Brier formula, and required to match between the two models.
+
+## Results
+
+![Mean Brier scores](figures/mean_brier_scores.png)
+
+The bars above use `tables/model_summary.csv`.
+
+![Forecast probabilities with observed outcomes](figures/forecast_probabilities.png)
+
+This chart plots both model probabilities and the observed binary outcome by forecast date. Its numeric source is `tables/paired_origin_predictions.csv`.
+
+![Advanced-minus-Lite probability differences](figures/probability_difference.png)
+
+Positive values indicate a higher Advanced probability; negative values indicate a higher Lite probability. The exact plotted series is the `probability_delta_advanced_minus_lite` column in `tables/paired_origin_predictions.csv`.
+
+![Calibration by probability bin](figures/calibration.png)
+
+The calibration chart compares the mean forecast probability with the observed event rate in each non-empty fixed-width 0.2 probability bin. Exact means, event rates, and counts—including empty bins—are in `tables/calibration_bins.csv`. The observed event count is small, so calibration points are descriptive and uncertain.
+
+## Decision rationale
+
+The origin-level table retains each model's probability, risk direction, concise rationale, supporting evidence, countervailing evidence, per-origin Brier score, and direction-agreement indicator. This is the source for examining how the models reached different or similar assessments; the reported rationale and evidence are model-generated explanations, not independent causal attribution.
+
+## Limitations
+
+- Monthly forecasts share overlapping three-month target windows, so origin-level results are dependent. No independent-observation significance test is reported.
+- The event is uncommon in this sample ({event_count}/{origin_count}), limiting the stability of calibration and aggregate comparisons.
+- No binary action threshold is defined. The model-stated `up`, `down`, and `neutral` directions are reported verbatim and are not converted into classifications.
+- Historical input revisions and release timing are subject to the use case's documented data-cutoff assumptions.
+
+## Included files
+
+- `tables/model_summary.csv` — aggregate scores, scored/skipped counts, event prevalence, and mean forecast probability.
+- `tables/paired_origin_predictions.csv` — all paired origin-level numeric values and model rationales/evidence.
+- `tables/calibration_bins.csv` — every calibration bin's source values and origin count.
+- `run_manifest.json` — cache identifier, specification, artifact timestamps, and chart-to-data mapping.
+- `build_report.py` — rebuilds figures and tables from saved cache artifacts without making LLM calls.
+"""
+    OUTPUT_DIR.joinpath("executive_report.md").write_text(report, encoding="utf-8")
+
+
+def main() -> None:
+    """Build the report tables, figures, manifest, and executive summary."""
+    cache_id, results = _load_complete_pair()
+    paired = _origin_rows(results)
+    summary = _model_summary(results, paired)
+    calibration = _calibration_table(paired)
+
+    TABLES_DIR.mkdir(parents=True, exist_ok=True)
+    summary.to_csv(TABLES_DIR / "model_summary.csv", index=False, float_format="%.12g")
+    paired.to_csv(TABLES_DIR / "paired_origin_predictions.csv", index=False, float_format="%.12g")
+    calibration.to_csv(TABLES_DIR / "calibration_bins.csv", index=False, float_format="%.12g")
+    _save_figures(summary, paired, calibration)
+
+    manifest = {
+        "built_at_utc": datetime.now(timezone.utc).isoformat(),
+        "cache_id": cache_id,
+        "cache_directory": (CACHE_DIR / cache_id).relative_to(REPO_ROOT).as_posix(),
+        "report_directory": OUTPUT_DIR.relative_to(REPO_ROOT).as_posix(),
+        "spec": results["adaptive_lite"].spec.model_dump(mode="json"),
+        "models": {
+            variant: {
+                **details,
+                "predictor_id": results[variant].predictor_id,
+                "ran_at": results[variant].ran_at.isoformat(),
+                "mean_brier": float(results[variant].mean_score),
+                "scored_origins": len(results[variant].predictions),
+                "skipped_origins": results[variant].skipped_origins,
+            }
+            for variant, details in MODEL_VARIANTS.items()
+        },
+        "paired_origins": len(paired),
+        "observed_stress_events": int(paired["observed_outcome"].sum()),
+        "risk_direction_agreements": int(paired["risk_direction_agrees"].sum()),
+        "charts": {
+            "figures/mean_brier_scores.png": "tables/model_summary.csv",
+            "figures/forecast_probabilities.png": "tables/paired_origin_predictions.csv",
+            "figures/probability_difference.png": "tables/paired_origin_predictions.csv",
+            "figures/calibration.png": "tables/calibration_bins.csv",
+        },
+        "llm_calls_during_report_build": 0,
+    }
+    OUTPUT_DIR.joinpath("run_manifest.json").write_text(
+        json.dumps(manifest, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    _write_report(
+        cache_id=cache_id,
+        results=results,
+        paired=paired,
+        summary=summary,
+        calibration=calibration,
+    )
+    print(
+        f"Built adaptive comparison report: {OUTPUT_DIR} "
+        f"({len(paired)} paired origins; risk directions agree on "
+        f"{manifest['risk_direction_agreements']}/{len(paired)})."
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/executive_report.md b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/executive_report.md
new file mode 100644
index 000000000..6c48211ba
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/executive_report.md
@@ -0,0 +1,62 @@
+# Manufacturing stress adaptive-model comparison
+
+## Executive summary
+
+This report compares the Lite and Advanced adaptive agents on **84 paired monthly forecast origins**. The lower mean Brier score in this saved historical run was **0.070457** (Advanced). The Advanced-minus-Lite Brier difference was **-0.019690**. Model-stated risk directions matched on **50 of 84 origins (59.5%)**. Direction agreement is a descriptive comparison, not an accuracy measure: the experiment defines no binary decision threshold.
+
+| Model | Model ID | Mean Brier (lower is better) | Scored origins | Skipped origins | Observed stress events |
+|---|---|---:|---:|---:|---:|
+| Lite          | gemini-3.1-flash-lite-preview |     0.090147 |               84 |                 0 |                        6 |
+| Advanced      | gemini-3.5-flash              |     0.070457 |               84 |                 0 |                        6 |
+
+The scores summarize this historical sample only; they do not establish which model will perform better in future periods.
+
+## Experiment
+
+- **Question:** estimate the probability that U.S. manufacturing will be under IPMAN-defined stress three months after each forecast origin.
+- **Outcome:** stress is `1` when the three-month IPMAN percentage change is at or below `-2%`; otherwise it is `0`.
+- **Origins:** 2018-01-01 through 2024-12-01, monthly stride (1 month); forecast dates run 2018-04-01 through 2025-03-01.
+- **Horizon:** 3 months. Since origins are monthly but forecast horizons are three months, adjacent forecast targets overlap.
+- **Warm-up:** 60 months, as specified in the saved backtest.
+- **Scoring:** binary Brier score, `mean((probability - observed_outcome) ** 2)`; lower is better.
+- **Observed events:** 6 of 84 forecast outcomes (7.1%).
+- **Mutation:** disabled; both models used the read-only comparison variant.
+- **Data source:** cached paired backtest artifacts in `manufacturing_stress_smoke_llmp_v3_14b1deebe4`. Report generation made no LLM calls.
+- **Outcome values:** the backtest artifact stores each prediction and its Brier score. The binary outcome in the origin table is recovered from that score and probability, validated against the binary Brier formula, and required to match between the two models.
+
+## Results
+
+![Mean Brier scores](figures/mean_brier_scores.png)
+
+The bars above use `tables/model_summary.csv`.
+
+![Forecast probabilities with observed outcomes](figures/forecast_probabilities.png)
+
+This chart plots both model probabilities and the observed binary outcome by forecast date. Its numeric source is `tables/paired_origin_predictions.csv`.
+
+![Advanced-minus-Lite probability differences](figures/probability_difference.png)
+
+Positive values indicate a higher Advanced probability; negative values indicate a higher Lite probability. The exact plotted series is the `probability_delta_advanced_minus_lite` column in `tables/paired_origin_predictions.csv`.
+
+![Calibration by probability bin](figures/calibration.png)
+
+The calibration chart compares the mean forecast probability with the observed event rate in each non-empty fixed-width 0.2 probability bin. Exact means, event rates, and counts—including empty bins—are in `tables/calibration_bins.csv`. The observed event count is small, so calibration points are descriptive and uncertain.
+
+## Decision rationale
+
+The origin-level table retains each model's probability, risk direction, concise rationale, supporting evidence, countervailing evidence, per-origin Brier score, and direction-agreement indicator. This is the source for examining how the models reached different or similar assessments; the reported rationale and evidence are model-generated explanations, not independent causal attribution.
+
+## Limitations
+
+- Monthly forecasts share overlapping three-month target windows, so origin-level results are dependent. No independent-observation significance test is reported.
+- The event is uncommon in this sample (6/84), limiting the stability of calibration and aggregate comparisons.
+- No binary action threshold is defined. The model-stated `up`, `down`, and `neutral` directions are reported verbatim and are not converted into classifications.
+- Historical input revisions and release timing are subject to the use case's documented data-cutoff assumptions.
+
+## Included files
+
+- `tables/model_summary.csv` — aggregate scores, scored/skipped counts, event prevalence, and mean forecast probability.
+- `tables/paired_origin_predictions.csv` — all paired origin-level numeric values and model rationales/evidence.
+- `tables/calibration_bins.csv` — every calibration bin's source values and origin count.
+- `run_manifest.json` — cache identifier, specification, artifact timestamps, and chart-to-data mapping.
+- `build_report.py` — rebuilds figures and tables from saved cache artifacts without making LLM calls.
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new file mode 100644
index 000000000..1c6f0cd35
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/run_manifest.json
@@ -0,0 +1,58 @@
+{
+  "built_at_utc": "2026-10-05T20:24:25.753257+00:00",
+  "cache_directory": "data/predictions/manufacturing_stress_adaptive_model_comparison/manufacturing_stress_smoke_llmp_v3_14b1deebe4",
+  "cache_id": "manufacturing_stress_smoke_llmp_v3_14b1deebe4",
+  "charts": {
+    "figures/calibration.png": "tables/calibration_bins.csv",
+    "figures/forecast_probabilities.png": "tables/paired_origin_predictions.csv",
+    "figures/mean_brier_scores.png": "tables/model_summary.csv",
+    "figures/probability_difference.png": "tables/paired_origin_predictions.csv"
+  },
+  "llm_calls_during_report_build": 0,
+  "models": {
+    "adaptive_advanced": {
+      "color": "#d9822b",
+      "label": "Advanced",
+      "mean_brier": 0.07045702380952382,
+      "model": "gemini-3.5-flash",
+      "predictor_id": "agent_predictor_manufacturing_stress_adaptive_analyst_gemini_3_5_flash_read_only_gemini-3.5-flash_discrete",
+      "ran_at": "2026-10-05T19:04:29.192147",
+      "scored_origins": 84,
+      "skipped_origins": 0
+    },
+    "adaptive_lite": {
+      "color": "#2f6f8f",
+      "label": "Lite",
+      "mean_brier": 0.09014672498745041,
+      "model": "gemini-3.1-flash-lite-preview",
+      "predictor_id": "agent_predictor_manufacturing_stress_adaptive_analyst_gemini_3_1_flash_lite_preview_read_only_gemini-3.1-flash-lite-preview_discrete",
+      "ran_at": "2026-10-05T17:11:43.673739",
+      "scored_origins": 84,
+      "skipped_origins": 0
+    }
+  },
+  "observed_stress_events": 6,
+  "paired_origins": 84,
+  "report_directory": "implementations/manufacturing_stress_forecasting/reports/adaptive_compare",
+  "risk_direction_agreements": 50,
+  "spec": {
+    "description": "Development backtest for forecasting whether U.S. manufacturing will be under IPMAN-defined stress three months after each forecast origin.",
+    "end": "2024-12-01T00:00:00",
+    "origin_dates": null,
+    "start": "2018-01-01T00:00:00",
+    "stride": 1,
+    "task": {
+      "categories": null,
+      "description": "Probability that U.S. manufacturing will be under stress three months ahead. A resolved month is stressed when IPMAN has declined by at least 2 percent over its preceding three months.",
+      "frequency": "MS",
+      "horizons": [
+        3
+      ],
+      "payload_type": "binary",
+      "resolution_fn": "observed_value_at_resolution_timestamp",
+      "target_series_id": "manufacturing_stress",
+      "task_id": "manufacturing_stress_3m"
+    },
+    "warmup": 60
+  }
+}
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/calibration_bins.csv b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/calibration_bins.csv
new file mode 100644
index 000000000..0724ec282
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/calibration_bins.csv
@@ -0,0 +1,11 @@
+model,model_label,probability_bin,bin_lower,bin_upper,origins,mean_predicted_probability,observed_event_rate
+adaptive_lite,Lite,0.0-0.2,0,0.2,81,0.0677506054693,0.0740740740741
+adaptive_lite,Lite,0.2-0.4,0.2,0.4,0,,
+adaptive_lite,Lite,0.4-0.6,0.4,0.6,0,,
+adaptive_lite,Lite,0.6-0.8,0.6,0.8,1,0.65,0
+adaptive_lite,Lite,0.8-1.0,0.8,1,2,0.85,0
+adaptive_advanced,Advanced,0.0-0.2,0,0.2,83,0.0672048192771,0.0722891566265
+adaptive_advanced,Advanced,0.2-0.4,0.2,0.4,0,,
+adaptive_advanced,Advanced,0.4-0.6,0.4,0.6,1,0.45,0
+adaptive_advanced,Advanced,0.6-0.8,0.6,0.8,0,,
+adaptive_advanced,Advanced,0.8-1.0,0.8,1,0,,
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/model_summary.csv b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/model_summary.csv
new file mode 100644
index 000000000..d5e8bd366
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/model_summary.csv
@@ -0,0 +1,3 @@
+model,model_label,model_id,metric,mean_brier,scored_origins,skipped_origins,observed_stress_events,observed_event_rate,mean_probability
+adaptive_lite,Lite,gemini-3.1-flash-lite-preview,brier,0.0901467249875,84,0,6,0.0714285714286,0.0933071314644
+adaptive_advanced,Advanced,gemini-3.5-flash,brier,0.0704570238095,84,0,6,0.0714285714286,0.0717619047619
diff --git a/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/paired_origin_predictions.csv b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/paired_origin_predictions.csv
new file mode 100644
index 000000000..a4b7e904b
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/adaptive_compare/tables/paired_origin_predictions.csv
@@ -0,0 +1,85 @@
+as_of,forecast_date,observed_outcome,adaptive_advanced_probability,adaptive_lite_probability,adaptive_advanced_brier_score,adaptive_lite_brier_score,adaptive_advanced_risk_direction,adaptive_lite_risk_direction,adaptive_advanced_rationale,adaptive_lite_rationale,adaptive_advanced_supporting_evidence,adaptive_lite_supporting_evidence,adaptive_advanced_countervailing_evidence,adaptive_lite_countervailing_evidence,probability_delta_advanced_minus_lite,risk_direction_agrees
+2018-01-01,2018-04-01,0,0.05,0.058,0.0025000000000000005,0.0033640000000000002,down,down,"Strong three-month IPMAN growth of 0.88% and robust macroeconomic indicators, including low credit spreads and strong equity returns, keep the risk of manufacturing stress very low, justifying a slight downward adjustment from the anchor.","The 3-month IPMAN change is positive (+0.88%), well above the -2% stress threshold. Macro indicators like the yield spread and credit spread remain healthy, suggesting no imminent manufacturing stress.",Positive three-month IPMAN growth of 0.88% and strong equity returns (SPY 3M return of 6.76%),None are material,A slight one-month decline in IPMAN of -0.26%,"3-month IPMAN change is positive at 0.88%, significantly above the -2% stress threshold.",-0.008,True
+2018-02-01,2018-05-01,0,0.052,0.055,0.002704,0.003025,down,down,"Strong macro indicators, including high XLI and SPY returns and low credit spreads, suggest low risk of manufacturing stress, despite a mild recent 3-month IPMAN decline of -0.50% remaining well above the -2.0% threshold.","The 3-month IPMAN contraction of -0.5% is well above the -2% stress threshold. Strong equity returns and a positive yield spread suggest a stable macroeconomic environment, reducing the likelihood of imminent manufacturing stress.",Strong XLI 3-month return of 12.07% and SPY 3-month return of 10.19% indicate robust economic momentum.,3-month IPMAN contraction of -0.5% is significantly above the -2% stress threshold.,Recent 3-month IPMAN decline of -0.50% and 1-month decline of -0.32% show a minor short-term slowdown.,None are material; macroeconomic indicators like SPY and XLI returns remain robust.,-0.0030000000000000027,True
+2018-03-01,2018-06-01,0,0.05,0.055,0.0025000000000000005,0.003025,down,down,"Strong recent IPMAN growth (+0.87% monthly) and positive macro indicators, including low unemployment (4.1%) and solid equity returns, keep the risk of manufacturing stress very low, justifying a slight downward adjustment from the anchor.","IPMAN shows positive 3-month growth (0.29%), remaining well above the -2% stress threshold. Macro indicators, including stable credit spreads and positive equity returns, suggest a low risk of manufacturing contraction in the near term.",Positive 1-month IPMAN growth of 0.87% and 3-month growth of 0.29%,None are material,Slightly elevated VIX at 19.85 and a narrowing yield spread of 0.62%,Positive 3-month IPMAN growth of 0.29% and stable macroeconomic environment.,-0.0049999999999999975,True
+2018-04-01,2018-07-01,0,0.055,0.061,0.003025,0.003721,down,neutral,"With three-month IPMAN growth positive at 0.50%, well above the -2.0% stress threshold, and a 0% trailing five-year stress rate, the risk of manufacturing stress remains low, justifying a slight downward adjustment from the anchor.","The 3-month IPMAN change is positive (+0.497%), indicating no immediate stress. While 1-month change is slightly negative, it remains far from the -2% threshold. Macro indicators show no systemic distress, supporting the statistical anchor.",Slight negative 1-month IPMAN change of -0.05% and negative 3-month XLI return of -1.43%,3-month IPMAN change is positive at 0.497%,Positive 3-month IPMAN growth of 0.50% and a 0% trailing 60-month stress rate,1-month IPMAN change is slightly negative at -0.054%,-0.005999999999999998,False
+2018-05-01,2018-08-01,0,0.05,0.065,0.0025000000000000005,0.0042250000000000005,down,up,"Strong recent IPMAN growth of 1.57% over three months and low unemployment (4.0%) reduce stress risk, though negative three-month equity returns (SPY -5.79%) present a minor countervailing signal.","IPMAN shows positive 3-month growth (+1.57%), remaining well above the -2% stress threshold. While equity returns (SPY, XLI) are negative, the overall manufacturing trend remains stable, justifying a slight upward adjustment from the anchor.",Strong 3-month IPMAN growth of 1.57% and low unemployment of 4.0%,Negative 3-month returns for SPY (-5.79%) and XLI (-9.05%) suggest potential sector headwinds.,"Negative 3-month equity returns (SPY -5.79%, XLI -9.05%)","IPMAN 3-month growth is positive at 1.57%, significantly above the -2% stress threshold.",-0.015,False
+2018-06-01,2018-09-01,0,0.06,0.06,0.0036,0.0036,neutral,down,"The 3-month IPMAN contraction of -0.12% remains far from the -2.0% stress threshold, though a -0.81% 1-month drop warrants caution. Strong macro indicators support a low stress probability of 0.060.","The current 3-month IPMAN contraction of -0.12% is well above the -2% stress threshold. Macroeconomic indicators remain stable, and recent IPMAN trends do not suggest an imminent breach of the stress criteria.",Recent 1-month IPMAN decline of -0.81% and negative 3-month XLI return of -2.49%,Current 3-month IPMAN contraction of -0.12% is significantly above the -2% stress threshold.,"Current 3-month IPMAN change of -0.12% is well above the -2.0% stress threshold, and UNRATE is low at 3.8%",None are material; macro indicators and IPMAN trends remain stable.,0.0,False
+2018-07-01,2018-10-01,0,0.053,0.055,0.002809,0.003025,down,down,"Positive recent IPMAN growth (+0.71% monthly, +0.64% quarterly) and strong macro indicators, including low unemployment (4.0%) and credit spreads (1.89%), keep the probability of manufacturing stress low, justifying a slight downward adjustment from the anchor.","IPMAN shows positive 3-month growth (0.64%), remaining well above the -2% stress threshold. Macro indicators, including stable unemployment and positive equity returns, suggest a low risk of manufacturing stress in the near term.","XLI 3-month return is negative at -2.49%, indicating some short-term industrial sector cooling.",None are material,"IPMAN grew 0.71% in the latest month and 0.64% over the last three months, well above the -2% stress threshold.",Positive 3-month IPMAN growth of 0.64% and stable macroeconomic environment.,-0.0020000000000000018,True
+2018-08-01,2018-11-01,0,0.05,0.055,0.0025000000000000005,0.003025,down,down,"With trailing three-month IPMAN contraction at a negligible -0.04%, well above the -2.0% stress threshold, and strong macroeconomic indicators including low unemployment and positive industrial equity returns, the risk of near-term manufacturing stress remains very low.","The 3-month IPMAN change is -4.05%, which is below the -2% threshold. However, the 1-month change is positive (+0.06%) and macro indicators like low VIX and positive equity returns suggest a stable environment, warranting a slight downward adjustment.","Trailing 3-month IPMAN change of -0.04% is far above the -2.0% stress threshold, supported by low unemployment of 3.8% and strong XLI 3-month return of 6.94%.",3-month IPMAN change of -4.05% is below the -2% stress threshold.,Slight trailing 3-month IPMAN contraction of -0.04% and a relatively flat yield curve spread of 0.29%.,"Positive 1-month IPMAN growth and stable macro indicators (low VIX, positive equity returns) suggest resilience.",-0.0049999999999999975,True
+2018-09-01,2018-12-01,0,0.05,0.05,0.0025000000000000005,0.0025000000000000005,down,down,"Strong positive three-month IPMAN growth of 1.00% and robust macroeconomic indicators, including low unemployment and solid equity returns, suggest very low risk of manufacturing stress, justifying a slight downward adjustment from the anchor.","The 3-month IPMAN change is positive (+1.00%), far from the -2% stress threshold. Strong equity returns and a stable yield spread suggest a low probability of manufacturing stress in the near term.","Three-month IPMAN growth is positive at 1.00%, well above the -2.0% stress threshold.",3-month IPMAN change is positive at 1.00%,"The yield curve spread is narrow at 0.29%, indicating potential future economic moderation.",None are material,0.0,True
+2018-10-01,2019-01-01,0,0.05,0.05,0.0025000000000000005,0.0025000000000000005,down,down,"Positive three-month IPMAN growth of 0.33% and strong macroeconomic indicators, including low unemployment (3.7%) and solid equity returns, suggest very low risk of manufacturing stress, justifying a slight downward adjustment from the anchor.","The 3-month IPMAN change is positive (+0.33%), indicating robust growth well above the -2% stress threshold. Strong equity returns and a positive yield spread further support a low probability of manufacturing stress.",Positive 3-month IPMAN growth of 0.33% and 6-month growth of 0.97%; Strong equity performance with SPY 3-month return at 7.65% and XLI 3-month return at 9.98%,IPMAN 3-month change is positive at 0.33%,A narrow yield curve spread of 0.24% indicates potential future macroeconomic deceleration,None are material,0.0,True
+2018-11-01,2019-02-01,0,0.065,0.06,0.0042250000000000005,0.0036,neutral,down,"The 3-month IPMAN change of -0.11% is well above the -2% stress threshold. However, negative 1-month IPMAN growth (-0.37%) and declining industrial equity returns (-8.71%) suggest slightly elevated risk, keeping probability near the anchor.","The current 3-month IPMAN contraction of -0.11% is far from the -2% stress threshold. Recent monthly data shows relative stability, and macroeconomic indicators do not signal an imminent systemic manufacturing downturn.",Negative 1-month IPMAN growth of -0.37% and declining XLI 3-month return of -8.71%,Trailing 3-month IPMAN contraction of -0.11% is well above the -2% stress threshold.,Current 3-month IPMAN change of -0.11% is far above the -2.0% stress threshold,None are material.,0.0050000000000000044,False
+2018-12-01,2019-03-01,0,0.075,0.064,0.005625,0.004096,up,neutral,"IPMAN declined for three consecutive months to -0.59% over three months, remaining above the -2.0% threshold. However, flat yield curve (0.28%) and negative industrial stock returns (-8.71%) suggest rising risk.","The 3-month IPMAN contraction of -0.59% remains well above the -2% stress threshold. While XLI and SPY returns are negative, macroeconomic indicators like the unemployment rate and yield spread suggest no immediate systemic manufacturing stress.","Three consecutive monthly declines in IPMAN, bringing the 3-month change to -0.59%; Negative 3-month XLI return of -8.71% and flat yield curve of 0.28%",Negative 3-month returns for XLI (-8.71%) and SPY (-3.37%) suggest sector-specific and broader market headwinds.,Current 3-month IPMAN contraction of -0.59% remains well above the -2.0% stress threshold,"The 3-month IPMAN contraction of -0.59% is significantly above the -2% stress threshold, and the unemployment rate remains low at 3.8%.",0.010999999999999996,False
+2019-01-01,2019-04-01,0,0.065,0.085,0.0042250000000000005,0.007225000000000001,down,up,"Stable recent IPMAN growth (+0.36% monthly) and a mild 3-month contraction (-0.27%) well above the -2% threshold justify a slight downward adjustment from the anchor, despite recent equity market volatility.","While the 3-month IPMAN contraction is mild (-0.27%), significant negative equity returns (SPY -13.5%, XLI -17.3%) and elevated volatility (VIX 25.4) suggest heightened downside risks to manufacturing activity over the next three months.",IPMAN 1-month growth of 0.36% and 3-month change of -0.27% are well above the -2.0% stress threshold.,Significant negative 3-month returns in SPY (-13.5%) and XLI (-17.3%); Elevated VIX (25.42) indicating market stress,"Significant equity market declines (SPY -13.53%, XLI -17.34% over 3 months) and elevated VIX (25.42) signal potential macroeconomic headwinds.",Current 3-month IPMAN contraction of -0.27% remains well above the -2% stress threshold,-0.020000000000000004,False
+2019-02-01,2019-05-01,0,0.07,0.06,0.004900000000000001,0.0036,up,down,"A sharp 1-month IPMAN decline of -0.88% and a flat yield curve increase stress risk, though the 3-month change remains above the -2.0% threshold.","The current 3-month IPMAN contraction of -0.77% remains well above the -2% stress threshold. Macroeconomic indicators, including a positive yield spread and stable unemployment, suggest no immediate systemic manufacturing stress.","IPMAN declined by 0.88% in the latest month, and the yield curve is flat at 0.18%.",3-month IPMAN contraction of -0.77% is significantly above the -2% stress threshold.,The 3-month trailing IPMAN change of -0.77% remains well above the -2.0% stress threshold.,None are material.,0.010000000000000009,False
+2019-03-01,2019-06-01,0,0.085,0.075,0.007225000000000001,0.005625,up,up,"IPMAN's downward trend (-1.10% over three months) and a narrow yield spread of 0.21% signal rising risk of manufacturing stress, though low unemployment and positive equity returns provide some offset.","The 3-month IPMAN contraction of -1.1% is approaching the -2% threshold. While macro indicators remain stable, the downward trend in manufacturing output warrants a slight upward adjustment from the base rate.",Trailing 3-month IPMAN contraction of -1.10% and a narrow yield spread of 0.21%,3-month IPMAN contraction of -1.1% is trending toward the -2% stress threshold.,Low unemployment rate of 3.8% and positive SPY 3-month return of 1.69%,"Stable macroeconomic environment, including positive yield spread and moderate credit spreads.",0.010000000000000009,True
+2019-04-01,2019-07-01,0,0.075,0.075,0.005625,0.005625,up,up,"With trailing three-month IPMAN contraction at -1.69%, close to the -2.0% stress threshold, risk is elevated. However, strong equity returns and low credit spreads provide a countervailing buffer.","The 3-month IPMAN contraction of -1.69% is approaching the -2% stress threshold. While macro indicators remain generally supportive, the downward momentum in manufacturing output increases the risk of hitting the threshold within three months.",Trailing three-month IPMAN contraction of -1.69% is near the -2.0% stress threshold.,3-month IPMAN contraction of -1.69% is nearing the -2% stress threshold.,Strong SPY and XLI three-month returns of 13.5% and 17.2% respectively.,Strong 3-month returns in SPY and XLI suggest broader economic and industrial resilience.,0.0,True
+2019-05-01,2019-08-01,0,0.085,0.065,0.007225000000000001,0.0042250000000000005,up,up,"The 3-month IPMAN decline of -1.46% is near the -2% threshold, and the 6-month decline is -2.22%. Combined with a flat yield curve (0.24), this warrants an upward adjustment from the anchor.","The 3-month IPMAN contraction of -1.46% is approaching the -2% threshold. While macro indicators remain stable, the downward trend in IPMAN warrants a slight upward adjustment from the anchor probability.",3-month IPMAN change of -1.46% and 6-month change of -2.22% indicate a clear downward trend in manufacturing.,IPMAN 3-month contraction of -1.46% is trending toward the -2% stress threshold.,Strong equity returns (SPY 3M return of 9.4%) and low unemployment (3.7%) suggest broader economic resilience.,"Macroeconomic indicators, including the yield spread and credit spread, remain stable.",0.020000000000000004,True
+2019-06-01,2019-09-01,0,0.055,0.065,0.003025,0.0042250000000000005,neutral,neutral,"IPMAN's three-month decline of -0.89% remains well above the -2.0% stress threshold, with recent one-month stabilization (-0.004%) and strong macroeconomic indicators, including positive yield spreads and robust equity returns, suggesting low near-term stress risk.","The 3-month IPMAN contraction of -0.89% remains well above the -2% stress threshold. While the 6-month trend is negative, recent equity market returns and stable credit spreads suggest limited near-term systemic manufacturing stress.",Recent 1-month IPMAN stabilization (-0.004%) and strong equity returns (SPY 3M at 9.40%),3-month IPMAN contraction of -0.89% is significantly above the -2% stress threshold.,Negative 3-month (-0.89%) and 6-month (-1.98%) IPMAN trends indicate ongoing contraction,Positive 3-month SPY and XLI returns suggest resilient industrial and broader market sentiment.,-0.010000000000000002,True
+2019-07-01,2019-10-01,0,0.055,0.055,0.003025,0.003025,down,down,"Recent IPMAN rebounded by 0.38% in June, keeping the three-month contraction at a mild -0.27%, well above the -2.0% stress threshold. Low unemployment and positive equity returns further mitigate near-term manufacturing stress risks.","The 3-month IPMAN contraction of -0.27% is well above the -2% stress threshold. Recent monthly data shows stabilization, and favorable macro indicators like the positive yield spread and moderate credit spreads suggest low near-term stress risk.",Recent 1-month IPMAN growth of 0.38% and a mild 3-month contraction of -0.27% remain far from the -2.0% stress threshold.,Current 3-month IPMAN contraction of -0.27% is significantly above the -2% stress threshold.,The 6-month IPMAN decline of -1.96% indicates underlying medium-term softness in manufacturing.,None are material; macro indicators remain stable.,0.0,True
+2019-08-01,2019-11-01,0,0.07,0.065,0.004900000000000001,0.0042250000000000005,up,neutral,"While the 3-month IPMAN contraction (-0.34%) remains above the -2.0% threshold, a sharp 1-month decline (-0.71%), a -1.80% 6-month drop, and a narrow yield spread (0.13) suggest rising manufacturing stress risks.","The 3-month IPMAN contraction of -0.34% remains well above the -2% stress threshold. While recent monthly data shows slight volatility, macroeconomic indicators like low unemployment and stable credit spreads suggest no immediate systemic manufacturing stress.",Recent 1-month IPMAN decline of -0.71% and 6-month decline of -1.80% indicate a downward trend.,3-month IPMAN contraction of -0.34% is significantly above the -2% stress threshold.,Current 3-month IPMAN change of -0.34% is well above the -2.0% stress threshold.,None are material.,0.0050000000000000044,False
+2019-09-01,2019-12-01,0,0.05,0.056,0.0025000000000000005,0.0031360000000000003,down,neutral,"Recent positive 1-month and 3-month IPMAN growth rates (+0.66% and +0.32%) provide a buffer against the -2% stress threshold, though a flat yield curve warrants caution.","The 3-month IPMAN change is positive (+0.32%), well above the -2% stress threshold. Macro indicators remain stable, and the current trajectory does not suggest an imminent breach of the stress criteria within the three-month horizon.",Positive 1-month IPMAN growth of 0.66% and 3-month growth of 0.32%.,None are material,"The yield curve spread is narrow at 0.13%, signaling potential macroeconomic headwinds.",Positive 3-month IPMAN change of 0.32% is well above the -2% stress threshold.,-0.005999999999999998,False
+2019-10-01,2020-01-01,0,0.07,0.065,0.004900000000000001,0.0042250000000000005,up,neutral,"IPMAN declined 0.81% in the latest month, and the yield curve is nearly flat (0.05), increasing stress risk. However, the 3-month contraction of -0.87% remains well above the -2.0% threshold, keeping overall probability low.","The 3-month IPMAN contraction of -0.87% remains well above the -2% stress threshold. Macro indicators like low unemployment and positive equity returns suggest stability, keeping the probability near the statistical anchor.",Latest 1-month IPMAN decline of -0.81% and a very flat yield curve spread of 0.05%,Trailing 3-month IPMAN contraction of -0.87% is significantly above the -2% stress threshold.,Current 3-month IPMAN contraction of -0.87% is still well above the -2.0% stress threshold,None are material.,0.0050000000000000044,False
+2019-11-01,2020-02-01,0,0.075,0.075,0.005625,0.005625,up,up,"IPMAN's downward trend (-0.82% monthly, -0.98% over three months) and a flat yield curve (0.17%) increase stress risk, though strong employment and positive equity returns provide a buffer.","The 3-month IPMAN contraction of -0.98% is approaching the -2% threshold. While macro signals remain relatively stable, the downward trend in IPMAN increases the risk of hitting the stress threshold within the three-month horizon.",IPMAN 1-month decline of -0.82% and 3-month decline of -0.98% indicate worsening momentum.,3-month IPMAN contraction of -0.98% is trending toward the -2% threshold.,Low unemployment rate of 3.6% and positive SPY 3-month return of 2.45%.,"Macroeconomic indicators, including the yield spread and credit spread, remain stable.",0.0,True
+2019-12-01,2020-03-01,1,0.05,0.05,0.9025,0.9025,down,down,"A strong 1-month IPMAN rebound (+0.83%) and stable macro indicators, including low unemployment (3.6%) and VIX (13.22), keep the 3-month contraction (-0.81%) well above the -2.0% stress threshold, reducing near-term risk.","The 3-month IPMAN contraction of -0.81% remains well above the -2% stress threshold. Recent monthly growth is positive, and stable macro indicators like the yield spread and low VIX suggest limited near-term risk of manufacturing stress.",Strong 1-month IPMAN rebound of +0.83% in November 2019,3-month IPMAN contraction of -0.81% is significantly above the -2% stress threshold.,Negative 3-month IPMAN change of -0.81% and flat 6-month trend (-0.50%),None are material; macro indicators remain stable.,0.0,True
+2020-01-01,2020-04-01,1,0.05,0.045,0.9025,0.912025,down,down,"With trailing 3-month IPMAN growth at +0.05%, well above the -2% stress threshold, and supportive macro indicators (low VIX, positive yield spread), the near-term risk of manufacturing stress remains low.","The 3-month IPMAN change is positive (+5.43%), far from the -2% stress threshold. Strong equity returns and low volatility suggest a stable environment, justifying a downward adjustment from the statistical anchor.","Trailing 3-month IPMAN change of +0.05% is well above the -2.0% stress threshold; Macroeconomic indicators are stable, with VIX at 13.78 and a positive yield spread of 0.34%",Positive 3-month IPMAN growth of 5.43%.,"The 6-month IPMAN change is negative at -0.82%, indicating some longer-term softness",None are material.,0.0050000000000000044,True
+2020-02-01,2020-05-01,1,0.048,0.052,0.9063039999999999,0.898704,down,neutral,"Positive three-month IPMAN growth of 0.53% and strong macroeconomic indicators, including low unemployment (3.6%) and robust equity returns, suggest low risk of manufacturing stress, justifying a slight downward adjustment from the anchor.","The 3-month IPMAN change is positive (+0.53%), indicating no immediate stress. Macro indicators like low unemployment and strong equity returns suggest a stable environment, keeping the probability near the statistical anchor.",Positive 3-month IPMAN growth of 0.53% and low unemployment rate of 3.6%,3-month IPMAN change is positive at 0.53%,Negative 1-month IPMAN change of -0.35% and negative 6-month IPMAN change of -0.46%,None are material,-0.003999999999999997,False
+2020-03-01,2020-06-01,1,0.05,0.054,0.9025,0.8949159999999999,down,neutral,"With trailing three-month IPMAN contraction at just -0.08%, well above the -2.0% stress threshold, and a positive 0.23% one-month growth, near-term manufacturing stress risk remains low, supporting a slight downward adjustment from the anchor.","The 3-month IPMAN contraction of -0.076% is well above the -2% stress threshold. Recent monthly growth is positive, and macroeconomic indicators like low unemployment and stable credit spreads suggest no immediate systemic manufacturing stress.","Positive 1-month IPMAN growth of 0.23% and a 3-month contraction of only -0.08%, far from the -2.0% stress threshold.",3-month IPMAN contraction of -0.076% is significantly above the -2% stress threshold.,Negative 6-month IPMAN change of -0.89% and a low yield curve spread of 0.18%.,None are material.,-0.003999999999999997,False
+2020-04-01,2020-07-01,0,0.45,0.65,0.2025,0.42250000000000004,up,up,"Severe manufacturing contraction (-4.64% over three months) and extreme macroeconomic stress from the COVID-19 shock (VIX at 53.54, ICSA at 5.9M) significantly elevate the probability of continued manufacturing stress.","The 3-month IPMAN contraction of -4.64% significantly exceeds the -2% stress threshold. High VIX (53.54), negative equity returns, and elevated credit spreads (3.93) indicate severe systemic stress, justifying a substantial upward adjustment from the anchor.",IPMAN 3-month decline of -4.64% already exceeds the -2% stress threshold; Extreme macro stress signals including VIX at 53.54 and weekly jobless claims (ICSA) at 5.95 million; XLI 3-month return of -27.01% and SPY 3-month return of -19.45%,3-month IPMAN contraction of -4.64% exceeds the -2% stress threshold; High VIX (53.54) and elevated credit spreads (3.93) signal significant market stress,FEDFUNDS lowered to 0.65% and a positive yield spread of 0.47% provide some policy support,None are material,-0.2,True
+2020-05-01,2020-08-01,0,0.15,0.85,0.0225,0.7224999999999999,down,up,"With April 2020 IPMAN at an extreme lockdown trough of 79.86, a further 2% decline by July is highly unlikely as industries reopen, despite severe current macro stress signals.","The 3-month IPMAN contraction of -19.01% far exceeds the -2% stress threshold. Severe macroeconomic indicators, including high unemployment (14.8%) and negative industrial sector returns, confirm a high probability of continued manufacturing stress.","Extreme April IPMAN drop to 79.86 creates a very low base, making further 3-month declines highly unlikely.",3-month IPMAN contraction of -19.01% is significantly below the -2% stress threshold.,"Severe macroeconomic stress, including 14.8% unemployment, high VIX (34.15), and elevated credit spreads (3.23%).",None are material.,-0.7,False
+2020-06-01,2020-09-01,0,0.12,0.85,0.0144,0.7224999999999999,neutral,up,"While IPMAN rebounded 4.46% in May, extreme macro stress (13.2% unemployment, 3.08% credit spread) keeps the three-month-ahead manufacturing stress risk elevated, justifying a slight upward adjustment from the anchor.","The 3-month IPMAN change is deeply negative (-15.6%), indicating severe ongoing contraction. Despite a recent 1-month rebound, the momentum remains heavily skewed toward sustained manufacturing stress over the next three months.",Severe macroeconomic distress indicated by a 13.2% unemployment rate and high credit spreads of 3.08%,"IPMAN 3-month change is -15.59%, far below the -2% stress threshold.","Strong 1-month IPMAN rebound of 4.46% in May, suggesting a potential bottoming out of the contraction","Recent 1-month IPMAN change shows a 4.46% increase, suggesting potential short-term stabilization.",-0.73,False
+2020-07-01,2020-10-01,0,0.07,0.15,0.004900000000000001,0.0225,down,up,"Strong recent IPMAN rebound (+7.86% monthly) and surging equity returns suggest a rapid recovery, though high unemployment and elevated credit spreads warrant caution. We adjust the anchor slightly downward.","The 3-month IPMAN change is -4.65%, exceeding the -2% stress threshold. Despite recent 1-month growth, the significant 3-month contraction indicates ongoing manufacturing weakness, justifying an upward adjustment from the anchor.",High unemployment rate of 11.0% and elevated initial claims of 1.44 million indicate ongoing economic disruption.,3-month IPMAN change of -4.65% is below the -2% stress threshold.,Strong 1-month IPMAN growth of 7.86% and robust 3-month SPY return of 20.16% indicate a sharp manufacturing and economic recovery.,Recent 1-month IPMAN growth of 7.86% suggests a potential recovery trend.,-0.07999999999999999,False
+2020-08-01,2020-11-01,0,0.04,0.055,0.0016,0.003025,down,down,"Strong recent IPMAN recovery (+16.8% over three months) and robust equity returns suggest low near-term stress risk, though elevated unemployment and VIX warrant caution. We adjust the anchor slightly downward.","The 3-month IPMAN change is strongly positive (+16.8%), far from the -2% stress threshold. While 6-month trends are negative, recent momentum and strong equity returns suggest low near-term stress risk.","Elevated unemployment (10.2%), high initial claims (1.26M), and high VIX (30.43) indicate ongoing macroeconomic fragility.","IPMAN 3-month change is +16.81%, well above the -2% stress threshold.",Strong 3-month IPMAN growth of 16.81% and robust 3-month SPY returns of 20.16% signal a rapid industrial recovery.,IPMAN 6-month change remains negative at -5.40%.,-0.015,True
+2020-09-01,2020-12-01,0,0.05,0.055,0.0025000000000000005,0.003025,down,down,"Strong 3-month IPMAN growth of 13.43% and positive 1-month momentum indicate low risk of near-term manufacturing stress, justifying a downward adjustment from the anchor.","The 3-month IPMAN growth is strong at 13.43%, far above the -2% stress threshold. Recent monthly growth is positive, and macro indicators like SPY and XLI returns suggest a robust industrial outlook, reducing stress risk.",Strong 3-month IPMAN growth of 13.43% and positive 1-month growth of 1.43%,IPMAN 3-month change of 13.43% is well above the -2% stress threshold.,"Elevated unemployment rate of 8.4% and high initial jobless claims of 881,000",None are material.,-0.0049999999999999975,True
+2020-10-01,2021-01-01,0,0.06,0.068,0.0036,0.0046240000000000005,down,neutral,"Strong three-month IPMAN growth of 5.14% and robust equity returns provide a significant buffer against the -2% stress threshold, justifying a downward adjustment from the anchor to 0.060 despite elevated unemployment and VIX.","The 3-month IPMAN change is positive (+5.14%), providing a significant buffer against the -2% stress threshold. Despite a slight 1-month decline, the overall trend remains robust, keeping the probability near the historical base rate.",Strong 3-month IPMAN growth of 5.14% and positive XLI 3-month return of 12.49%,None are material,Elevated unemployment rate of 7.8% and high VIX of 26.37,3-month IPMAN change of +5.14% is well above the -2% stress threshold.,-0.008000000000000007,False
+2020-11-01,2021-02-01,1,0.05,0.06009904301259727,0.9025,0.8834138089458354,down,neutral,"Strong recent IPMAN growth (+2.45% over three months) and robust equity returns indicate continued recovery, keeping the risk of a 2% contraction low. High unemployment and elevated VIX remain minor countervailing risks.","The 3-month IPMAN change is positive (+2.45%), well above the -2% stress threshold. Recent growth and stable macroeconomic indicators, including a positive yield spread and moderate credit spreads, suggest low risk of imminent manufacturing stress.",Three-month IPMAN growth of 2.45% and six-month growth of 19.67% show strong recovery well above the -2% stress threshold.,None are material,Elevated unemployment rate of 6.9% and VIX at 26.37 indicate ongoing economic uncertainty.,Positive 3-month IPMAN growth of 2.45% is well above the -2% stress threshold.,-0.010099043012597266,False
+2020-12-01,2021-03-01,0,0.055,0.06,0.003025,0.0036,down,down,"Strong recent IPMAN recovery (+1.55% over three months) and robust industrial stock returns (+13.57%) indicate low risk of near-term manufacturing stress, justifying a downward adjustment from the anchor.","The 3-month IPMAN change is positive (+1.55%), well above the -2% stress threshold. Recent growth and stable macroeconomic indicators suggest low risk of manufacturing stress in the near term.","Three-month IPMAN growth of 1.55% and six-month growth of 15.19% show strong recovery, well above the -2% stress threshold.; XLI 3-month return of 13.57% indicates strong industrial sector momentum.",None are material,"Elevated unemployment rate of 6.7% and high jobless claims (ICSA at 737,000) reflect lingering labor market weakness.",Positive 3-month IPMAN growth of 1.55% provides a significant buffer against the -2% stress threshold.,-0.0049999999999999975,True
+2021-01-01,2021-04-01,0,0.058,0.06,0.0033640000000000002,0.0036,down,down,"Strong recent IPMAN growth (+2.38% over three months) and robust industrial equity returns (XLI +15.5%) provide a substantial buffer against the -2% stress threshold, justifying a downward adjustment from the anchor.",The 3-month IPMAN growth of 2.38% is well above the -2% stress threshold. Recent positive momentum and stable macroeconomic indicators suggest a low risk of manufacturing stress in the upcoming three-month horizon.,Three-month IPMAN growth of 2.38% is well above the -2% stress threshold; Strong industrial sector equity performance with XLI 3-month return of 15.51%,IPMAN 3-month growth of 2.38% is significantly above the -2% stress threshold.,"Elevated unemployment rate of 6.7% and high initial jobless claims of 773,000 indicate lingering labor market weakness",None are material.,-0.001999999999999995,True
+2021-02-01,2021-05-01,0,0.06,0.06,0.0036,0.0036,down,down,"Strong positive IPMAN momentum (+2.33% over three months) and robust equity returns provide a substantial buffer against the -2% stress threshold, justifying a downward adjustment from the anchor to 0.060.","The 3-month IPMAN growth is positive (+2.33%), providing a significant buffer against the -2% stress threshold. Recent momentum is positive, and macroeconomic indicators do not signal imminent manufacturing contraction.","Elevated VIX of 33.09 and high initial jobless claims of 803,000 indicate lingering macroeconomic uncertainty.",IPMAN 3-month change is positive at 2.33%.,Strong positive 3-month IPMAN growth of 2.33% and robust XLI 3-month return of 12.19%.,None are material.,0.0,True
+2021-03-01,2021-06-01,0,0.12,0.12,0.0144,0.0144,up,up,"Current IPMAN is in stress (-2.45% over three months) due to a sharp February drop, and elevated jobless claims suggest near-term risk, though strong equity returns and a steep yield curve indicate medium-term recovery.","The 3-month IPMAN contraction of -2.45% has breached the -2% stress threshold. While macro signals remain relatively stable, the immediate momentum indicates elevated risk of sustained manufacturing stress in the near term.","Current 3-month IPMAN decline of -2.45% meets the stress threshold, accompanied by high jobless claims of 704k and elevated VIX of 27.95.",Current 3-month trailing IPMAN change of -2.45% is below the -2% stress threshold.,"Strong 12-month SPY (30.78%) and XLI (26.51%) returns, a positive yield spread of 1.30%, and low federal funds rate of 0.08% suggest resilience.",Positive 3-month and 12-month SPY and XLI returns suggest broader market resilience.,0.0,True
+2021-04-01,2021-07-01,0,0.06,0.06,0.0036,0.0036,down,down,"Strong 1-month IPMAN recovery (+3.13%) and positive macro indicators, including robust equity returns, keep the 3-month contraction risk well above the -2% stress threshold, justifying a downward adjustment from the anchor.","The 3-month IPMAN contraction of -0.18% is well above the -2% stress threshold. Recent 1-month growth of 3.13% indicates strong recovery, reducing the likelihood of hitting the stress threshold within the three-month horizon.",Strong 1-month IPMAN growth of 3.13% in March 2021 indicates robust recovery from the February dip.,Current 3-month IPMAN contraction of -0.18% is significantly above the -2% stress threshold.,Trailing 3-month IPMAN change remains slightly negative at -0.18%.,None are material.,0.0,True
+2021-05-01,2021-08-01,0,0.06,0.065,0.0036,0.0042250000000000005,down,down,"With trailing three-month IPMAN contraction at -0.93%, well above the -2.0% stress threshold, and recent monthly growth of 0.22% indicating stabilization, the probability of manufacturing stress is adjusted slightly downward from the anchor.","The 3-month IPMAN contraction of -0.93% remains well above the -2% stress threshold. Recent monthly growth is positive, and strong equity returns suggest a resilient manufacturing environment, justifying a slight downward adjustment from the anchor.",Recent 1-month IPMAN growth of 0.22% and 6-month growth of 1.38% indicate stabilization and recovery from the February dip.,Current 3-month IPMAN contraction of -0.93% is significantly above the -2% stress threshold.,"The trailing 3-month IPMAN change remains negative at -0.93%, reflecting the lingering impact of the February production drop.",None are material.,-0.0050000000000000044,True
+2021-06-01,2021-09-01,0,0.055,0.06,0.003025,0.0036,down,down,"Strong recent IPMAN growth (+4.50% over three months) and supportive macro indicators, including low credit spreads and robust equity returns, suggest very low risk of manufacturing stress, justifying a downward adjustment from the anchor.","The 3-month IPMAN growth is strong at 4.5%, well above the -2% stress threshold. Macro indicators like the yield spread and equity returns remain supportive, suggesting low risk of manufacturing stress in the near term.","Strong 3-month IPMAN growth of +4.50%, well above the -2.0% stress threshold.",3-month IPMAN growth of 4.5% is significantly above the stress threshold.,Elevated inflation (CPI YOY at 4.92%) and moderate unemployment (5.8%) present minor headwinds.,None are material.,-0.0049999999999999975,True
+2021-07-01,2021-10-01,0,0.06,0.065,0.0036,0.0042250000000000005,down,neutral,"Strong positive three-month IPMAN growth of 1.34% and robust equity returns suggest low risk of near-term manufacturing stress, justifying a downward adjustment from the anchor probability.","The 3-month IPMAN growth is positive (1.34%), indicating no immediate stress. Macro indicators like the yield spread (1.2) and strong equity returns suggest a stable environment, keeping the probability near the statistical anchor.",Positive 3-month IPMAN growth of 1.34% and strong 12-month XLI return of 51.19%.,"IPMAN 3-month change is positive at 1.34%, well above the -2% stress threshold.","Elevated CPI inflation at 5.30% YoY and initial jobless claims at 368,000.",None are material.,-0.0050000000000000044,False
+2021-08-01,2021-11-01,0,0.06,0.06,0.0036,0.0036,down,down,"Trailing 3-month IPMAN growth is strong at +2.02%, well above the -2% stress threshold. Supportive macro signals, including low credit spreads and positive equity returns, suggest a low probability of near-term manufacturing stress.","The 3-month IPMAN change is positive (+2.02%), significantly above the -2% stress threshold. Strong equity returns and a healthy yield spread suggest a stable manufacturing environment, justifying a downward adjustment from the anchor.",Strong trailing 3-month IPMAN growth of +2.02% provides a significant buffer above the -2% stress threshold.,3-month IPMAN change of +2.02% is well above the -2% stress threshold.,"Elevated CPI inflation at 5.25% could prompt monetary tightening, posing a potential medium-term risk.",None are material.,0.0,True
+2021-09-01,2021-12-01,0,0.065,0.065,0.0042250000000000005,0.0042250000000000005,down,neutral,"With positive 3-month (+0.32%) and strong 6-month (+4.83%) IPMAN growth, and supportive macro signals like low credit spreads (1.94%) and low VIX (16.48), the risk of manufacturing stress remains low, justifying a slight downward adjustment.","The 3-month IPMAN change remains positive (+0.32%), providing a significant buffer against the -2% stress threshold. Macro indicators, including a healthy yield spread and strong 12-month equity returns, suggest continued stability.",Positive 3-month (+0.32%) and strong 6-month (+4.83%) IPMAN growth provide a significant buffer against the -2% stress threshold.,3-month IPMAN change is positive at 0.32%,A negative 1-month IPMAN change of -0.59% and slight contraction in XLI 3-month return (-0.27%) indicate minor near-term softening.,None are material,0.0,False
+2021-10-01,2022-01-01,0,0.085,0.085,0.007225000000000001,0.007225000000000001,up,up,"IPMAN declined 1.22% in the latest month, bringing the 3-month change to -0.92%, closer to the -2% stress threshold. Negative XLI returns (-4.14%) support rising risk, though low interest rates and credit spreads provide some cushion.","The 3-month IPMAN contraction of -0.92% is approaching the -2% threshold. While macro signals remain stable, the recent downward momentum in IPMAN warrants a slight upward adjustment from the anchor probability.",Recent 1-month IPMAN decline of -1.22% and negative 3-month XLI return of -4.14%,IPMAN 3-month trailing change of -0.92% is trending toward the stress threshold.,Low FEDFUNDS rate of 0.08% and supportive yield curve spread of 1.24%,Yield spread of 1.24 and stable credit spreads suggest no immediate systemic manufacturing stress.,0.0,True
+2021-11-01,2022-02-01,0,0.06,0.065,0.0036,0.0042250000000000005,down,down,"Strong one-month IPMAN growth of 1.41% and robust macroeconomic indicators, including low credit spreads and high equity returns, suggest low risk of manufacturing stress, justifying a downward adjustment from the anchor.","The 3-month IPMAN contraction of -0.42% is well above the -2% stress threshold. Recent 1-month growth of 1.41% suggests stabilization, keeping the probability of reaching the stress threshold low.",Strong 1-month IPMAN growth of 1.41% and a 3-month change of -0.42% well above the -2.0% stress threshold.,3-month IPMAN contraction of -0.42% is significantly above the -2% stress threshold.,Slightly negative 3-month IPMAN change of -0.42% and elevated CPI inflation of 6.24%.,None are material.,-0.0050000000000000044,True
+2021-12-01,2022-03-01,0,0.07,0.075,0.004900000000000001,0.005625,down,neutral,"Positive IPMAN growth over one and three months keeps the index far from the stress threshold, though elevated VIX and negative short-term industrial stock returns suggest some market caution.","IPMAN shows positive 3-month growth (+0.76%), remaining well above the -2% stress threshold. Macro indicators like the yield spread are positive, suggesting low near-term risk of manufacturing stress.",Positive 1-month (0.59%) and 3-month (0.76%) IPMAN growth rates,None are material,Elevated VIX of 27.19 and negative 3-month XLI return of -3.27%,Positive 3-month IPMAN growth of 0.76% is well above the -2% stress threshold.,-0.0049999999999999906,False
+2022-01-01,2022-04-01,0,0.065,0.07,0.0042250000000000005,0.004900000000000001,down,down,"Strong trailing three-month IPMAN growth of 1.89% provides a substantial buffer against the -2% stress threshold, though elevated VIX and negative XLI returns suggest some market caution.","The 3-month IPMAN growth remains positive at 1.89%, providing a significant buffer against the -2% stress threshold. While XLI returns are negative, broader macro indicators suggest stability, warranting a slight downward adjustment from the anchor.",Trailing 3-month IPMAN growth of 1.89% is well above the -2% stress threshold,3-month IPMAN growth of 1.89% is well above the -2% stress threshold.,Elevated VIX of 27.19 and negative 3-month XLI return of -3.27%,"XLI 3-month return is negative at -3.27%, indicating sector-specific weakness.",-0.0050000000000000044,True
+2022-02-01,2022-05-01,0,0.085,0.075,0.007225000000000001,0.005625,up,neutral,"Recent sharp 1-month IPMAN decline of -0.95% and negative industrial equity returns (XLI -3.23%) elevate manufacturing stress risks, though the 3-month contraction (-0.49%) remains above the -2% threshold. We adjust the anchor slightly upward.","The 3-month IPMAN contraction of -0.49% remains well above the -2% stress threshold. While recent industrial and equity returns are negative, the overall macroeconomic environment does not currently signal an imminent manufacturing contraction.",IPMAN 1-month decline of -0.95%; Negative XLI 3-month return of -3.23%,XLI_RETURN_3M of -3.23% suggests sector-specific weakness.,IPMAN 3-month change of -0.49% is still well above the -2.0% stress threshold,3-month IPMAN contraction of -0.49% is significantly above the -2% stress threshold.,0.010000000000000009,False
+2022-03-01,2022-06-01,0,0.075,0.085,0.005625,0.007225000000000001,down,up,"Recent one-month IPMAN growth of 0.58% and a three-month contraction of -0.50% remain well above the -2.0% stress threshold, though elevated VIX and negative stock returns warrant caution.","The 3-month IPMAN contraction is mild (-0.5%) and well above the -2% stress threshold. However, elevated VIX (30.15) and negative 3-month SPY returns suggest increased market volatility and potential downside risks to manufacturing.",One-month IPMAN growth of 0.58% and three-month change of -0.50% are well above the -2.0% stress threshold,Elevated VIX (30.15) and negative 3-month SPY returns (-3.8%) indicate heightened market risk.,Elevated VIX of 30.15 and negative three-month SPY return of -3.82% indicate market stress,Current 3-month IPMAN contraction of -0.5% remains significantly above the -2% stress threshold.,-0.010000000000000009,False
+2022-04-01,2022-07-01,0,0.07,0.07,0.004900000000000001,0.004900000000000001,down,down,"Positive short-term IPMAN growth (+0.45% monthly) and a 3-month expansion of +0.07% keep manufacturing far from the -2% stress threshold, though high inflation and a flat yield curve present medium-term headwinds.","The 3-month IPMAN change is positive (+0.07%), remaining well above the -2% stress threshold. Despite negative equity returns, the labor market (UNRATE 3.7%) and credit conditions suggest no immediate manufacturing contraction.",Positive 1-month IPMAN growth of 0.45% and 3-month growth of 0.07% keep the index far from the stress threshold.,SPY_RETURN_3M of -4.61% and XLI_RETURN_3M of -2.35% indicate potential sector headwinds.,A flat yield curve of 0.04 and high CPI inflation of 8.57% signal macroeconomic headwinds.,"IPMAN 3-month change is positive (+0.07%), significantly above the -2% stress threshold.",0.0,True
+2022-05-01,2022-08-01,0,0.07,0.075,0.004900000000000001,0.005625,down,neutral,"IPMAN 3-month growth of +0.99% remains well above the -2% stress threshold, indicating a solid buffer. However, macroeconomic headwinds like high inflation (8.23%) and a flat yield spread (0.04) warrant caution.","The 3-month IPMAN change remains positive (+0.98%), providing a significant buffer against the -2% stress threshold. While recent 1-month data shows a slight decline, macroeconomic indicators do not currently signal an imminent systemic manufacturing contraction.",Positive 3-month IPMAN growth of +0.99% provides a substantial buffer against the -2% stress threshold.,Recent 1-month IPMAN decline of 5.05%.,A flat yield spread of 0.04 and high CPI inflation of 8.23% signal rising macroeconomic headwinds.,"3-month IPMAN change remains positive at 0.98%, well above the -2% stress threshold.",-0.0049999999999999906,False
+2022-06-01,2022-09-01,0,0.075,0.075,0.005625,0.005625,neutral,neutral,"With trailing three-month IPMAN contraction at just -0.04%, well above the -2% threshold, immediate stress risk is low. However, a negative one-month IPMAN change (-0.45%) and declining equity returns warrant a cautious, near-anchor probability of 7.5%.","The 3-month IPMAN contraction of -0.04% remains well above the -2% stress threshold. While negative equity returns and elevated volatility suggest caution, the manufacturing sector shows no immediate signs of reaching the defined stress level.",Negative one-month IPMAN change of -0.45% and negative three-month XLI return of -4.88% indicate emerging headwinds.,Negative 3-month and 12-month returns for SPY and XLI suggest broader market headwinds.,"Current trailing three-month IPMAN change of -0.04% is far above the -2.0% stress threshold, supported by a low unemployment rate of 3.6%.",Current 3-month IPMAN contraction of -0.04% is significantly above the -2% stress threshold.,0.0,True
+2022-07-01,2022-10-01,0,0.12,0.11,0.0144,0.0121,up,up,"IPMAN's three-month decline of -1.04% and negative momentum, combined with high inflation, a flat yield curve, and falling equity markets, elevate the risk of manufacturing stress, justifying an upward adjustment from the anchor.","The 3-month IPMAN contraction of -1.04% is approaching the -2% threshold. Negative equity returns (SPY -16.1%) and elevated credit spreads (2.31) suggest rising macroeconomic headwinds, warranting a modest upward adjustment from the anchor.",Three-month IPMAN decline of -1.04% with consecutive monthly drops; Weak macro signals including a flat yield curve of 0.06% and negative SPY/XLI returns,SPY_RETURN_3M of -16.11% indicates significant market stress.; CREDIT_SPREAD of 2.31 suggests tightening financial conditions.,Current trailing three-month IPMAN change of -1.04% remains above the -2.0% stress threshold,Current 3-month IPMAN contraction of -1.04% remains above the -2% stress threshold.,0.009999999999999995,True
+2022-08-01,2022-11-01,0,0.075,0.072,0.005625,0.005183999999999999,neutral,down,"The 3-month IPMAN contraction of -0.91% remains above the -2.0% stress threshold, supported by positive 1-month growth, though an inverted yield curve and high inflation present countervailing risks.","The 3-month IPMAN contraction of -0.91% remains well above the -2% stress threshold. While the inverted yield curve suggests future risk, current manufacturing data shows recent monthly stabilization, supporting a slight downward adjustment from the anchor.",Recent 1-month IPMAN growth of 0.08% and a 3-month contraction of -0.91% that remains well above the -2.0% stress threshold.,3-month IPMAN contraction of -0.91% is significantly above the -2% stress threshold.,Inverted yield curve (YC_SPREAD of -0.22) and high inflation (CPI_YOY of 8.46%) signal macroeconomic headwinds.,Inverted yield curve (-0.22) and negative 12-month equity returns indicate potential macroeconomic headwinds.,0.0030000000000000027,False
+2022-09-01,2022-12-01,1,0.095,0.085,0.819025,0.8372250000000001,up,up,"Elevated inflation and an inverted yield curve signal rising macroeconomic headwinds, though current IPMAN contraction remains well above the stress threshold.","The 3-month IPMAN contraction is currently -0.48%, well above the -2% threshold. However, the inverted yield curve and negative equity returns suggest rising macroeconomic headwinds, warranting a slight upward adjustment from the anchor.",Inverted yield curve (YC_SPREAD of -0.30) and negative 12-month equity returns signal macroeconomic headwinds.,Inverted yield curve (YC_SPREAD: -0.30); Negative 3-month and 12-month SPY returns,Current 3-month IPMAN contraction of -0.49% is well above the -2.0% stress threshold.,3-month IPMAN contraction (-0.48%) remains significantly above the -2% stress threshold,0.009999999999999995,True
+2022-10-01,2023-01-01,0,0.085,0.0775,0.007225000000000001,0.00600625,up,neutral,"While current IPMAN remains stable and above the stress threshold, the inverted yield curve and negative equity returns signal rising macroeconomic headwinds, justifying a slight upward adjustment from the anchor.","The 3-month IPMAN change is positive (+5.65%), providing a significant buffer against the -2% stress threshold. Despite negative equity returns, the current IPMAN trend does not indicate imminent manufacturing stress.",Inverted yield curve (YC_SPREAD of -0.30) and negative 12-month equity returns (SPY_RETURN_12M of -11.24%),SPY_RETURN_3M of -3.89% and XLI_RETURN_3M of -1.44% suggest potential headwinds.,"Current 3-month IPMAN change is positive at 0.056%, well above the -2.0% stress threshold.","IPMAN 3-month change is +5.65%, well above the -2% stress threshold.",0.007500000000000007,False
+2022-11-01,2023-02-01,0,0.07,0.085,0.004900000000000001,0.007225000000000001,neutral,up,"Recent IPMAN growth of 0.22% over three months provides a buffer against the -2% stress threshold. However, macroeconomic headwinds, including an inverted yield curve (-0.41%) and high inflation, keep overall stress risks elevated near the anchor.","The 3-month IPMAN trend is positive (+0.22%), providing a buffer against the -2% stress threshold. However, the inverted yield curve (-0.41) and negative equity returns suggest rising macroeconomic headwinds, warranting a slight upward adjustment from the anchor.",Positive three-month IPMAN growth of 0.22% and low unemployment rate of 3.6%,Inverted yield curve (YC_SPREAD: -0.41); Negative 12-month SPY return (-14.63%),Inverted yield curve of -0.41% and elevated inflation at 7.76%,Positive 3-month IPMAN growth (+0.22%); Low unemployment rate (3.6%),-0.015,False
+2022-12-01,2023-03-01,0,0.085,0.085,0.007225000000000001,0.007225000000000001,up,up,"Recent sharp monthly IPMAN decline of -0.80% and inverted yield curve increase stress risk, though the current three-month contraction remains well above the -2.0% threshold.","The 3-month IPMAN contraction of -0.56% remains well above the -2% stress threshold. However, the inverted yield curve (-0.70) and negative 12-month SPY returns suggest rising macroeconomic headwinds, warranting a slight upward adjustment from the anchor.","IPMAN declined by -0.80% in the latest month, and the yield curve is inverted at -0.70%.",Inverted yield curve (YC_SPREAD: -0.70),The current trailing 3-month IPMAN change of -0.56% is still far from the -2.0% stress threshold.,3-month IPMAN contraction (-0.56%) is significantly above the -2% stress threshold,0.0,True
+2023-01-01,2023-04-01,0,0.095,0.15,0.009025,0.0225,up,up,"Deeply inverted yield curve and high inflation suggest ongoing macro headwinds, though the low December IPMAN base makes a further 2% decline over the next three months less certain.","The 3-month IPMAN change is -2.28%, already exceeding the -2% stress threshold. Despite some positive equity returns, the inverted yield curve and negative industrial momentum suggest elevated risk of continued manufacturing contraction.",Deeply inverted yield curve (YC_SPREAD of -0.70) and high inflation (CPI_YOY of 6.40%) indicate significant macroeconomic headwinds.,3-month IPMAN change of -2.28% has breached the -2% stress threshold.,"The sharp 1-month IPMAN decline of -1.75% in December lowers the base, making an additional 2% drop by March less likely.",Positive 3-month XLI and SPY returns suggest some resilience in industrial and broader equity markets.,-0.05499999999999999,True
+2023-02-01,2023-05-01,0,0.068,0.07,0.0046240000000000005,0.004900000000000001,down,down,"Strong 1.75% monthly IPMAN rebound in January reduces immediate stress risk, leaving the 3-month change at -0.82%, well above the -2% threshold, despite an inverted yield curve.","The 3-month IPMAN contraction of -0.82% remains well above the -2% stress threshold. Recent 1-month growth of 1.75% suggests stabilization, reducing the likelihood of hitting the stress threshold within the three-month horizon.","Strong 1.75% monthly IPMAN rebound in January 2023, keeping the 3-month change at -0.82%, well above the -2% stress threshold.",3-month IPMAN contraction of -0.82% is significantly above the -2% stress threshold.,Inverted yield curve (YC_SPREAD of -0.69) and elevated FEDFUNDS rate of 4.33% signal ongoing macroeconomic headwinds.,Recent 1-month IPMAN growth of 1.75% indicates a potential recovery trend.,-0.0020000000000000018,True
+2023-03-01,2023-06-01,0,0.07,0.07,0.004900000000000001,0.004900000000000001,down,down,"Recent IPMAN growth (+0.03% over three months) indicates stabilization, offsetting risks from an inverted yield curve (-0.89%) and negative equity returns.","The 3-month IPMAN change is positive (+2.73%), well above the -2% stress threshold. Despite an inverted yield curve and negative equity returns, the recent manufacturing momentum suggests a low probability of hitting the stress threshold soon.",Inverted yield curve with YC_SPREAD at -0.89% and negative SPY 12-month return of -7.77%,Inverted yield curve (YC_SPREAD -0.89) and negative 3-month SPY returns (-2.35%) suggest macroeconomic headwinds.,Positive 3-month IPMAN growth of 0.03% and 1-month growth of 0.05% indicating near-term stabilization,Current 3-month IPMAN change (+2.73%) is significantly above the -2% stress threshold.,0.0,True
+2023-04-01,2023-07-01,0,0.09,0.076,0.0081,0.005776,up,neutral,"Deep yield curve inversion (-0.89%) and high interest rates (4.65%) elevate future stress risks, despite a positive 3-month IPMAN growth buffer (+1.15%).","The 3-month IPMAN change is positive (+1.15%), providing a significant buffer against the -2% stress threshold. Despite inverted yield curves and negative equity returns, manufacturing data does not currently signal imminent stress.",Deeply inverted yield curve (YC_SPREAD of -0.89%) and high federal funds rate (4.65%),3-month IPMAN change is positive at 1.15%,Positive 3-month IPMAN growth of 1.15% provides a buffer from the stress threshold,Inverted yield curve (YC_SPREAD -0.89) and negative 12-month SPY returns,0.013999999999999999,False
+2023-05-01,2023-08-01,0,0.065,0.065,0.0042250000000000005,0.0042250000000000005,down,neutral,"Recent IPMAN growth (+0.95% monthly, +0.35% quarterly) indicates stabilization, well above the -2% stress threshold. Despite an inverted yield curve, low unemployment and stable credit spreads support a slight downward adjustment from the anchor.","The 3-month IPMAN change is positive (+0.35%), well above the -2% stress threshold. While the inverted yield curve suggests long-term risk, current manufacturing data shows stability, keeping the probability near the statistical anchor.",Recent 1-month IPMAN growth of +0.95% and 3-month growth of +0.35% show stabilization above the stress threshold.,3-month IPMAN change is positive at 0.35%,The yield curve remains inverted at -0.60% and XLI 3-month return is negative at -1.41%.,Inverted yield curve (YC_SPREAD -0.60),0.0,False
+2023-06-01,2023-09-01,0,0.07,0.065,0.004900000000000001,0.0042250000000000005,neutral,down,"Starting from the 7.3% anchor, we adjust to 0.070. Positive three-month IPMAN growth of 0.12% provides a buffer, though the inverted yield curve (-0.76%) and negative XLI returns (-3.69%) signal some underlying risk.","The 3-month IPMAN change is positive (+0.12%), remaining well above the -2% stress threshold. While the yield curve is inverted, current manufacturing data shows stability, justifying a slight downward adjustment from the anchor.",Inverted yield curve (YC_SPREAD of -0.76%) and negative 3-month XLI return (-3.69%),3-month IPMAN change is positive at 0.116%,Positive 3-month IPMAN growth of 0.12% and low unemployment rate of 3.6%,Inverted yield curve (YC_SPREAD: -0.76),0.0050000000000000044,False
+2023-07-01,2023-10-01,0,0.075,0.065,0.005625,0.0042250000000000005,neutral,neutral,"The 3-month IPMAN change remains positive at 0.068%, well above the -2% stress threshold. However, a negative 1-month IPMAN change of -0.69% and an inverted yield curve suggest some emerging risks, keeping the outlook neutral.","The 3-month IPMAN change remains positive (0.069%), providing a significant buffer against the -2% stress threshold. Despite a negative 1-month change, the overall trend does not suggest imminent manufacturing stress.",Inverted yield curve of -0.76% and negative 1-month IPMAN change of -0.69% indicate emerging risks.,Negative 1-month IPMAN change of -0.69%,Current 3-month IPMAN change of 0.068% is well above the -2.0% stress threshold.,Positive 3-month IPMAN change of 0.069% and 6-month change of 1.22%,0.009999999999999995,True
+2023-08-01,2023-11-01,0,0.06,0.06,0.0036,0.0036,down,down,Recent monthly IPMAN growth of 0.40% and a 3-month contraction of -0.48% remain well above the -2.0% stress threshold. Strong equity returns and low credit spreads support a downward adjustment to 0.060.,The current 3-month IPMAN contraction of -0.48% is well above the -2% stress threshold. Recent monthly growth and stable macro indicators suggest low risk of reaching the stress threshold within the three-month horizon.,Recent 1-month IPMAN growth of 0.40% and strong 3-month XLI return of 10.86%,Current 3-month IPMAN contraction of -0.48% is significantly above the -2% stress threshold.,Inverted yield curve of -0.91% and high federal funds rate of 5.12%,None are material; recent 1-month IPMAN growth of 0.40% indicates stabilization.,0.0,True
+2023-09-01,2023-12-01,0,0.06,0.06,0.0036,0.0036,down,down,"IPMAN's 3-month decline of -0.34% is well above the -2% stress threshold. Strong equity returns and low credit spreads suggest low risk, justifying a downward adjustment from the anchor.","The current 3-month IPMAN contraction of -0.34% is well above the -2% stress threshold. Recent monthly data shows stabilization, and macroeconomic indicators do not signal an imminent systemic shock to the manufacturing sector.",IPMAN 3-month change of -0.34% is far above the -2.0% stress threshold,None are material,Inverted yield spread of -0.76% and high Fed Funds rate of 5.33% present macroeconomic headwinds,Current 3-month trailing IPMAN contraction of -0.34% is significantly above the -2% stress threshold.,0.0,True
+2023-10-01,2024-01-01,0,0.06,0.06,0.0036,0.0036,down,down,"Positive three-month IPMAN growth of 0.39% provides a substantial buffer above the -2% stress threshold, while strong equity returns suggest low near-term risk, justifying a downward adjustment from the anchor.","The current 3-month IPMAN change is positive (+0.39%), significantly above the -2% stress threshold. Recent growth and stable macro indicators suggest low risk of hitting the stress threshold within the three-month horizon.",Positive 3-month IPMAN growth of 0.39% is well above the -2.0% stress threshold.,Current 3-month IPMAN change is positive at 0.39%.,Inverted yield curve of -0.76% and high federal funds rate of 5.33%.,None are material.,0.0,True
+2023-11-01,2024-02-01,0,0.07,0.07,0.004900000000000001,0.004900000000000001,neutral,neutral,"While negative equity and industrial sector returns suggest rising risks, the current three-month IPMAN contraction of -0.54% remains well above the -2.0% stress threshold, warranting a minor downward adjustment from the anchor.","The current 3-month IPMAN contraction of -0.54% remains well above the -2% stress threshold. Despite negative equity returns, the lack of rapid deterioration in manufacturing output supports a stable, low-probability outlook.",Negative three-month XLI return of -10.56% and SPY return of -8.33% indicate market concern.,3-month IPMAN contraction of -0.54% is significantly above the -2% stress threshold.,Current three-month IPMAN contraction of -0.54% is well above the -2.0% stress threshold.,Negative 3-month returns in SPY and XLI suggest potential headwinds for the manufacturing sector.,0.0,True
+2023-12-01,2024-03-01,0,0.06,0.065,0.0036,0.0042250000000000005,down,neutral,"Recent 1-month IPMAN growth of 0.48% and a flat 3-month trend (-0.02%) keep manufacturing well above the -2.0% stress threshold, justifying a downward adjustment from the anchor.","The 3-month IPMAN contraction of -1.76% is approaching the -2% threshold, but recent 1-month growth (+0.48%) and stable macro indicators like low unemployment and moderate credit spreads suggest stress is not imminent.",Recent 1-month IPMAN growth of 0.48% and flat 3-month trend of -0.02% indicate near-term stabilization.,3-month IPMAN contraction of -1.76% is nearing the -2% stress threshold.,Inverted yield curve of -0.36 and high FEDFUNDS rate of 5.33% present ongoing macroeconomic headwinds.,Recent 1-month IPMAN growth of 0.48% and low unemployment rate (3.7%) indicate resilience.,-0.0050000000000000044,False
+2024-01-01,2024-04-01,0,0.06,0.06,0.0036,0.0036,down,down,"With trailing three-month IPMAN contraction at just -0.06%, well above the -2.0% stress threshold, and strong equity returns (XLI up 13.1%), the risk of manufacturing stress remains low. We adjust the anchor slightly downward to 0.060.","The 3-month IPMAN contraction of -6.08% significantly exceeds the -2% stress threshold. However, recent monthly stabilization and strong equity market returns suggest a lower risk of sustained decline, justifying a slight downward adjustment from the anchor.",Trailing 3-month IPMAN change of -0.06% is far above the -2.0% stress threshold; Strong industrial sector equity performance with XLI 3-month return of 13.05%,3-month IPMAN contraction of -6.08% is well below the -2% stress threshold.,Inverted yield curve with YC_SPREAD at -0.35 and elevated FEDFUNDS rate of 5.33%,Strong 3-month SPY and XLI returns suggest resilient industrial and market sentiment.,0.0,True
+2024-02-01,2024-05-01,0,0.08,0.06,0.0064,0.0036,up,down,"IPMAN declined sharply by 1.54% in January, bringing the 3-month change to -1.07%. Coupled with an inverted yield curve, this elevates stress risks, though strong equity returns provide a countervailing buffer.","The current 3-month IPMAN contraction of -1.07% remains well above the -2% stress threshold. Strong equity market returns and low unemployment suggest resilience, justifying a slight downward adjustment from the statistical anchor.",Sharp 1-month IPMAN decline of -1.54% in January 2024 and an inverted yield curve of -0.28%.,Current 3-month IPMAN contraction of -1.07% is significantly above the -2% stress threshold.,Strong equity market performance with SPY 3-month return at 15.93% and XLI 3-month return at 15.40%.,Strong 3-month SPY and XLI returns indicate positive market sentiment and potential manufacturing resilience.,0.020000000000000004,False
+2024-03-01,2024-06-01,0,0.058,0.06,0.0033640000000000002,0.0036,down,down,"Recent 1-month IPMAN rebound of 1.26% and a 3-month change of -0.30% leave a substantial buffer above the -2.0% stress threshold, while strong equity returns and low credit spreads suggest low near-term risk.","The 3-month IPMAN contraction of -0.3% is well above the -2% stress threshold. Recent monthly growth and strong equity market returns suggest manufacturing stability, justifying a slight downward adjustment from the statistical anchor.",Inverted yield curve (YC_SPREAD at -0.39%) and high federal funds rate (5.33%) continue to pose restrictive monetary headwinds.,IPMAN 3-month trailing change of -0.3% is significantly above the -2% stress threshold.,"Strong 1-month IPMAN rebound of 1.26% and 3-month change of -0.30% are well above the -2.0% stress threshold, supported by robust XLI returns.",None are material.,-0.001999999999999995,True
+2024-04-01,2024-07-01,0,0.06,0.06,0.0036,0.0036,down,down,"IPMAN's three-month contraction of -0.16% is far from the -2.0% stress threshold, with recent monthly growth of 0.14% showing stabilization. Strong equity returns (XLI up 10.8%) offset the inverted yield curve risk.","The current 3-month IPMAN contraction of -0.16% is well above the -2% stress threshold. Recent monthly growth and strong equity market returns suggest manufacturing stability, justifying a slight downward adjustment from the statistical anchor.",Recent 1-month IPMAN growth of 0.14% and 3-month contraction of -0.16% are well above the -2.0% stress threshold.,Current 3-month IPMAN contraction of -0.16% is significantly above the -2% stress threshold.,"The yield curve remains inverted at -0.39%, signaling potential macroeconomic headwinds.",Positive 1-month IPMAN growth and strong 3-month equity market returns (SPY/XLI) indicate resilience.,0.0,True
+2024-05-01,2024-08-01,0,0.06,0.06,0.0036,0.0036,down,down,"Positive three-month IPMAN growth of 0.85% and strong industrial stock returns keep stress risk low, offsetting the inverted yield curve. We adjust the anchor downward to 0.060.","The 3-month IPMAN change is positive (+0.85%), providing a significant buffer against the -2% stress threshold. Macro indicators, including stable credit spreads and positive equity returns, suggest low near-term risk of manufacturing stress.",Positive 3-month IPMAN growth of 0.85% and strong XLI 3-month return of 7.98% indicate near-term resilience.,Positive 3-month IPMAN change of 0.85%,Inverted yield curve (YC_SPREAD of -0.35%) and high FEDFUNDS rate of 5.33% present ongoing macroeconomic headwinds.,None are material,0.0,True
+2024-06-01,2024-09-01,0,0.06,0.06,0.0036,0.0036,down,down,"With recent 1-month and 3-month IPMAN growth positive (0.56% and 0.15% respectively), the index remains well above the -2% stress threshold. Strong equity returns and stable credit spreads support a downward adjustment to 0.060.","The 3-month IPMAN change is positive (+0.15%), well above the -2% stress threshold. Recent monthly data shows stability, and macroeconomic indicators like positive XLI returns and stable credit spreads suggest low near-term stress risk.","Positive 1-month and 3-month IPMAN growth of 0.56% and 0.15% respectively, keeping the index far from the -2% stress threshold.",3-month IPMAN change is positive at 0.15%,Inverted yield curve (YC_SPREAD of -0.35) and elevated FEDFUNDS rate of 5.33% present potential macroeconomic headwinds.,None are material,0.0,True
+2024-07-01,2024-10-01,0,0.06,0.06,0.0036,0.0036,down,down,"With trailing three-month IPMAN contraction at -0.17%, well above the -2.0% stress threshold, and low credit spreads, near-term stress risk remains low, though an inverted yield curve warrants caution.","The current 3-month IPMAN contraction of -0.17% is far from the -2% stress threshold. Recent monthly data shows relative stability, and macroeconomic indicators do not signal an imminent systemic manufacturing downturn.",Inverted yield curve (YC_SPREAD of -0.35%) and negative 3-month XLI return (-2.91%) indicate potential industrial headwinds.,3-month IPMAN contraction of -0.17% is well above the -2% stress threshold.,"Current 3-month IPMAN contraction of -0.17% is far from the -2.0% stress threshold, and credit spreads remain low at 1.55%.",None are material.,0.0,True
+2024-08-01,2024-11-01,0,0.068,0.064,0.0046240000000000005,0.004096,neutral,neutral,"Recent 1-month IPMAN decline of -0.78% and inverted yield curve suggest rising risk, but strong equity returns and a 3-month IPMAN change of -0.41% keep the probability near the anchor of 0.064.","The 3-month IPMAN contraction of -0.41% remains well above the -2% stress threshold. Despite slight recent volatility, macroeconomic indicators like positive equity returns and stable unemployment suggest no immediate systemic manufacturing stress.",Recent 1-month IPMAN decline of -0.78% and inverted yield curve of -0.20%,3-month IPMAN contraction of -0.41% is significantly above the -2% stress threshold.,Strong 3-month SPY return of 10.08% and XLI return of 5.58%,None are material; current macro signals and IPMAN trends do not indicate imminent stress.,0.0040000000000000036,True
+2024-09-01,2024-12-01,0,0.058,0.06,0.0033640000000000002,0.0036,down,down,"With 1-month IPMAN rebounding by 0.48% and the 3-month contraction (-0.49%) remaining far from the -2.0% stress threshold, near-term risk is low. We adjust the anchor downward to 0.058.",The 3-month IPMAN contraction of -0.49% remains well above the -2% stress threshold. Recent monthly growth and stable macro indicators suggest low risk of reaching the stress threshold within the three-month horizon.,Recent 1-month IPMAN growth of 0.48% indicates a rebound and stabilization.,3-month trailing IPMAN contraction of -0.49% is significantly above the -2% stress threshold.,"The yield curve remains inverted at -0.20%, and the 3-month IPMAN change is still slightly negative at -0.49%.",None are material.,-0.001999999999999995,True
+2024-10-01,2025-01-01,0,0.058,0.0622,0.0033640000000000002,0.0038688399999999997,down,neutral,"With trailing three-month IPMAN contraction at -0.87%, well above the -2.0% stress threshold, and strong equity returns (XLI up 11.5%), the risk of manufacturing stress remains low, justifying a slight downward adjustment from the anchor.","The current 3-month IPMAN contraction of -0.87% remains significantly above the -2% stress threshold. Macroeconomic indicators, including positive equity returns and stable credit spreads, suggest no immediate systemic manufacturing stress.",Trailing three-month IPMAN contraction of -0.87% remains well above the -2.0% stress threshold.,Trailing 3-month IPMAN contraction of -0.87% is well above the -2% stress threshold.,"Recent one-month IPMAN declined by -0.56%, indicating some short-term softening.",Positive 3-month and 12-month SPY and XLI returns indicate resilient market sentiment.,-0.004199999999999995,False
+2024-11-01,2025-02-01,0,0.062,0.06,0.0038439999999999998,0.0036,neutral,down,"IPMAN's 3-month contraction of -0.75% remains well above the -2.0% stress threshold. Despite recent monthly declines, stable macro indicators and strong equity returns support keeping the stress probability near the anchor of 6.4%.","The current 3-month IPMAN contraction of -0.75% remains well above the -2% stress threshold. Recent macro indicators, including positive XLI returns and stable credit spreads, suggest limited near-term risk of manufacturing stress.","Recent consecutive monthly declines in IPMAN, including a 1-month drop of -0.67%.",Current 3-month IPMAN contraction of -0.75% is significantly above the -2% stress threshold.,"The 3-month IPMAN change of -0.75% is well above the -2.0% stress threshold, and XLI 3-month return is positive at 5.01%.",None are material.,0.0020000000000000018,False
+2024-12-01,2025-03-01,0,0.06,0.063,0.0036,0.003969,down,neutral,"Recent 1-month IPMAN growth of 0.29% and strong equity returns suggest stabilization, keeping the 3-month contraction (-0.93%) well above the -2.0% stress threshold. We adjust the anchor slightly downward to 0.060.","The current 3-month IPMAN contraction of -0.93% remains well above the -2% stress threshold. Recent monthly growth suggests stabilization, and macroeconomic indicators do not signal an imminent systemic shock to manufacturing.",Negative trailing 3-month and 6-month IPMAN growth rates of -0.93% and -1.42%.,Current 3-month IPMAN contraction of -0.93% is significantly above the -2% stress threshold.,Positive 1-month IPMAN growth of 0.29% and strong 3-month XLI return of 5.01%.,None are material; current macro signals and recent IPMAN stabilization suggest low near-term stress risk.,-0.0030000000000000027,False
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/build_report.py b/implementations/manufacturing_stress_forecasting/reports/compare_all/build_report.py
new file mode 100644
index 000000000..4de344df3
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/build_report.py
@@ -0,0 +1,919 @@
+"""Build presentation-ready reports from the saved manufacturing-stress run.
+
+Agent predictions are loaded from the existing backtest artefacts. Only the
+deterministic predictors are recomputed, using the already-cached input data.
+"""
+
+from __future__ import annotations
+
+import ast
+import json
+import sys
+from datetime import datetime, timezone
+from html.parser import HTMLParser
+from pathlib import Path
+from typing import Any
+
+import matplotlib as mpl
+
+
+mpl.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import yaml
+
+
+REPO_ROOT = Path(__file__).resolve().parents[4]
+USE_CASE_DIR = REPO_ROOT / "implementations" / "manufacturing_stress_forecasting"
+for package_root in (REPO_ROOT / "aieng-forecasting", REPO_ROOT / "implementations"):
+    if str(package_root) not in sys.path:
+        sys.path.insert(0, str(package_root))
+
+from aieng.forecasting.data.context import ForecastContext
+from aieng.forecasting.evaluation import BacktestSpec, backtest
+from aieng.forecasting.evaluation.artifacts import load_backtest_result
+from aieng.forecasting.evaluation.backtest import BacktestResult
+from aieng.forecasting.evaluation.prediction import BinaryForecast, Prediction
+from aieng.forecasting.methods import HistoricalFrequencyPredictor
+from manufacturing_stress_forecasting.data import build_manufacturing_stress_service
+from manufacturing_stress_forecasting.features import (
+    CREDIT_SPREAD_SERIES_ID,
+    FEATURE_SERIES_IDS,
+    VIXCLS_SERIES_ID,
+    build_feature_snapshot,
+)
+from manufacturing_stress_forecasting.hybrid_agent import build_numerical_anchor
+from manufacturing_stress_forecasting.predictors import (
+    ManufacturingStressLogisticPredictor,
+    ManufacturingStressXGBoostPredictor,
+)
+from manufacturing_stress_forecasting.regimes import (
+    classify_regimes,
+    conditional_brier_table,
+    origin_only_threshold,
+)
+from manufacturing_stress_forecasting.run_agent_backtest import backtest_cache_id
+
+
+OUTPUT_DIR = Path(__file__).resolve().parent
+TABLES_DIR = OUTPUT_DIR / "tables"
+FIGURES_DIR = OUTPUT_DIR / "figures"
+NOTEBOOK_PATH = USE_CASE_DIR / "manufacturing_stress_workbench_2.ipynb"
+SPEC_PATH = USE_CASE_DIR / "specs" / "manufacturing_stress_smoke.yaml"
+STORE_DIR = REPO_ROOT / "data" / "predictions"
+
+MODEL_LABELS = {
+    "historical_frequency": "Historical frequency",
+    "logistic_c_0_001": "Logistic (C=0.001)",
+    "xgb_50_depth2_lr0_03_minchild3_l2_5": "XGBoost (50 trees)",
+    "hybrid_anchor": "Hybrid anchor (logistic)",
+    "analyst_agent": "Analyst agent",
+    "hybrid_agent_adjusted": "Hybrid agent adjusted",
+    "adaptive_agent": "Adaptive agent",
+}
+MODEL_DESCRIPTIONS = {
+    "historical_frequency": "Expanding historical event frequency; the Brier-score reference baseline.",
+    "logistic_c_0_001": "Fit-at-origin logistic regression (C=0.001) using cutoff-safe IPMAN, macroeconomic, and market features.",
+    "xgb_50_depth2_lr0_03_minchild3_l2_5": (
+        "XGBoost with 50 trees, max depth 2, learning rate 0.03, min_child_weight 3, and reg_lambda 5."
+    ),
+    "hybrid_anchor": "Deterministic anchor; logistic-only, not a 50/50 blend.",
+    "analyst_agent": "LLM analyst forecast from cutoff-safe evidence and historical event rate.",
+    "hybrid_agent_adjusted": "LLM-proposed probability adjusted from the logistic anchor, with the change bounded to ±0.03.",
+    "adaptive_agent": (
+        "Stateful LLM agent using the logistic anchor and persisted strategy; its adjustment is bounded to ±0.03."
+    ),
+}
+MODEL_ORDER = list(MODEL_LABELS)
+
+
+class _TableParser(HTMLParser):
+    """Read simple HTML table cells from saved notebook dataframe output."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.rows: list[list[str]] = []
+        self._row: list[str] | None = None
+        self._cell: list[str] | None = None
+
+    def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None:
+        if tag == "tr":
+            self._row = []
+        elif tag in {"th", "td"}:
+            self._cell = []
+
+    def handle_data(self, data: str) -> None:
+        if self._cell is not None:
+            self._cell.append(data)
+
+    def handle_endtag(self, tag: str) -> None:
+        if tag in {"th", "td"} and self._cell is not None:
+            assert self._row is not None
+            self._row.append(" ".join("".join(self._cell).split()))
+            self._cell = None
+        elif tag == "tr" and self._row:
+            self.rows.append(self._row)
+            self._row = None
+
+
+def _notebook_cell_by_execution(execution_count: int) -> dict[str, Any]:
+    notebook = json.loads(NOTEBOOK_PATH.read_text(encoding="utf-8"))
+    for cell in notebook["cells"]:
+        if cell.get("cell_type") == "code" and cell.get("execution_count") == execution_count:
+            return cell
+    raise ValueError(f"Notebook has no saved code-cell output for execution {execution_count}.")
+
+
+def _saved_frame_from_output(output: dict[str, Any]) -> pd.DataFrame:
+    html = output.get("data", {}).get("text/html")
+    if not html:
+        raise ValueError("Expected a saved HTML dataframe output.")
+    parser = _TableParser()
+    parser.feed("".join(html))
+    if len(parser.rows) < 2:
+        raise ValueError("Saved dataframe output did not contain a table.")
+    headers = parser.rows[0]
+    records = [dict(zip(headers, row, strict=False)) for row in parser.rows[1:] if len(row) == len(headers)]
+    frame = pd.DataFrame(records)
+    if headers and not headers[0]:
+        frame = frame.drop(columns=[""])
+    return frame
+
+
+def _saved_run_config() -> dict[str, Any]:
+    cell = _notebook_cell_by_execution(4)
+    for output in cell.get("outputs", []):
+        if output.get("output_type") == "stream":
+            text = "".join(output.get("text", []))
+            if text.startswith("{"):
+                return ast.literal_eval(text.strip())
+    raise ValueError("Saved notebook settings output could not be read.")
+
+
+class _AnchorPredictor:
+    @property
+    def predictor_id(self) -> str:
+        return "manufacturing_stress_hybrid_anchor_v1"
+
+    def predict(self, task: Any, context: ForecastContext) -> list[Prediction]:
+        anchor = build_numerical_anchor(task, context)
+        as_of = pd.Timestamp(context.as_of)
+        lead = pd.tseries.frequencies.to_offset(task.frequency) * task.horizons[0]
+        return [
+            Prediction(
+                predictor_id=self.predictor_id,
+                task_id=task.task_id,
+                issued_at=datetime.now(tz=timezone.utc).replace(tzinfo=None),
+                as_of=context.as_of,
+                forecast_date=(as_of + lead).to_pydatetime(),
+                payload=BinaryForecast(probability=anchor.probability),
+                metadata=anchor.as_dict(),
+            )
+        ]
+
+
+def _load_agent_result(spec: BacktestSpec, predictor_id: str) -> BacktestResult:
+    result = load_backtest_result(
+        backtest_cache_id(spec),
+        predictor_id,
+        store_dir=STORE_DIR,
+    )
+    if result is None:
+        raise FileNotFoundError(f"Missing saved agent result for {predictor_id!r}.")
+    if result.spec != spec:
+        raise ValueError(f"Cached agent result for {predictor_id!r} does not match the saved notebook spec.")
+    if result.skipped_origins:
+        raise ValueError(f"Cached agent result for {predictor_id!r} has skipped origins.")
+    return result
+
+
+def _build_results(spec: BacktestSpec, service: Any, run_config: dict[str, Any]) -> dict[str, BacktestResult]:
+    if not all(run_config.get(key, False) for key in ("run_analyst_agent", "run_hybrid_agent", "run_adaptive_agent")):
+        raise ValueError("Saved notebook run did not enable all three agent models.")
+
+    results: dict[str, BacktestResult] = {}
+    deterministic_predictors = [
+        ("historical_frequency", HistoricalFrequencyPredictor()),
+        ("logistic_c_0_001", ManufacturingStressLogisticPredictor(regularization_c=0.001)),
+        (
+            "xgb_50_depth2_lr0_03_minchild3_l2_5",
+            ManufacturingStressXGBoostPredictor(
+                n_estimators=50,
+                max_depth=2,
+                learning_rate=0.03,
+                min_child_weight=3.0,
+                reg_lambda=5.0,
+            ),
+        ),
+        ("hybrid_anchor", _AnchorPredictor()),
+    ]
+    for name, predictor in deterministic_predictors:
+        results[name] = backtest(predictor=predictor, spec=spec, data_service=service)
+
+    results["analyst_agent"] = _load_agent_result(
+        spec,
+        "agent_predictor_manufacturing_stress_analyst_gemini-3.1-flash-lite-preview_discrete",
+    )
+    results["hybrid_agent_adjusted"] = _load_agent_result(spec, "manufacturing_stress_hybrid_agent_v2")
+    results["adaptive_agent"] = _load_agent_result(spec, "manufacturing_stress_adaptive_agent_v1")
+    return results
+
+
+def _prediction_rows(results: dict[str, BacktestResult], service: Any) -> pd.DataFrame:
+    target = service.get_series(
+        "manufacturing_stress",
+        as_of=pd.Timestamp("2100-01-01").to_pydatetime(),
+    )
+    outcome_by_date = {
+        pd.Timestamp(timestamp): float(value)
+        for timestamp, value in zip(target["timestamp"], target["value"], strict=True)
+    }
+    rows: list[dict[str, Any]] = []
+    for model, result in results.items():
+        if len(result.predictions) != len(result.scores):
+            raise ValueError(f"Result {model!r} has a different number of predictions and scores.")
+        for prediction, score in zip(result.predictions, result.scores, strict=True):
+            metadata = prediction.metadata or {}
+            agent_metadata = metadata.get("agent_metadata") or {}
+            evidence = {**agent_metadata, **metadata}
+            forecast_date = pd.Timestamp(prediction.forecast_date)
+            row = {
+                "model": model,
+                "model_label": MODEL_LABELS[model],
+                "as_of": pd.Timestamp(prediction.as_of).date().isoformat(),
+                "forecast_date": forecast_date.date().isoformat(),
+                "probability": float(prediction.payload.probability),
+                "outcome": outcome_by_date[forecast_date],
+                "brier": float(score),
+                "anchor_probability": metadata.get("anchor_probability"),
+                "agent_probability": metadata.get("agent_probability"),
+                "raw_agent_adjustment": metadata.get("raw_agent_adjustment"),
+                "agent_adjustment": metadata.get("agent_adjustment"),
+                "adjusted_probability": metadata.get("adjusted_probability"),
+                "validation_status": metadata.get("validation_status"),
+                "rationale": evidence.get("rationale"),
+                "supporting_evidence": json.dumps(evidence.get("supporting_evidence", []), ensure_ascii=True),
+                "countervailing_evidence": json.dumps(evidence.get("countervailing_evidence", []), ensure_ascii=True),
+                "direction": evidence.get("direction"),
+                "langfuse_trace_id": evidence.get("langfuse_trace_id"),
+                "langfuse_trace_url": evidence.get("langfuse_trace_url"),
+            }
+            rows.append(row)
+    frame = pd.DataFrame(rows)
+    frame["model_order"] = frame["model"].map({model: index for index, model in enumerate(MODEL_ORDER)})
+    return frame.sort_values(["as_of", "model_order"]).drop(columns="model_order").reset_index(drop=True)
+
+
+def _saved_comparison_check(results: dict[str, BacktestResult]) -> None:
+    cell = _notebook_cell_by_execution(7)
+    output = next(
+        (
+            item
+            for item in cell.get("outputs", [])
+            if item.get("output_type") == "display_data" and "text/html" in item.get("data", {})
+        ),
+        None,
+    )
+    if output is None:
+        raise ValueError("Saved notebook is missing its model-comparison table.")
+    saved = _saved_frame_from_output(output)
+    if not {"model", "mean_brier", "scored_origins", "skipped_origins"}.issubset(saved.columns):
+        raise ValueError("Saved comparison table has an unexpected shape.")
+    for row in saved.to_dict(orient="records"):
+        result = results[str(row["model"])]
+        if not np.isclose(float(row["mean_brier"]), result.mean_score, atol=5e-7):
+            raise ValueError(
+                f"Recomputed {row['model']} Brier score does not match the notebook output "
+                f"({result.mean_score:.8f} vs {float(row['mean_brier']):.8f})."
+            )
+        if int(row["scored_origins"]) != len(result.scores) or int(row["skipped_origins"]) != result.skipped_origins:
+            raise ValueError(f"Origin counts for {row['model']} do not match the saved notebook output.")
+
+
+def _make_comparison(results: dict[str, BacktestResult]) -> pd.DataFrame:
+    baseline = results["historical_frequency"].mean_score
+    rows = [
+        {
+            "model": model,
+            "model_label": MODEL_LABELS[model],
+            "description": MODEL_DESCRIPTIONS[model],
+            "metric": result.metric,
+            "mean_brier": result.mean_score,
+            "scored_origins": len(result.scores),
+            "skipped_origins": result.skipped_origins,
+            "delta_vs_baseline": result.mean_score - baseline,
+            "brier_skill_vs_baseline": 1.0 - result.mean_score / baseline if baseline else np.nan,
+        }
+        for model, result in results.items()
+    ]
+    return pd.DataFrame(rows).sort_values("mean_brier").reset_index(drop=True)
+
+
+def _regime_rows(service: Any, origin_dates: list[pd.Timestamp]) -> pd.DataFrame:
+    rows = []
+    for origin in origin_dates:
+        context = service.context(as_of=origin.to_pydatetime())
+        feature_frames = {series_id: context.get_series(series_id) for series_id in FEATURE_SERIES_IDS}
+        snapshot = build_feature_snapshot(origin, feature_frames)
+        if snapshot is None:
+            rows.append(
+                {
+                    "as_of": origin.date().isoformat(),
+                    "momentum_regime": "unknown",
+                    "financial_regime": "unknown",
+                    "volatility_regime": "unknown",
+                }
+            )
+            continue
+        credit = feature_frames[CREDIT_SPREAD_SERIES_ID]
+        vix = feature_frames[VIXCLS_SERIES_ID]
+        credit_visible = credit[pd.to_datetime(credit["released_at"]) <= origin]["value"]
+        vix_visible = vix[pd.to_datetime(vix["released_at"]) <= origin]["value"]
+        try:
+            labels = classify_regimes(
+                snapshot,
+                historical_credit_median=origin_only_threshold(credit_visible, median=True),
+                historical_vix_percentile=origin_only_threshold(vix_visible, percentile=0.66),
+            )
+        except (KeyError, ValueError):
+            labels = {
+                "momentum_regime": "unknown",
+                "financial_regime": "unknown",
+                "volatility_regime": "unknown",
+            }
+        rows.append({"as_of": origin.date().isoformat(), **labels})
+    return pd.DataFrame(rows)
+
+
+def _regime_tables(origin_long: pd.DataFrame, service: Any) -> tuple[pd.DataFrame, pd.DataFrame]:
+    origins = sorted(pd.to_datetime(origin_long["as_of"]).unique())
+    labels = _regime_rows(service, [pd.Timestamp(origin) for origin in origins])
+    wide = origin_long.pivot(
+        index=["as_of", "forecast_date", "outcome"],
+        columns="model",
+        values="probability",
+    ).reset_index()
+    wide = wide.merge(labels, on="as_of", how="left", validate="one_to_one")
+
+    count_rows = []
+    conditional_rows = []
+    for dimension in ("momentum_regime", "financial_regime", "volatility_regime"):
+        counts = (
+            wide.groupby(dimension, dropna=False)
+            .agg(
+                origins=("outcome", "size"),
+                stress_events=("outcome", "sum"),
+                observed_event_rate=("outcome", "mean"),
+            )
+            .reset_index()
+            .rename(columns={dimension: "regime"})
+        )
+        counts.insert(0, "regime_dimension", dimension)
+        count_rows.extend(counts.to_dict(orient="records"))
+
+        conditional = conditional_brier_table(
+            wide,
+            predictor_columns=[model for model in MODEL_ORDER if model in wide.columns],
+            regime_column=dimension,
+            actual_column="outcome",
+        )
+        conditional.insert(0, "regime_dimension", dimension)
+        conditional["model_label"] = conditional["predictor"].map(MODEL_LABELS)
+        conditional_rows.extend(conditional.to_dict(orient="records"))
+
+    return pd.DataFrame(count_rows), pd.DataFrame(conditional_rows)
+
+
+def _hybrid_weight_sweep(wide: pd.DataFrame, comparison: pd.DataFrame) -> pd.DataFrame:
+    logistic = "logistic_c_0_001"
+    xgboost = "xgb_50_depth2_lr0_03_minchild3_l2_5"
+    brier_by_model = comparison.set_index("model")["mean_brier"]
+    rows = []
+    for weight in np.linspace(0.0, 1.0, 11):
+        probability = weight * wide[logistic] + (1.0 - weight) * wide[xgboost]
+        mean_brier = float(np.mean((probability - wide["outcome"]) ** 2))
+        rows.append(
+            {
+                "logistic_weight": round(float(weight), 1),
+                "xgboost_weight": round(float(1.0 - weight), 1),
+                "mean_brier": mean_brier,
+                "delta_vs_logistic": mean_brier - float(brier_by_model[logistic]),
+                "delta_vs_xgboost": mean_brier - float(brier_by_model[xgboost]),
+            }
+        )
+    return pd.DataFrame(rows)
+
+
+def _calibration_tables(origin_long: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
+    edges = np.linspace(0.0, 1.0, 6)
+    labels = [f"{left:.1f}-{right:.1f}" for left, right in zip(edges[:-1], edges[1:], strict=True)]
+    calibration_rows = []
+    histogram_rows = []
+    for model, group in origin_long.groupby("model", sort=False):
+        bucketed = group.assign(
+            probability_bin=pd.cut(
+                group["probability"],
+                bins=edges,
+                labels=labels,
+                include_lowest=True,
+                right=True,
+            )
+        )
+        calibration = (
+            bucketed.dropna(subset=["probability_bin"])
+            .groupby("probability_bin", observed=True)
+            .agg(
+                origins=("outcome", "size"),
+                mean_probability=("probability", "mean"),
+                observed_event_rate=("outcome", "mean"),
+            )
+        )
+        for probability_bin, row in calibration.iterrows():
+            calibration_rows.append(
+                {
+                    "model": model,
+                    "model_label": MODEL_LABELS[model],
+                    "probability_bin": str(probability_bin),
+                    **row.to_dict(),
+                }
+            )
+        counts, _ = np.histogram(group["probability"], bins=edges)
+        histogram_rows.extend(
+            {
+                "model": model,
+                "model_label": MODEL_LABELS[model],
+                "probability_bin": label,
+                "origins": int(count),
+            }
+            for label, count in zip(labels, counts, strict=True)
+        )
+    return pd.DataFrame(calibration_rows), pd.DataFrame(histogram_rows)
+
+
+def _adaptive_adjustments(origin_long: pd.DataFrame) -> pd.DataFrame:
+    adaptive = origin_long[origin_long["model"] == "adaptive_agent"].copy()
+    adjustment = adaptive["agent_adjustment"].dropna()
+    if adjustment.empty:
+        raise ValueError("Adaptive predictions are missing adjustment metadata.")
+    return pd.DataFrame(
+        [
+            {
+                "origins": len(adaptive),
+                "mean_anchor_probability": adaptive["anchor_probability"].mean(),
+                "mean_agent_proposal": adaptive["agent_probability"].mean(),
+                "mean_final_probability": adaptive["probability"].mean(),
+                "mean_applied_adjustment": adjustment.mean(),
+                "median_applied_adjustment": adjustment.median(),
+                "minimum_applied_adjustment": adjustment.min(),
+                "maximum_applied_adjustment": adjustment.max(),
+                "adjustments_up": int((adjustment > 0).sum()),
+                "adjustments_down": int((adjustment < 0).sum()),
+                "adjustments_unchanged": int((adjustment == 0).sum()),
+                "clamped_proposals": int((adaptive["validation_status"] == "clamped").sum()),
+            }
+        ]
+    )
+
+
+def _plot_comparison_diagnostics(origin_long: pd.DataFrame, comparison: pd.DataFrame) -> None:
+    palette = dict(zip(MODEL_ORDER, plt.get_cmap("tab10").colors, strict=False))
+    plot_data = origin_long.copy()
+    plot_data["as_of"] = pd.to_datetime(plot_data["as_of"])
+    plot_data["model_label"] = plot_data["model"].map(MODEL_LABELS)
+    plot_data["model_order"] = plot_data["model"].map({model: index for index, model in enumerate(MODEL_ORDER)})
+    plot_data = plot_data.sort_values(["model_order", "as_of"])
+    baseline = float(comparison.loc[comparison["model"] == "historical_frequency", "mean_brier"].iloc[0])
+
+    fig, axes = plt.subplots(2, 3, figsize=(18, 10), constrained_layout=True)
+    comparison.sort_values("mean_brier").plot.bar(
+        x="model_label", y="mean_brier", ax=axes[0, 0], legend=False, color="#2f6690"
+    )
+    axes[0, 0].set_title("Mean Brier score (lower is better)")
+    axes[0, 0].set_ylabel("Mean Brier score")
+    axes[0, 0].set_xlabel("")
+
+    skill = comparison.sort_values("brier_skill_vs_baseline")
+    skill.plot.bar(x="model_label", y="brier_skill_vs_baseline", ax=axes[0, 1], legend=False, color="#3a7d44")
+    axes[0, 1].axhline(0.0, color="black", linewidth=1)
+    axes[0, 1].set_title("Brier skill vs. historical-frequency baseline")
+    axes[0, 1].set_ylabel("Brier skill (positive is better)")
+    axes[0, 1].set_xlabel("")
+
+    for model, group in plot_data.groupby("model", sort=False):
+        axes[0, 2].plot(
+            group["as_of"], group["brier"], marker=".", linewidth=0.9, label=MODEL_LABELS[model], color=palette[model]
+        )
+    axes[0, 2].set_title("Per-origin Brier score")
+    axes[0, 2].set_ylabel("Brier score")
+    axes[0, 2].legend(fontsize=7)
+
+    for model, group in plot_data.groupby("model", sort=False):
+        axes[1, 0].plot(
+            group["as_of"],
+            group["brier"].expanding().mean(),
+            linewidth=1.2,
+            label=MODEL_LABELS[model],
+            color=palette[model],
+        )
+    axes[1, 0].axhline(baseline, color="black", linestyle="--", linewidth=1, label="Baseline")
+    axes[1, 0].set_title("Cumulative mean Brier score")
+    axes[1, 0].set_ylabel("Cumulative mean Brier")
+    axes[1, 0].legend(fontsize=7)
+
+    calibration, _ = _calibration_tables(origin_long)
+    for model, group in calibration.groupby("model", sort=False):
+        axes[1, 1].plot(
+            group["mean_probability"],
+            group["observed_event_rate"],
+            marker="o",
+            label=MODEL_LABELS[model],
+            color=palette[model],
+        )
+    axes[1, 1].plot([0, 1], [0, 1], "k--", alpha=0.6, linewidth=1)
+    axes[1, 1].set_title("Reliability (calibration) diagram")
+    axes[1, 1].set_xlabel("Mean predicted probability")
+    axes[1, 1].set_ylabel("Observed event rate")
+    axes[1, 1].legend(fontsize=7)
+
+    bins = np.linspace(0.0, 1.0, 6)
+    for model, group in plot_data.groupby("model", sort=False):
+        axes[1, 2].hist(group["probability"], bins=bins, alpha=0.35, label=MODEL_LABELS[model], color=palette[model])
+    axes[1, 2].set_title("Forecast probability distributions")
+    axes[1, 2].set_xlabel("Predicted stress probability")
+    axes[1, 2].set_ylabel("Forecast count")
+    axes[1, 2].legend(fontsize=7)
+
+    for axis in axes.flat:
+        axis.tick_params(axis="x", rotation=35, labelsize=7)
+        axis.grid(axis="y", alpha=0.2)
+    fig.suptitle("Manufacturing-stress model comparison — 84 monthly origins, 6 stress events", fontsize=15)
+    fig.savefig(FIGURES_DIR / "comparison_diagnostics.png", dpi=180)
+    plt.close(fig)
+
+
+def _plot_regime_diagnostics(conditional: pd.DataFrame) -> None:
+    dimensions = ("momentum_regime", "financial_regime", "volatility_regime")
+    fig, axes = plt.subplots(1, 3, figsize=(18, 5.5), constrained_layout=True)
+    for axis, dimension in zip(axes, dimensions, strict=True):
+        dimension_rows = conditional[conditional["regime_dimension"] == dimension]
+        matrix = dimension_rows.pivot(index="regime", columns="model_label", values="brier")
+        matrix = matrix.reindex(
+            columns=[MODEL_LABELS[model] for model in MODEL_ORDER if MODEL_LABELS[model] in matrix.columns]
+        )
+        matrix.plot.bar(ax=axis, width=0.8)
+        axis.set_title(f"Brier score by {dimension.replace('_', ' ')}")
+        axis.set_ylabel("Mean Brier (lower is better)")
+        axis.set_xlabel("")
+        axis.tick_params(axis="x", rotation=25)
+        axis.grid(axis="y", alpha=0.2)
+        if axis.get_legend() is not None:
+            axis.get_legend().remove()
+    handles, labels = axes[0].get_legend_handles_labels()
+    fig.legend(handles, labels, loc="lower center", ncol=4, fontsize=8, bbox_to_anchor=(0.5, -0.12))
+    fig.suptitle("Regime-conditional backtest performance", fontsize=15)
+    fig.savefig(FIGURES_DIR / "regime_conditional_brier.png", dpi=180, bbox_inches="tight")
+    plt.close(fig)
+
+
+def _plot_weight_sweep(weight_sweep: pd.DataFrame, comparison: pd.DataFrame) -> None:
+    xgb_score = float(
+        comparison.loc[comparison["model"] == "xgb_50_depth2_lr0_03_minchild3_l2_5", "mean_brier"].iloc[0]
+    )
+    logistic_score = float(comparison.loc[comparison["model"] == "logistic_c_0_001", "mean_brier"].iloc[0])
+    best = weight_sweep.loc[weight_sweep["mean_brier"].idxmin()]
+    fig, axis = plt.subplots(figsize=(9, 5.5), constrained_layout=True)
+    axis.plot(weight_sweep["logistic_weight"], weight_sweep["mean_brier"], marker="o", color="#2f6690")
+    axis.axhline(logistic_score, color="#3a7d44", linestyle="--", label="Logistic-only score")
+    axis.axhline(xgb_score, color="#bc4749", linestyle=":", label="XGBoost-only score")
+    axis.scatter([best["logistic_weight"]], [best["mean_brier"]], color="#bc4749", zorder=3)
+    axis.annotate(
+        f"Best tested: logistic weight {best['logistic_weight']:.1f}\nBrier {best['mean_brier']:.6f}",
+        (best["logistic_weight"], best["mean_brier"]),
+        xytext=(-140, 25),
+        textcoords="offset points",
+        arrowprops={"arrowstyle": "->", "color": "#444444"},
+    )
+    axis.set_title("Offline logistic / XGBoost weight sweep")
+    axis.set_xlabel("Logistic weight (XGBoost weight = 1 − logistic weight)")
+    axis.set_ylabel("Mean Brier score (lower is better)")
+    axis.set_xticks(weight_sweep["logistic_weight"])
+    axis.grid(alpha=0.2)
+    axis.legend()
+    fig.savefig(FIGURES_DIR / "hybrid_weight_sweep.png", dpi=180)
+    plt.close(fig)
+
+
+def _plot_adaptive_adjustments(origin_long: pd.DataFrame) -> None:
+    adaptive = origin_long[origin_long["model"] == "adaptive_agent"].copy()
+    adaptive["as_of"] = pd.to_datetime(adaptive["as_of"])
+    fig, axes = plt.subplots(2, 1, figsize=(12, 7), sharex=True, constrained_layout=True)
+    axes[0].plot(adaptive["as_of"], adaptive["anchor_probability"], label="Logistic anchor", color="#2f6690")
+    axes[0].plot(adaptive["as_of"], adaptive["probability"], label="Adaptive final probability", color="#bc4749")
+    axes[0].set_title("Adaptive-agent probability vs. logistic anchor")
+    axes[0].set_ylabel("Stress probability")
+    axes[0].legend()
+    axes[1].bar(adaptive["as_of"], adaptive["agent_adjustment"], width=22, color="#6c8e5e")
+    axes[1].axhline(0.0, color="black", linewidth=0.8)
+    axes[1].set_title("Applied adaptive-agent adjustment")
+    axes[1].set_ylabel("Adjustment")
+    axes[1].set_xlabel("Forecast origin")
+    for axis in axes:
+        axis.grid(axis="y", alpha=0.2)
+    fig.savefig(FIGURES_DIR / "adaptive_agent_adjustments.png", dpi=180)
+    plt.close(fig)
+
+
+def _write_plots(
+    origin_long: pd.DataFrame,
+    comparison: pd.DataFrame,
+    conditional: pd.DataFrame,
+    weight_sweep: pd.DataFrame,
+) -> None:
+    _plot_comparison_diagnostics(origin_long, comparison)
+    _plot_regime_diagnostics(conditional)
+    _plot_weight_sweep(weight_sweep, comparison)
+    _plot_adaptive_adjustments(origin_long)
+
+
+def _markdown_table(frame: pd.DataFrame, columns: list[str], formats: dict[str, str] | None = None) -> str:
+    formats = formats or {}
+    header = "| " + " | ".join(columns) + " |"
+    separator = "|" + "|".join("---" for _ in columns) + "|"
+    rows = []
+    for record in frame[columns].to_dict(orient="records"):
+        cells = []
+        for column in columns:
+            value = record[column]
+            if pd.isna(value):
+                rendered = ""
+            elif column in formats:
+                rendered = format(float(value), formats[column])
+            else:
+                rendered = str(value)
+            cells.append(rendered.replace("|", "\\|").replace("\n", " "))
+        rows.append("| " + " | ".join(cells) + " |")
+    return "\n".join([header, separator, *rows])
+
+
+def _write_report(
+    comparison: pd.DataFrame,
+    counts: pd.DataFrame,
+    weight_sweep: pd.DataFrame,
+    adaptive_summary: pd.DataFrame,
+    config: dict[str, Any],
+    spec: BacktestSpec,
+    origin_long: pd.DataFrame,
+) -> None:
+    events = int(origin_long.drop_duplicates(["as_of", "forecast_date"])["outcome"].sum())
+    total = origin_long.drop_duplicates(["as_of", "forecast_date"]).shape[0]
+    event_rate = events / total
+    top = comparison.iloc[0]
+    event_date_start = pd.to_datetime(origin_long["as_of"]).min().date().isoformat()
+    event_date_end = pd.to_datetime(origin_long["as_of"]).max().date().isoformat()
+    best_weight = weight_sweep.loc[weight_sweep["mean_brier"].idxmin()]
+    adjusted = adaptive_summary.iloc[0]
+
+    report = f"""# Manufacturing stress: all-model comparison
+
+## Executive summary
+
+This report consolidates the saved outputs from `manufacturing_stress_workbench_2.ipynb`.
+It compares seven forecasting methods on the same **{total} monthly forecast origins** from
+**{event_date_start} through {event_date_end}**. There were **{events} observed stress events**
+({event_rate:.1%}); all models scored every origin with no skipped forecasts.
+
+The historical-frequency baseline had the lowest mean Brier score (**{top["mean_brier"]:.6f}**).
+All model Brier skills relative to that baseline were non-positive. The result is descriptive,
+not evidence that any model is statistically inferior: six positive events provide limited
+information for a rare-event comparison.
+
+## Experiment
+
+- **Task:** estimate the probability that U.S. manufacturing is under IPMAN-defined stress three
+  months after each forecast origin.
+- **Outcome:** a resolved month is positive when IPMAN has fallen by at least 2% over the
+  preceding three months (percentage change is at or below −2%).
+- **Evaluation window:** `{spec.start.date().isoformat()}` to `{spec.end.date().isoformat()}` origins,
+  monthly stride `{spec.stride}`, with a three-month forecast horizon and `{spec.warmup}`-month
+  warm-up. The first/last scored forecast dates are 2018-04-01 and 2025-03-01.
+- **Scoring:** Brier score, the mean squared difference between the forecast probability and
+  binary outcome. Lower is better. Brier skill is `1 − model score / baseline score`; positive
+  values beat historical frequency.
+- **Inputs:** FRED series IPMAN, FEDFUNDS, DGS10, DGS2, CPIAUCSL, UNRATE, ICSA, VIXCLS, and
+  BAA10Y; Yahoo Finance adjusted-close histories for SPY and XLI. Derived features include
+  1-, 3-, and 6-month IPMAN changes; the federal-funds rate and 10-year-minus-2-year spread;
+  CPI level and year-over-year change; unemployment, initial claims, VIX, credit spread; and
+  3-/12-month SPY and XLI returns.
+- **Cutoff handling:** model features are limited to observations released by each origin.
+  Because the FRED adapter does not provide historical release vintages, IPMAN observations use a
+  conservative one-month release lag.
+- **Input handling:** the saved run used existing FRED and market-data caches (`refresh_input_data`
+  was `{config["refresh_input_data"]}`). All three agent options were enabled. Their saved YAML
+  results were reused here; this report build did not issue LLM calls. Deterministic methods were
+  recomputed locally from the same cached input data and checked against the notebook's saved
+  comparison table.
+- **Regime diagnostics:** momentum, financial-condition, and volatility labels were reconstructed
+  at each origin using only data released by that cutoff. They are descriptive slices; they did
+  not retune the models.
+- **Hybrid weight sweep:** this is an offline calculation over the already-scored logistic and
+  XGBoost probabilities. It is not a fitted model and does not change the active logistic-only
+  anchor.
+
+## Methods compared
+
+{_markdown_table(comparison, ["model_label", "description"])}
+
+## Overall results
+
+{_markdown_table(comparison, ["model_label", "mean_brier", "scored_origins", "skipped_origins", "delta_vs_baseline", "brier_skill_vs_baseline"], {"mean_brier": ".6f", "delta_vs_baseline": ".6f", "brier_skill_vs_baseline": ".4f"})}
+
+`hybrid_anchor` is intentionally identical to logistic regression because the active anchor is
+logistic-only. The agent rows report the final probability used for scoring; their proposal,
+anchor, applied adjustment, evidence, and trace references (where present) are included in the
+origin-level CSV.
+
+## Regime results
+
+The event counts below add to six within each regime dimension; a forecast origin is counted once
+in each separate dimension.
+
+{_markdown_table(counts, ["regime_dimension", "regime", "origins", "stress_events", "observed_event_rate"], {"observed_event_rate": ".1%"})}
+
+The full regime-conditional score and calibration diagnostics are in
+[`tables/regime_conditional_brier.csv`](tables/regime_conditional_brier.csv). Regimes with few
+origins/events should not be treated as stable performance rankings.
+
+## Offline hybrid weight sweep
+
+Weight 1.0 is pure logistic and weight 0.0 is pure XGBoost. The best tested point was logistic
+weight **{best_weight["logistic_weight"]:.1f}** (mean Brier **{best_weight["mean_brier"]:.6f}**),
+which is the logistic-only endpoint. No tested blend beat logistic-only or the historical-frequency
+baseline. The sweep is exploratory and does not change the configured anchor.
+
+{_markdown_table(weight_sweep, ["logistic_weight", "xgboost_weight", "mean_brier", "delta_vs_logistic", "delta_vs_xgboost"], {"mean_brier": ".6f", "delta_vs_logistic": ".6f", "delta_vs_xgboost": ".6f"})}
+
+## Adaptive-agent adjustments
+
+Across {int(adjusted["origins"])} origins, the adaptive agent's mean applied adjustment was
+{adjusted["mean_applied_adjustment"]:+.5f}; it adjusted upward on {int(adjusted["adjustments_up"])}
+origins and downward on {int(adjusted["adjustments_down"])}; it made no adjustment on
+{int(adjusted["adjustments_unchanged"])} origins. {int(adjusted["clamped_proposals"])} proposals
+were clamped by the ±0.03 adjustment bound. These are descriptive outputs, not proof that
+adaptation improved accuracy.
+
+## Figures and interpretation
+
+### All-model diagnostics
+
+![Six-panel model diagnostics](figures/comparison_diagnostics.png)
+
+The panels show mean Brier, Brier skill against baseline, per-origin Brier, cumulative Brier,
+reliability, and probability distributions. The reliability and histogram panels use five
+probability bins. Because the event is rare, these plots are sensitive to the small number of
+positive outcomes.
+
+### Regime-conditional Brier scores
+
+![Regime-conditional Brier scores](figures/regime_conditional_brier.png)
+
+Each panel compares models within one cutoff-safe regime dimension. The number of origins and
+events for each group is reported in `tables/regime_origin_counts.csv`.
+
+### Hybrid weight sweep
+
+![Offline hybrid weight sweep](figures/hybrid_weight_sweep.png)
+
+This plot varies only the logistic/XGBoost blend weight over the saved forecasts. It is not an
+additional backtest.
+
+### Adaptive-agent probability and adjustment
+
+![Adaptive-agent probability and adjustment](figures/adaptive_agent_adjustments.png)
+
+The upper panel compares the logistic anchor and adaptive final probability; the lower panel shows
+the applied adjustment at each origin.
+
+## Data files
+
+- [`tables/model_comparison.csv`](tables/model_comparison.csv): aggregate model metrics.
+- [`tables/origin_level_predictions.csv`](tables/origin_level_predictions.csv): long-format,
+  84-origin by seven-model results with probabilities, outcomes, scores, agent evidence, and
+  available Langfuse trace references.
+- [`tables/origin_level_probabilities_wide.csv`](tables/origin_level_probabilities_wide.csv):
+  compact one-row-per-origin comparison.
+- [`tables/regime_origin_counts.csv`](tables/regime_origin_counts.csv): cases, events, and event
+  rates by regime.
+- [`tables/regime_conditional_brier.csv`](tables/regime_conditional_brier.csv): conditional model
+  score and calibration metrics.
+- [`tables/hybrid_weight_sweep.csv`](tables/hybrid_weight_sweep.csv): all eleven tested weights.
+- [`tables/calibration_bins.csv`](tables/calibration_bins.csv) and
+  [`tables/probability_distribution_bins.csv`](tables/probability_distribution_bins.csv): data
+  behind the calibration and probability-distribution panels.
+- [`tables/adaptive_agent_adjustments.csv`](tables/adaptive_agent_adjustments.csv): summary of the
+  adaptive agent's anchor, proposal, applied adjustments, and clamping.
+
+## Limitations
+
+This is a development backtest, not a protected holdout or a causal evaluation of adaptation.
+Origins advance monthly while the target horizon is three months, so adjacent outcomes overlap.
+There are only six positive events, which limits calibration and regime-level conclusions.
+Model comparisons should be treated as exploratory pending a larger, untouched evaluation set.
+
+## Rebuilding this package
+
+Run `python reports/compare_all/build_report.py` from the repository root. The script requires the
+saved agent-result YAML files and cached input data. It loads agent artifacts and runs only local
+deterministic predictors; it does not call the LLM services or refresh external data.
+"""
+    (OUTPUT_DIR / "executive_report.md").write_text(report, encoding="utf-8")
+
+
+def main() -> None:
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    TABLES_DIR.mkdir(parents=True, exist_ok=True)
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+
+    config = _saved_run_config()
+    if config["refresh_input_data"]:
+        raise ValueError("Saved notebook run refreshed input data; this report script requires saved local data.")
+    required_data = [
+        *(
+            REPO_ROOT / "data" / "fred" / f"{series}.parquet"
+            for series in ("IPMAN", "FEDFUNDS", "DGS10", "DGS2", "CPIAUCSL", "UNRATE", "ICSA", "VIXCLS", "BAA10Y")
+        ),
+        REPO_ROOT / "data" / "yfinance" / "spy_adj_close_1d.parquet",
+        REPO_ROOT / "data" / "yfinance" / "xli_adj_close_1d.parquet",
+    ]
+    missing = [str(path) for path in required_data if not path.exists()]
+    if missing:
+        raise FileNotFoundError(f"Cannot build from local caches; missing input file(s): {missing}")
+
+    with SPEC_PATH.open(encoding="utf-8") as file:
+        base_spec = BacktestSpec.model_validate(yaml.safe_load(file))
+    spec = base_spec.model_copy(update={"stride": int(config["backtest_stride"])})
+    service = build_manufacturing_stress_service(
+        cache_dir=REPO_ROOT / "data" / "fred",
+        yahoo_cache_dir=REPO_ROOT / "data" / "yfinance",
+        refresh=False,
+    )
+    results = _build_results(spec, service, config)
+
+    reference_keys = {
+        (pd.Timestamp(prediction.as_of), pd.Timestamp(prediction.forecast_date))
+        for prediction in results["historical_frequency"].predictions
+    }
+    for model, result in results.items():
+        keys = {
+            (pd.Timestamp(prediction.as_of), pd.Timestamp(prediction.forecast_date))
+            for prediction in result.predictions
+        }
+        if keys != reference_keys:
+            raise ValueError(f"{model} did not score the same origin/forecast-date pairs as the baseline.")
+
+    _saved_comparison_check(results)
+    comparison = _make_comparison(results)
+    origin_long = _prediction_rows(results, service)
+    wide = origin_long.pivot(
+        index=["as_of", "forecast_date", "outcome"],
+        columns="model",
+        values="probability",
+    ).reset_index()
+    wide.columns.name = None
+    counts, conditional = _regime_tables(origin_long, service)
+    weight_sweep = _hybrid_weight_sweep(wide, comparison)
+    calibration, histogram = _calibration_tables(origin_long)
+    adaptive_summary = _adaptive_adjustments(origin_long)
+
+    comparison.to_csv(TABLES_DIR / "model_comparison.csv", index=False)
+    origin_long.to_csv(TABLES_DIR / "origin_level_predictions.csv", index=False)
+    wide.to_csv(TABLES_DIR / "origin_level_probabilities_wide.csv", index=False)
+    counts.to_csv(TABLES_DIR / "regime_origin_counts.csv", index=False)
+    conditional.to_csv(TABLES_DIR / "regime_conditional_brier.csv", index=False)
+    weight_sweep.to_csv(TABLES_DIR / "hybrid_weight_sweep.csv", index=False)
+    calibration.to_csv(TABLES_DIR / "calibration_bins.csv", index=False)
+    histogram.to_csv(TABLES_DIR / "probability_distribution_bins.csv", index=False)
+    adaptive_summary.to_csv(TABLES_DIR / "adaptive_agent_adjustments.csv", index=False)
+
+    _write_plots(origin_long, comparison, conditional, weight_sweep)
+    _write_report(comparison, counts, weight_sweep, adaptive_summary, config, spec, origin_long)
+    manifest = {
+        "source_notebook": str(NOTEBOOK_PATH.relative_to(REPO_ROOT)),
+        "cache_spec_id": backtest_cache_id(spec),
+        "backtest_window": {"start": spec.start.isoformat(), "end": spec.end.isoformat()},
+        "stride_months": spec.stride,
+        "horizon_months": spec.task.horizons[0],
+        "scored_origins": int(origin_long["as_of"].nunique()),
+        "models": MODEL_ORDER,
+        "agent_results_reused_from_cache": True,
+        "deterministic_models_recomputed_from_local_inputs": True,
+        "generated_utc": datetime.now(tz=timezone.utc).isoformat(),
+    }
+    (OUTPUT_DIR / "run_manifest.json").write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8")
+    print(f"Built report package at {OUTPUT_DIR}")
+    print(f"Scored origins: {manifest['scored_origins']}; models: {len(MODEL_ORDER)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/executive_report.md b/implementations/manufacturing_stress_forecasting/reports/compare_all/executive_report.md
new file mode 100644
index 000000000..6a66472e4
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/executive_report.md
@@ -0,0 +1,186 @@
+# Manufacturing stress: all-model comparison
+
+## Executive summary
+
+This report consolidates the saved outputs from `manufacturing_stress_workbench_2.ipynb`.
+It compares seven forecasting methods on the same **84 monthly forecast origins** from
+**2018-01-01 through 2024-12-01**. There were **6 observed stress events**
+(7.1%); all models scored every origin with no skipped forecasts.
+
+The historical-frequency baseline had the lowest mean Brier score (**0.066693**).
+All model Brier skills relative to that baseline were non-positive. The result is descriptive,
+not evidence that any model is statistically inferior: six positive events provide limited
+information for a rare-event comparison.
+
+## Experiment
+
+- **Task:** estimate the probability that U.S. manufacturing is under IPMAN-defined stress three
+  months after each forecast origin.
+- **Outcome:** a resolved month is positive when IPMAN has fallen by at least 2% over the
+  preceding three months (percentage change is at or below −2%).
+- **Evaluation window:** `2018-01-01` to `2024-12-01` origins,
+  monthly stride `1`, with a three-month forecast horizon and `60`-month
+  warm-up. The first/last scored forecast dates are 2018-04-01 and 2025-03-01.
+- **Scoring:** Brier score, the mean squared difference between the forecast probability and
+  binary outcome. Lower is better. Brier skill is `1 − model score / baseline score`; positive
+  values beat historical frequency.
+- **Inputs:** FRED series IPMAN, FEDFUNDS, DGS10, DGS2, CPIAUCSL, UNRATE, ICSA, VIXCLS, and
+  BAA10Y; Yahoo Finance adjusted-close histories for SPY and XLI. Derived features include
+  1-, 3-, and 6-month IPMAN changes; the federal-funds rate and 10-year-minus-2-year spread;
+  CPI level and year-over-year change; unemployment, initial claims, VIX, credit spread; and
+  3-/12-month SPY and XLI returns.
+- **Cutoff handling:** model features are limited to observations released by each origin.
+  Because the FRED adapter does not provide historical release vintages, IPMAN observations use a
+  conservative one-month release lag.
+- **Input handling:** the saved run used existing FRED and market-data caches (`refresh_input_data`
+  was `False`). All three agent options were enabled. Their saved YAML
+  results were reused here; this report build did not issue LLM calls. Deterministic methods were
+  recomputed locally from the same cached input data and checked against the notebook's saved
+  comparison table.
+- **Regime diagnostics:** momentum, financial-condition, and volatility labels were reconstructed
+  at each origin using only data released by that cutoff. They are descriptive slices; they did
+  not retune the models.
+- **Hybrid weight sweep:** this is an offline calculation over the already-scored logistic and
+  XGBoost probabilities. It is not a fitted model and does not change the active logistic-only
+  anchor.
+
+## Methods compared
+
+| model_label | description |
+|---|---|
+| Historical frequency | Expanding historical event frequency; the Brier-score reference baseline. |
+| Logistic (C=0.001) | Fit-at-origin logistic regression (C=0.001) using cutoff-safe IPMAN, macroeconomic, and market features. |
+| Hybrid anchor (logistic) | Deterministic anchor; logistic-only, not a 50/50 blend. |
+| Adaptive agent | Stateful LLM agent using the logistic anchor and persisted strategy; its adjustment is bounded to ±0.03. |
+| Hybrid agent adjusted | LLM-proposed probability adjusted from the logistic anchor, with the change bounded to ±0.03. |
+| XGBoost (50 trees) | XGBoost with 50 trees, max depth 2, learning rate 0.03, min_child_weight 3, and reg_lambda 5. |
+| Analyst agent | LLM analyst forecast from cutoff-safe evidence and historical event rate. |
+
+## Overall results
+
+| model_label | mean_brier | scored_origins | skipped_origins | delta_vs_baseline | brier_skill_vs_baseline |
+|---|---|---|---|---|---|
+| Historical frequency | 0.066693 | 84 | 0 | 0.000000 | 0.0000 |
+| Logistic (C=0.001) | 0.068921 | 84 | 0 | 0.002229 | -0.0334 |
+| Hybrid anchor (logistic) | 0.068921 | 84 | 0 | 0.002229 | -0.0334 |
+| Adaptive agent | 0.069205 | 84 | 0 | 0.002512 | -0.0377 |
+| Hybrid agent adjusted | 0.070689 | 84 | 0 | 0.003997 | -0.0599 |
+| XGBoost (50 trees) | 0.072189 | 84 | 0 | 0.005496 | -0.0824 |
+| Analyst agent | 0.097926 | 84 | 0 | 0.031234 | -0.4683 |
+
+`hybrid_anchor` is intentionally identical to logistic regression because the active anchor is
+logistic-only. The agent rows report the final probability used for scoring; their proposal,
+anchor, applied adjustment, evidence, and trace references (where present) are included in the
+origin-level CSV.
+
+## Regime results
+
+The event counts below add to six within each regime dimension; a forecast origin is counted once
+in each separate dimension.
+
+| regime_dimension | regime | origins | stress_events | observed_event_rate |
+|---|---|---|---|---|
+| momentum_regime | expanding | 35 | 3.0 | 8.6% |
+| momentum_regime | slowing | 43 | 3.0 | 7.0% |
+| momentum_regime | stressed | 6 | 0.0 | 0.0% |
+| financial_regime | restrictive | 43 | 2.0 | 4.7% |
+| financial_regime | supportive | 41 | 4.0 | 9.8% |
+| volatility_regime | elevated | 27 | 2.0 | 7.4% |
+| volatility_regime | normal | 57 | 4.0 | 7.0% |
+
+The full regime-conditional score and calibration diagnostics are in
+[`tables/regime_conditional_brier.csv`](tables/regime_conditional_brier.csv). Regimes with few
+origins/events should not be treated as stable performance rankings.
+
+## Offline hybrid weight sweep
+
+Weight 1.0 is pure logistic and weight 0.0 is pure XGBoost. The best tested point was logistic
+weight **1.0** (mean Brier **0.068921**),
+which is the logistic-only endpoint. No tested blend beat logistic-only or the historical-frequency
+baseline. The sweep is exploratory and does not change the configured anchor.
+
+| logistic_weight | xgboost_weight | mean_brier | delta_vs_logistic | delta_vs_xgboost |
+|---|---|---|---|---|
+| 0.0 | 1.0 | 0.072189 | 0.003267 | 0.000000 |
+| 0.1 | 0.9 | 0.071744 | 0.002822 | -0.000445 |
+| 0.2 | 0.8 | 0.071325 | 0.002404 | -0.000864 |
+| 0.3 | 0.7 | 0.070932 | 0.002011 | -0.001256 |
+| 0.4 | 0.6 | 0.070566 | 0.001645 | -0.001623 |
+| 0.5 | 0.5 | 0.070226 | 0.001305 | -0.001962 |
+| 0.6 | 0.4 | 0.069913 | 0.000991 | -0.002276 |
+| 0.7 | 0.3 | 0.069625 | 0.000704 | -0.002563 |
+| 0.8 | 0.2 | 0.069364 | 0.000443 | -0.002824 |
+| 0.9 | 0.1 | 0.069130 | 0.000208 | -0.003059 |
+| 1.0 | 0.0 | 0.068921 | 0.000000 | -0.003267 |
+
+## Adaptive-agent adjustments
+
+Across 84 origins, the adaptive agent's mean applied adjustment was
++0.00155; it adjusted upward on 26
+origins and downward on 58; it made no adjustment on
+0 origins. 7 proposals were clamped by the ±0.03 adjustment bound. These are descriptive outputs,
+not proof that adaptation improved accuracy.
+
+## Figures and interpretation
+
+### All-model diagnostics
+
+![Six-panel model diagnostics](figures/comparison_diagnostics.png)
+
+The panels show mean Brier, Brier skill against baseline, per-origin Brier, cumulative Brier,
+reliability, and probability distributions. The reliability and histogram panels use five
+probability bins. Because the event is rare, these plots are sensitive to the small number of
+positive outcomes.
+
+### Regime-conditional Brier scores
+
+![Regime-conditional Brier scores](figures/regime_conditional_brier.png)
+
+Each panel compares models within one cutoff-safe regime dimension. The number of origins and
+events for each group is reported in `tables/regime_origin_counts.csv`.
+
+### Hybrid weight sweep
+
+![Offline hybrid weight sweep](figures/hybrid_weight_sweep.png)
+
+This plot varies only the logistic/XGBoost blend weight over the saved forecasts. It is not an
+additional backtest.
+
+### Adaptive-agent probability and adjustment
+
+![Adaptive-agent probability and adjustment](figures/adaptive_agent_adjustments.png)
+
+The upper panel compares the logistic anchor and adaptive final probability; the lower panel shows
+the applied adjustment at each origin.
+
+## Data files
+
+- [`tables/model_comparison.csv`](tables/model_comparison.csv): aggregate model metrics.
+- [`tables/origin_level_predictions.csv`](tables/origin_level_predictions.csv): long-format,
+  84-origin by seven-model results with probabilities, outcomes, scores, agent evidence, and
+  available Langfuse trace references.
+- [`tables/origin_level_probabilities_wide.csv`](tables/origin_level_probabilities_wide.csv):
+  compact one-row-per-origin comparison.
+- [`tables/regime_origin_counts.csv`](tables/regime_origin_counts.csv): cases, events, and event
+  rates by regime.
+- [`tables/regime_conditional_brier.csv`](tables/regime_conditional_brier.csv): conditional model
+  score and calibration metrics.
+- [`tables/hybrid_weight_sweep.csv`](tables/hybrid_weight_sweep.csv): all eleven tested weights.
+- [`tables/calibration_bins.csv`](tables/calibration_bins.csv) and
+  [`tables/probability_distribution_bins.csv`](tables/probability_distribution_bins.csv): data
+  behind the calibration and probability-distribution panels.
+- [`tables/adaptive_agent_adjustments.csv`](tables/adaptive_agent_adjustments.csv): summary of the
+  adaptive agent's anchor, proposal, applied adjustments, and clamping.
+
+## Limitations
+
+This is a development backtest, not a protected holdout or a causal evaluation of adaptation.
+Origins advance monthly while the target horizon is three months, so adjacent outcomes overlap.
+There are only six positive events, which limits calibration and regime-level conclusions.
+Model comparisons should be treated as exploratory pending a larger, untouched evaluation set.
+
+## Rebuilding this package
+
+Run `python reports/compare_all/build_report.py` from the repository root. The script requires the
+saved agent-result YAML files and cached input data. It loads agent artifacts and runs only local
+deterministic predictors; it does not call the LLM services or refresh external data.
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/adaptive_agent_adjustments.png b/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/adaptive_agent_adjustments.png
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index 000000000..93707e22a
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diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/comparison_diagnostics.png b/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/comparison_diagnostics.png
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index 000000000..6a59bc4b8
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diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/hybrid_weight_sweep.png b/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/hybrid_weight_sweep.png
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diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/regime_conditional_brier.png b/implementations/manufacturing_stress_forecasting/reports/compare_all/figures/regime_conditional_brier.png
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index 000000000..e4019cc6f
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diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/run_manifest.json b/implementations/manufacturing_stress_forecasting/reports/compare_all/run_manifest.json
new file mode 100644
index 000000000..f93242f49
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/run_manifest.json
@@ -0,0 +1,23 @@
+{
+  "source_notebook": "implementations/manufacturing_stress_forecasting/manufacturing_stress_workbench_2.ipynb",
+  "cache_spec_id": "manufacturing_stress_smoke_llmp_v3_14b1deebe4",
+  "backtest_window": {
+    "start": "2018-01-01T00:00:00",
+    "end": "2024-12-01T00:00:00"
+  },
+  "stride_months": 1,
+  "horizon_months": 3,
+  "scored_origins": 84,
+  "models": [
+    "historical_frequency",
+    "logistic_c_0_001",
+    "xgb_50_depth2_lr0_03_minchild3_l2_5",
+    "hybrid_anchor",
+    "analyst_agent",
+    "hybrid_agent_adjusted",
+    "adaptive_agent"
+  ],
+  "agent_results_reused_from_cache": true,
+  "deterministic_models_recomputed_from_local_inputs": true,
+  "generated_utc": "2026-10-05T16:25:38.333317+00:00"
+}
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/adaptive_agent_adjustments.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/adaptive_agent_adjustments.csv
new file mode 100644
index 000000000..64bda0500
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/adaptive_agent_adjustments.csv
@@ -0,0 +1,2 @@
+origins,mean_anchor_probability,mean_agent_proposal,mean_final_probability,mean_applied_adjustment,median_applied_adjustment,minimum_applied_adjustment,maximum_applied_adjustment,adjustments_up,adjustments_down,adjustments_unchanged,clamped_proposals
+84,0.07091526242766816,0.0935607142857143,0.07246580972993251,0.0015505473022643366,-0.0025022053080348307,-0.008158040658308746,0.03,26,58,0,7
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/calibration_bins.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/calibration_bins.csv
new file mode 100644
index 000000000..a2829400b
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/calibration_bins.csv
@@ -0,0 +1,15 @@
+model,model_label,probability_bin,origins,mean_probability,observed_event_rate
+historical_frequency,Historical frequency,0.0-0.2,84.0,0.06510478340276714,0.07142857142857142
+logistic_c_0_001,Logistic (C=0.001),0.0-0.2,82.0,0.06668504922865147,0.07317073170731707
+logistic_c_0_001,Logistic (C=0.001),0.2-0.4,2.0,0.24435397869468886,0.0
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),0.0-0.2,84.0,0.07611176019002285,0.07142857142857142
+hybrid_anchor,Hybrid anchor (logistic),0.0-0.2,82.0,0.06668504922865147,0.07317073170731707
+hybrid_anchor,Hybrid anchor (logistic),0.2-0.4,2.0,0.24435397869468886,0.0
+analyst_agent,Analyst agent,0.0-0.2,74.0,0.12195945945945946,0.06756756756756757
+analyst_agent,Analyst agent,0.2-0.4,7.0,0.27714285714285714,0.14285714285714285
+analyst_agent,Analyst agent,0.6-0.8,1.0,0.65,0.0
+analyst_agent,Analyst agent,0.8-1.0,2.0,0.85,0.0
+hybrid_agent_adjusted,Hybrid agent adjusted,0.0-0.2,82.0,0.08119954548853549,0.07317073170731707
+hybrid_agent_adjusted,Hybrid agent adjusted,0.2-0.4,2.0,0.27435395949974006,0.0
+adaptive_agent,Adaptive agent,0.0-0.2,82.0,0.06754170851603476,0.07317073170731707
+adaptive_agent,Adaptive agent,0.2-0.4,2.0,0.27435395949974006,0.0
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/hybrid_weight_sweep.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/hybrid_weight_sweep.csv
new file mode 100644
index 000000000..5fdc59f43
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/hybrid_weight_sweep.csv
@@ -0,0 +1,12 @@
+logistic_weight,xgboost_weight,mean_brier,delta_vs_logistic,delta_vs_xgboost
+0.0,1.0,0.07218861617575986,0.0032674542872497686,0.0
+0.1,0.9,0.07174351376461349,0.002822351876103399,-0.0004451024111463697
+0.2,0.8,0.0713247129051163,0.0024035510166062063,-0.0008639032706435623
+0.3,0.7,0.07093221359726828,0.002011051708758191,-0.0012564025784915778
+0.4,0.6,0.07056601584106952,0.001644853952559422,-0.0016226003346903467
+0.5,0.5,0.07022611963651992,0.0013049577480098301,-0.0019624965392399385
+0.6,0.4,0.06991252498361956,0.000991363095109471,-0.0022760911921402976
+0.7,0.3,0.06962523188236838,0.0007040699938582889,-0.0025633842933914797
+0.8,0.2,0.0693642403327664,0.0004430784442563118,-0.002824375842993457
+0.9,0.1,0.06912955033481365,0.00020838844630355347,-0.003059065840946215
+1.0,0.0,0.0689211618885101,0.0,-0.0032674542872497686
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/model_comparison.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/model_comparison.csv
new file mode 100644
index 000000000..905169828
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/model_comparison.csv
@@ -0,0 +1,8 @@
+model,model_label,description,metric,mean_brier,scored_origins,skipped_origins,delta_vs_baseline,brier_skill_vs_baseline
+historical_frequency,Historical frequency,Expanding historical event frequency; the Brier-score reference baseline.,brier,0.06669266093465771,84,0,0.0,0.0
+logistic_c_0_001,Logistic (C=0.001),"Fit-at-origin logistic regression (C=0.001) using cutoff-safe IPMAN, macroeconomic, and market features.",brier,0.0689211618885101,84,0,0.0022285009538523803,-0.033414485531410465
+hybrid_anchor,Hybrid anchor (logistic),"Deterministic anchor; logistic-only, not a 50/50 blend.",brier,0.0689211618885101,84,0,0.0022285009538523803,-0.033414485531410465
+adaptive_agent,Adaptive agent,Stateful LLM agent using the logistic anchor and persisted strategy; its adjustment is bounded to ±0.03.,brier,0.06920487988057167,84,0,0.002512218945913955,-0.03766859667475719
+hybrid_agent_adjusted,Hybrid agent adjusted,"LLM-proposed probability adjusted from the logistic anchor, with the change bounded to ±0.03.",brier,0.07068946389906912,84,0,0.003996802964411406,-0.05992867743464725
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),"XGBoost with 50 trees, max depth 2, learning rate 0.03, min_child_weight 3, and reg_lambda 5.",brier,0.07218861617575986,84,0,0.005495955241102149,-0.08240719689512499
+analyst_agent,Analyst agent,LLM analyst forecast from cutoff-safe evidence and historical event rate.,brier,0.09792648809523812,84,0,0.031233827160580402,-0.46832480100294416
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/origin_level_predictions.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/origin_level_predictions.csv
new file mode 100644
index 000000000..8868aeb15
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/origin_level_predictions.csv
@@ -0,0 +1,589 @@
+model,model_label,as_of,forecast_date,probability,outcome,brier,anchor_probability,agent_probability,raw_agent_adjustment,agent_adjustment,adjusted_probability,validation_status,rationale,supporting_evidence,countervailing_evidence,direction,langfuse_trace_id,langfuse_trace_url
+historical_frequency,Historical frequency,2018-01-01,2018-04-01,0.06375227686703097,0.0,0.004064352805730572,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-01-01,2018-04-01,0.05827915949664922,0.0,0.003396460431635879,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-01-01,2018-04-01,0.03169894590973854,0.0,0.0010048231717885298,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-01-01,2018-04-01,0.05827915949664922,0.0,0.003396460431635879,0.05827915949664922,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-01-01,2018-04-01,0.05,0.0,0.0025000000000000005,,,,,,,"IPMAN momentum remains positive over three months. Macro indicators, including a positive yield spread and low VIX, suggest a stable environment, outweighing the minor one-month IPMAN decline.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-01-01,2018-04-01,0.058,0.0,0.0033640000000000002,0.05827916180498346,0.058,-0.0002791618049834549,-0.0002791618049834549,0.058,passed,"IPMAN trends remain positive over three and six months, and macroeconomic indicators like low credit spreads and strong equity returns suggest a stable environment, keeping the probability of manufacturing stress near the historical base rate.","[""None are material""]","[""Positive 3-month IPMAN growth of 0.88% and strong 12-month XLI returns of 23.98%.""]",neutral,,
+adaptive_agent,Adaptive agent,2018-01-01,2018-04-01,0.058,0.0,0.0033640000000000002,0.05827916180498346,0.058,-0.0002791618049834549,-0.0002791618049834549,0.058,passed,"The 3-month IPMAN change is positive (+0.88%), indicating no immediate stress. Macro indicators, including a positive yield spread and low VIX, suggest a stable environment, keeping the probability near the statistical anchor.","[""Positive 3-month IPMAN change of 0.88%.""]","[""None are material.""]",neutral,f6d00ad800b001c0e5d40d233f1f5174,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/f6d00ad800b001c0e5d40d233f1f5174
+historical_frequency,Historical frequency,2018-02-01,2018-05-01,0.06363636363636363,0.0,0.004049586776859503,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-02-01,2018-05-01,0.05780021418284348,0.0,0.0033408647595825807,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-02-01,2018-05-01,0.05238844081759453,0.0,0.0027445487312986044,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-02-01,2018-05-01,0.05780021418284348,0.0,0.0033408647595825807,0.05780021418284348,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-02-01,2018-05-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is slightly negative but far from the -2% threshold. Macro indicators like the positive yield spread and strong equity returns suggest a resilient environment, keeping the probability near the historical base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-02-01,2018-05-01,0.065,0.0,0.0042250000000000005,0.05780021226974176,0.065,0.007199787730258242,0.007199787730258242,0.065,passed,"IPMAN trends are slightly negative but remain well above the -2% stress threshold. Macro indicators like low unemployment and positive equity returns suggest resilience, keeping the probability of manufacturing stress low.","[""IPMAN 3-month change is negative at -0.50%""]","[""Strong 12-month equity returns (SPY and XLI) and low unemployment rate (4.0%)""]",neutral,,
+adaptive_agent,Adaptive agent,2018-02-01,2018-05-01,0.055,0.0,0.003025,0.05780021226974176,0.055,-0.00280021226974176,-0.00280021226974176,0.055,passed,The 3-month IPMAN contraction of -0.50% remains well above the -2% stress threshold. Strong equity returns (SPY/XLI) and stable macro indicators suggest low near-term risk of manufacturing stress.,"[""3-month IPMAN contraction of -0.50% is significantly above the -2% stress threshold.""]","[""None are material.""]",down,fc27f92f44d0f81119703df1efa71aef,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/fc27f92f44d0f81119703df1efa71aef
+historical_frequency,Historical frequency,2018-03-01,2018-06-01,0.06352087114337568,0.0,0.004034901070813337,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-03-01,2018-06-01,0.05796100576794664,0.0,0.0033594781896319438,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-03-01,2018-06-01,0.030501529574394226,0.0,0.0009303433063776456,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-03-01,2018-06-01,0.05796100576794664,0.0,0.0033594781896319438,0.05796100576794664,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-03-01,2018-06-01,0.05,0.0,0.0025000000000000005,,,,,,,"IPMAN momentum is positive, and macro indicators like low unemployment and positive equity returns suggest stability. The base rate is low, and no signals currently indicate an imminent contraction in manufacturing activity.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-03-01,2018-06-01,0.058,0.0,0.0033640000000000002,0.05796100932603686,0.058,3.899067396314487e-05,3.899067396314487e-05,0.058,passed,"IPMAN trends remain positive over 1, 3, and 6 months. Macro indicators, including low unemployment and positive equity returns, suggest no immediate manufacturing stress. The statistical anchor remains low, reflecting stable conditions.","[""None are material""]","[""Positive 3-month IPMAN growth of 0.29% and stable macroeconomic indicators like low unemployment and positive XLI returns.""]",neutral,,
+adaptive_agent,Adaptive agent,2018-03-01,2018-06-01,0.055,0.0,0.003025,0.05796100932603686,0.055,-0.002961009326036858,-0.002961009326036858,0.055,passed,"The 3-month IPMAN change is positive (+0.29%), indicating no immediate stress. With stable macro indicators and a healthy distance from the -2% threshold, the probability of stress remains low, slightly below the statistical anchor.","[""None are material""]","[""Positive 3-month IPMAN growth of 0.29% and stable macroeconomic indicators.""]",down,e8c6cef9feee7faad939c1f8f82b3901,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/e8c6cef9feee7faad939c1f8f82b3901
+historical_frequency,Historical frequency,2018-04-01,2018-07-01,0.06340579710144928,0.0,0.004020295106070154,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-04-01,2018-07-01,0.061187908035554274,0.0,0.003743960089767447,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-04-01,2018-07-01,0.030335301533341408,0.0,0.0009202305191187456,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-04-01,2018-07-01,0.061187908035554274,0.0,0.003743960089767447,0.061187908035554274,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-04-01,2018-07-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum remains positive over three months. While recent monthly data shows a slight dip and equity returns are soft, macro indicators like low unemployment and a positive yield spread suggest no immediate manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-04-01,2018-07-01,0.065,0.0,0.0042250000000000005,0.06118790124122692,0.065,0.003812098758773079,0.003812098758773079,0.065,passed,"The 3-month IPMAN trend remains positive, and the trailing base rate is zero. However, recent monthly declines and negative 3-month equity returns (SPY/XLI) suggest a slight increase in near-term downside risk.","[""Negative 1-month IPMAN change and negative 3-month XLI return.""]","[""Positive 3-month and 6-month IPMAN growth trends.""]",up,,
+adaptive_agent,Adaptive agent,2018-04-01,2018-07-01,0.065,0.0,0.0042250000000000005,0.06118790124122692,0.065,0.003812098758773079,0.003812098758773079,0.065,passed,"The 3-month IPMAN change is positive (+0.497%), indicating no immediate stress. While the 1-month change is negative, the overall trend remains well above the -2% threshold, supporting a low probability of stress.","[""3-month IPMAN change is positive at 0.497%""]","[""1-month IPMAN change is negative at -0.054%""]",neutral,8ac6646cb0d6caccd722586eb0e80a19,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/8ac6646cb0d6caccd722586eb0e80a19
+historical_frequency,Historical frequency,2018-05-01,2018-08-01,0.06329113924050633,0.0,0.00400576830636116,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-05-01,2018-08-01,0.059623951120558974,0.0,0.003555015547226806,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-05-01,2018-08-01,0.04841608554124832,0.0,0.0023441173391374748,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-05-01,2018-08-01,0.059623951120558974,0.0,0.003555015547226806,0.059623951120558974,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-05-01,2018-08-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is positive and stable. While equity returns (SPY/XLI) are weak, the yield curve is not inverted and unemployment remains low, suggesting no immediate manufacturing contraction despite recent market volatility.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-05-01,2018-08-01,0.085,0.0,0.007225000000000001,0.05962395510338089,0.085,0.025376044896619113,0.025376044896619113,0.085,passed,"IPMAN trends remain positive, but significant declines in 3-month equity returns (SPY and XLI) and a narrowing yield spread suggest rising macroeconomic headwinds that increase the risk of manufacturing stress over the next quarter.","[""XLI_RETURN_3M of -9.05% indicates significant sector-specific market pessimism.""]","[""IPMAN_change_3m_pct remains positive at 1.57%, indicating current production growth.""]",up,,
+adaptive_agent,Adaptive agent,2018-05-01,2018-08-01,0.055,0.0,0.003025,0.05962395510338089,0.055,-0.004623955103380893,-0.004623955103380893,0.055,passed,"IPMAN shows positive 3-month growth (+1.57%), far from the -2% stress threshold. While equity returns (SPY/XLI) are negative, labor market and credit conditions remain stable, supporting a low probability of near-term manufacturing stress.","[""Positive 3-month IPMAN growth of 1.57%.""]","[""Negative 3-month returns for SPY (-5.79%) and XLI (-9.05%).""]",down,8c835681f2ecc85405cda705d9465621,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/8c835681f2ecc85405cda705d9465621
+historical_frequency,Historical frequency,2018-06-01,2018-09-01,0.0631768953068592,0.0,0.0039913201006138484,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-06-01,2018-09-01,0.06336615679077409,0.0,0.004015269826432966,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-06-01,2018-09-01,0.039699967950582504,0.0,0.001576087455277278,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-06-01,2018-09-01,0.06336615679077409,0.0,0.004015269826432966,0.06336615679077409,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-06-01,2018-09-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is slightly negative but well above the -2% stress threshold. Macro conditions remain supportive with a positive yield curve and low unemployment, suggesting low near-term risk of a manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-06-01,2018-09-01,0.075,0.0,0.005625,0.0633661684100344,0.075,0.011633831589965601,0.011633831589965601,0.075,passed,"IPMAN trends are stable, but the negative 3-month XLI return suggests sector-specific weakness. Low unemployment and positive yield spread provide a buffer, keeping the overall stress probability near the historical base rate.","[""XLI 3-month return is negative at -2.49 percent.""]","[""Unemployment rate remains low at 3.8 percent and the yield spread is positive.""]",neutral,,
+adaptive_agent,Adaptive agent,2018-06-01,2018-09-01,0.063,0.0,0.003969,0.0633661684100344,0.063,-0.0003661684100343954,-0.0003661684100343954,0.063,passed,"The 3-month IPMAN contraction of -0.12% remains well above the -2% stress threshold. Macro indicators, including a positive yield spread and stable credit conditions, suggest no immediate systemic manufacturing stress.","[""3-month IPMAN contraction of -0.12% is significantly above the -2% stress threshold.""]","[""None are material.""]",neutral,b7912980aad76af7543b6cfd9b2e2dc8,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/b7912980aad76af7543b6cfd9b2e2dc8
+historical_frequency,Historical frequency,2018-07-01,2018-10-01,0.06306306306306306,0.0,0.003976949922895868,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-07-01,2018-10-01,0.058598132173098255,0.0,0.003433741094175893,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-07-01,2018-10-01,0.0340878926217556,0.0,0.0011619844233923399,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-07-01,2018-10-01,0.058598132173098255,0.0,0.003433741094175893,0.058598132173098255,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-07-01,2018-10-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is positive and stable. Macro indicators, including low unemployment and a positive yield curve, suggest resilience. While XLI returns are slightly negative, the overall environment lacks clear signals of imminent manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-07-01,2018-10-01,0.065,0.0,0.0042250000000000005,0.05859814303950494,0.065,0.006401856960495063,0.006401856960495063,0.065,passed,"IPMAN trends remain positive, but the negative 3-month XLI return suggests emerging sector-specific weakness. The low base rate and stable macro environment keep the overall probability of manufacturing stress low.","[""XLI_RETURN_3M is negative at -2.49 percent, indicating potential sector-specific headwinds.""]","[""IPMAN 3-month change is positive at 0.64 percent, showing recent production growth.""]",up,,
+adaptive_agent,Adaptive agent,2018-07-01,2018-10-01,0.055,0.0,0.003025,0.05859814303950494,0.055,-0.003598143039504939,-0.003598143039504939,0.055,passed,"IPMAN trends are positive (3-month change +0.64%), remaining well above the -2% stress threshold. Macro indicators like low unemployment and stable credit spreads suggest a low risk of near-term manufacturing stress.","[""XLI_RETURN_3M is negative at -2.49%, suggesting some sector-specific weakness.""]","[""IPMAN 3-month change is positive (+0.64%), indicating growth rather than contraction.""]",down,c7a5fa2ae15c75ca69c9279977c4bfb2,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/c7a5fa2ae15c75ca69c9279977c4bfb2
+historical_frequency,Historical frequency,2018-08-01,2018-11-01,0.06294964028776978,0.0,0.003962657212359609,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-08-01,2018-11-01,0.05714126279903641,0.0,0.0032651239142685424,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-08-01,2018-11-01,0.029688911512494087,0.0,0.000881431466796704,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-08-01,2018-11-01,0.05714126279903641,0.0,0.0032651239142685424,0.05714126279903641,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-08-01,2018-11-01,0.12,0.0,0.0144,,,,,,,"The 3-month IPMAN decline (-4.05%) exceeds the stress threshold, but recent 1-month momentum is positive. Favorable macro conditions (low unemployment, positive yield spread, and strong equity returns) suggest the current contraction is likely transitory.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-08-01,2018-11-01,0.065,0.0,0.0042250000000000005,0.05714125736879296,0.065,0.00785874263120704,0.00785874263120704,0.065,passed,"The 3-month IPMAN decline of -4.05% exceeds the stress threshold, indicating current weakness. However, positive 1-month momentum and strong equity returns suggest potential stabilization, keeping the probability of future stress relatively low.","[""IPMAN 3-month percentage change of -4.05% exceeds the -2% stress threshold.""]","[""Positive 1-month IPMAN growth and strong 3-month XLI returns suggest near-term recovery.""]",up,,
+adaptive_agent,Adaptive agent,2018-08-01,2018-11-01,0.055,0.0,0.003025,0.05714125736879296,0.055,-0.002141257368792962,-0.002141257368792962,0.055,passed,"The 3-month IPMAN contraction of -4.05% exceeds the -2% threshold, indicating current stress. However, the 1-month growth of 5.91% and strong equity returns suggest a potential recovery, warranting a slight downward adjustment from the anchor.","[""3-month IPMAN change of -4.05% is below the -2% stress threshold.""]","[""1-month IPMAN growth of 5.91% and positive 3-month SPY/XLI returns.""]",down,214c7b50864c3b84898bdb91c71a513e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/214c7b50864c3b84898bdb91c71a513e
+historical_frequency,Historical frequency,2018-09-01,2018-12-01,0.06283662477558348,0.0,0.003948441413187472,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-09-01,2018-12-01,0.05571478817502238,0.0,0.0031041376213876135,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-09-01,2018-12-01,0.02956486865878105,0.0,0.0008740814588109741,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-09-01,2018-12-01,0.05571478817502238,0.0,0.0031041376213876135,0.05571478817502238,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-09-01,2018-12-01,0.05,0.0,0.0025000000000000005,,,,,,,"Positive IPMAN momentum and healthy macro indicators (low unemployment, positive yield spread, strong equity returns) suggest low near-term stress risk. The trailing base rate is zero, supporting a cautious, low-probability outlook.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-09-01,2018-12-01,0.0557,0.0,0.00310249,0.05571478287466969,0.0557,-1.478287466968875e-05,-1.478287466968875e-05,0.0557,passed,"IPMAN shows positive momentum over three months, and macroeconomic indicators like low unemployment and positive equity returns suggest a stable environment. No evidence of manufacturing stress is present.","[""None are material""]","[""IPMAN 3-month change is positive at 1.00%, indicating growth rather than contraction.""]",neutral,,
+adaptive_agent,Adaptive agent,2018-09-01,2018-12-01,0.055,0.0,0.003025,0.05571478287466969,0.055,-0.000714782874669688,-0.000714782874669688,0.055,passed,"IPMAN shows positive 3-month growth (+1.00%), remaining well above the -2% stress threshold. Macro indicators, including low unemployment and positive equity returns, suggest a stable environment, justifying a slight downward adjustment from the anchor.","[""IPMAN 3-month change is positive at 1.00%""]","[""None are material""]",down,5e62e9f1377ec2cd7441c27d27528b68,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/5e62e9f1377ec2cd7441c27d27528b68
+historical_frequency,Historical frequency,2018-10-01,2019-01-01,0.06272401433691756,0.0,0.00393430197453784,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-10-01,2019-01-01,0.055697593735500046,0.0,0.0031022219479248142,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-10-01,2019-01-01,0.02940630353987217,0.0,0.0008647306878790985,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-10-01,2019-01-01,0.055697593735500046,0.0,0.0031022219479248142,0.055697593735500046,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-10-01,2019-01-01,0.04,0.0,0.0016,,,,,,,"IPMAN momentum is strongly positive over 3 months. Macro indicators, including low unemployment and positive equity returns, suggest a stable environment. The lack of recent stress and favorable momentum support a low probability of near-term decline.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-10-01,2019-01-01,0.055,0.0,0.003025,0.05569760105796562,0.055,-0.0006976010579656175,-0.0006976010579656175,0.055,passed,"IPMAN shows positive momentum over three and six months. Macro indicators, including low unemployment and positive equity returns, suggest a stable environment. No evidence of imminent manufacturing stress is present.","[""none are material""]","[""IPMAN 3-month change is positive at 0.33 percent, well above the stress threshold.""]",down,,
+adaptive_agent,Adaptive agent,2018-10-01,2019-01-01,0.05,0.0,0.0025000000000000005,0.05569760105796562,0.05,-0.005697601057965615,-0.005697601057965615,0.05,passed,"IPMAN shows positive 3-month growth (0.33%), far from the -2% stress threshold. Strong equity returns and stable macro indicators suggest low near-term risk of manufacturing contraction.","[""Positive 3-month IPMAN growth of 0.33%""]","[""None are material""]",down,c273ba3dd97a1e7ebc7b6e2ca7652c7e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/c273ba3dd97a1e7ebc7b6e2ca7652c7e
+historical_frequency,Historical frequency,2018-11-01,2019-02-01,0.0626118067978533,0.0,0.003920238350491709,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-11-01,2019-02-01,0.06453510920404289,0.0,0.004164780319977741,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-11-01,2019-02-01,0.04872920736670494,0.0,0.002374535650587331,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-11-01,2019-02-01,0.06453510920404289,0.0,0.004164780319977741,0.06453510920404289,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-11-01,2019-02-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is slightly negative but well above the -2% stress threshold. Macro signals show moderate headwinds (XLI weakness, rising VIX), but the yield curve remains positive and unemployment is low, limiting immediate stress risk.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-11-01,2019-02-01,0.09453510993897833,0.0,0.00893688701117472,0.06453510993897833,0.12,0.05546489006102166,0.03,0.09453510993897833,clamped,"IPMAN trends remain stable, but negative XLI and SPY returns, combined with a narrowing yield spread, suggest rising downside risks to manufacturing activity over the next three months.","[""XLI_RETURN_3M of -8.71% indicates significant weakness in industrial sector equity performance.""]","[""IPMAN 3-month change of -0.11% remains well above the -2% stress threshold.""]",up,,
+adaptive_agent,Adaptive agent,2018-11-01,2019-02-01,0.06,0.0,0.0036,0.06453510993897833,0.06,-0.004535109938978335,-0.004535109938978335,0.06,passed,"The 3-month IPMAN contraction of -0.11% remains far above the -2% stress threshold. Despite negative equity returns, macro indicators like low unemployment and stable credit spreads suggest no immediate systemic manufacturing stress.","[""Negative 3-month equity returns (SPY -3.37%, XLI -8.71%) suggest potential industrial headwinds.""]","[""3-month IPMAN contraction of -0.11% is significantly above the -2% stress threshold.""]",down,0df2d9cf6846f0919870c701d4a47f7e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/0df2d9cf6846f0919870c701d4a47f7e
+historical_frequency,Historical frequency,2018-12-01,2019-03-01,0.0625,0.0,0.00390625,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2018-12-01,2019-03-01,0.06427457203620024,0.0,0.004131220610436694,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2018-12-01,2019-03-01,0.08102866262197495,0.0,0.00656564416630584,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2018-12-01,2019-03-01,0.06427457203620024,0.0,0.004131220610436694,0.06427457203620024,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2018-12-01,2019-03-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is negative but remains well above the -2% stress threshold. Macro signals like negative XLI returns and tightening credit spreads suggest caution, but the low base rate and stable unemployment limit immediate stress probability.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2018-12-01,2019-03-01,0.09427457278729906,0.0,0.008887695074227748,0.06427457278729906,0.12,0.055725427212700934,0.03,0.09427457278729906,clamped,"While the IPMAN three-month decline is modest, the significant negative returns in XLI and SPY, combined with a narrowing yield spread, suggest rising downside risks to manufacturing activity over the next quarter.","[""XLI_RETURN_3M of -8.71% indicates significant sector-specific weakness.""]","[""UNRATE at 3.8% remains historically low, supporting broader economic stability.""]",up,,
+adaptive_agent,Adaptive agent,2018-12-01,2019-03-01,0.068,0.0,0.0046240000000000005,0.06427457278729906,0.068,0.0037254272127009436,0.0037254272127009436,0.068,passed,"The 3-month IPMAN contraction of -0.59% is well above the -2% threshold. However, negative 3-month returns in SPY and XLI, combined with a narrowing yield spread, suggest rising downside risks to manufacturing activity.","[""Negative 3-month returns in SPY (-3.37%) and XLI (-8.71%) indicate weakening market sentiment.""]","[""Current 3-month IPMAN contraction of -0.59% remains significantly above the -2% stress threshold.""]",up,539cdca80b5d13687a79529fcca6d715,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/539cdca80b5d13687a79529fcca6d715
+historical_frequency,Historical frequency,2019-01-01,2019-04-01,0.062388591800356503,0.0,0.0038923363868315105,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-01-01,2019-04-01,0.06827462369664905,0.0,0.004661424240919032,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-01-01,2019-04-01,0.0733458548784256,0.0,0.005379614427847068,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-01-01,2019-04-01,0.06827462369664905,0.0,0.004661424240919032,0.06827462369664905,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-01-01,2019-04-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is slightly negative but far from the -2% threshold. Weak equity returns and XLI performance suggest manufacturing headwinds, but low unemployment and a positive yield spread provide resilience against immediate stress.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-01-01,2019-04-01,0.09827461237705169,0.0,0.009657899437859762,0.0682746123770517,0.15,0.0817253876229483,0.03,0.09827461237705169,clamped,"While IPMAN remains stable, significant declines in XLI and SPY returns, combined with a narrowing yield spread, suggest heightened downside risks to manufacturing activity over the next three months.","[""XLI_RETURN_3M of -17.34% and SPY_RETURN_3M of -13.53% indicate significant market-based stress in industrial sectors.""]","[""IPMAN 3-month change of -0.27% is well above the -2% stress threshold.""]",up,,
+adaptive_agent,Adaptive agent,2019-01-01,2019-04-01,0.065,0.0,0.0042250000000000005,0.0682746123770517,0.065,-0.0032746123770516916,-0.0032746123770516916,0.065,passed,"The 3-month IPMAN contraction of -0.27% remains well above the -2% stress threshold. While equity market returns are negative, the current IPMAN trend and macro stability suggest a low probability of reaching the stress threshold.","[""3-month IPMAN contraction of -0.27% is significantly above the -2% stress threshold.""]","[""Negative 3-month and 12-month SPY and XLI returns indicate potential headwinds for the manufacturing sector.""]",neutral,008286205a0f9b25652ce58e6ac1855f,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/008286205a0f9b25652ce58e6ac1855f
+historical_frequency,Historical frequency,2019-02-01,2019-05-01,0.06227758007117438,0.0,0.003878496979521536,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-02-01,2019-05-01,0.06430843461757971,0.0,0.004135574762963524,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-02-01,2019-05-01,0.11629296839237213,0.0,0.013524054497509264,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-02-01,2019-05-01,0.06430843461757971,0.0,0.004135574762963524,0.06430843461757971,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-02-01,2019-05-01,0.12,0.0,0.0144,,,,,,,IPMAN momentum is slightly negative but stable. Macro signals are mixed: low unemployment and positive XLI returns offset the narrow yield spread and negative 12-month equity returns. Stress risk remains low given the current stable trend.,[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-02-01,2019-05-01,0.085,0.0,0.007225000000000001,0.06430844072951633,0.085,0.020691559270483675,0.020691559270483675,0.085,passed,"IPMAN trends are slightly negative, and the 12-month XLI return is significantly negative, suggesting underlying weakness. However, the 3-month IPMAN decline remains well above the -2% stress threshold, keeping the near-term risk relatively low.","[""XLI 12-month return is negative at -8.26%, indicating industrial sector weakness.""]","[""3-month IPMAN change of -0.77% is well above the -2% stress threshold.""]",up,,
+adaptive_agent,Adaptive agent,2019-02-01,2019-05-01,0.064,0.0,0.004096,0.06430844072951633,0.064,-0.0003084407295163294,-0.0003084407295163294,0.064,passed,"The 3-month IPMAN contraction of -0.77% remains well above the -2% stress threshold. Macro indicators, including a positive yield spread and stable credit spreads, suggest no immediate systemic manufacturing stress.","[""3-month trailing IPMAN contraction of -0.77% is significantly above the -2% stress threshold.""]","[""None are material.""]",neutral,16e0c588bb93b1cb103178e9c2b803c8,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/16e0c588bb93b1cb103178e9c2b803c8
+historical_frequency,Historical frequency,2019-03-01,2019-06-01,0.06216696269982238,0.0,0.003864731251321107,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-03-01,2019-06-01,0.06206808432780956,0.0,0.0038524470921240785,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-03-01,2019-06-01,0.10655960440635681,0.0,0.011354949291239258,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-03-01,2019-06-01,0.06206808432780956,0.0,0.0038524470921240785,0.06206808432780956,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-03-01,2019-06-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is negative but remains above the -2% threshold. Macro signals like low unemployment and positive yield spread suggest resilience, though tightening conditions warrant caution against a potential acceleration in manufacturing decline.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-03-01,2019-06-01,0.085,0.0,0.007225000000000001,0.06206809203044091,0.085,0.022931907969559094,0.022931907969559094,0.085,passed,"The 3-month IPMAN decline is accelerating toward the -2% threshold. While macro indicators like low unemployment and positive equity returns provide some buffer, the downward momentum in manufacturing output warrants a slight increase in stress probability.","[""IPMAN 3-month decline of -1.1% indicates negative momentum approaching the stress threshold.""]","[""Low unemployment rate (3.8%) and positive 3-month XLI returns suggest underlying economic resilience.""]",up,,
+adaptive_agent,Adaptive agent,2019-03-01,2019-06-01,0.062,0.0,0.0038439999999999998,0.06206809203044091,0.062,-6.809203044091244e-05,-6.809203044091244e-05,0.062,passed,"The 3-month IPMAN contraction of -1.1% remains well above the -2% stress threshold. Macroeconomic indicators, including stable credit spreads and positive equity returns, suggest no immediate systemic manufacturing stress.","[""3-month IPMAN contraction of -1.1% is significantly above the -2% stress threshold.""]","[""None are material.""]",neutral,d7e741bf13795c2004aa3214d5a205e0,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/d7e741bf13795c2004aa3214d5a205e0
+historical_frequency,Historical frequency,2019-04-01,2019-07-01,0.06205673758865248,0.0,0.003851038680146874,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-04-01,2019-07-01,0.058074651480429305,0.0,0.00337266514457333,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-04-01,2019-07-01,0.07374972105026245,0.0,0.0054390213549915245,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-04-01,2019-07-01,0.058074651480429305,0.0,0.00337266514457333,0.058074651480429305,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-04-01,2019-07-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative and approaching the threshold, but macro conditions remain supportive. Low unemployment, stable credit spreads, and positive equity returns suggest the manufacturing slowdown is not yet systemic or indicative of imminent stress.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-04-01,2019-07-01,0.08,0.0,0.0064,0.05807465748588282,0.08,0.02192534251411718,0.02192534251411718,0.08,passed,"IPMAN is trending downward, nearing the stress threshold of -2%. While macro indicators like low unemployment and positive equity returns provide support, the momentum in manufacturing output suggests an increased risk of hitting the stress threshold.","[""IPMAN 3-month change is -1.69%, approaching the -2% stress threshold.""]","[""Low unemployment rate (3.8%) and strong 3-month equity returns (SPY +13.5%) indicate broader economic resilience.""]",up,,
+adaptive_agent,Adaptive agent,2019-04-01,2019-07-01,0.065,0.0,0.0042250000000000005,0.05807465748588282,0.065,0.00692534251411718,0.00692534251411718,0.065,passed,"The 3-month IPMAN contraction of -1.69% is approaching the -2% threshold. While macro indicators remain stable, the narrowing gap to the stress threshold warrants a slight upward adjustment from the statistical anchor.","[""3-month IPMAN contraction of -1.69% is nearing the -2% stress threshold.""]","[""Stable macroeconomic environment, including low unemployment and positive equity market returns.""]",up,7617a719a99b48cecf3c6d6224142659,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/7617a719a99b48cecf3c6d6224142659
+historical_frequency,Historical frequency,2019-05-01,2019-08-01,0.061946902654867256,0.0,0.0038374187485316,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-05-01,2019-08-01,0.059575988727502575,0.0,0.003549298432859514,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-05-01,2019-08-01,0.08421660214662552,0.0,0.00709243607712301,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-05-01,2019-08-01,0.059575988727502575,0.0,0.003549298432859514,0.059575988727502575,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-05-01,2019-08-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative and approaching the threshold, but macro conditions remain supportive. Low unemployment, positive equity returns, and a non-inverted yield curve suggest resilience, keeping the probability of a sharp decline relatively low.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-05-01,2019-08-01,0.085,0.0,0.007225000000000001,0.05957599448343381,0.085,0.0254240055165662,0.0254240055165662,0.085,passed,"IPMAN is trending downward, with a 3-month decline of 1.46% approaching the 2% stress threshold. While macro indicators remain stable, the momentum suggests an increased risk of hitting the threshold within three months.","[""IPMAN 3-month decline of 1.46% is approaching the 2% stress threshold.""]","[""Low VIX and positive equity returns (SPY/XLI) suggest resilient market sentiment.""]",up,,
+adaptive_agent,Adaptive agent,2019-05-01,2019-08-01,0.065,0.0,0.0042250000000000005,0.05957599448343381,0.065,0.005424005516566195,0.005424005516566195,0.065,passed,"The 3-month IPMAN contraction of -1.46% is approaching the -2% threshold. While macro signals remain stable, the narrowing gap to the stress threshold warrants a slight upward adjustment from the anchor.","[""3-month IPMAN contraction of -1.46% is trending toward the -2% threshold.""]","[""Stable macroeconomic environment with low unemployment (3.7%) and positive equity market returns.""]",up,992903133b6da9cde56c8b203d6d0e26,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/992903133b6da9cde56c8b203d6d0e26
+historical_frequency,Historical frequency,2019-06-01,2019-09-01,0.061837455830388695,0.0,0.0038238709435752727,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-06-01,2019-09-01,0.057075291243421276,0.0,0.0032575888705213612,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-06-01,2019-09-01,0.07815449684858322,0.0,0.006108125377655205,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-06-01,2019-09-01,0.057075291243421276,0.0,0.0032575888705213612,0.057075291243421276,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-06-01,2019-09-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but decelerating, remaining above the -2% threshold. Macro indicators like low unemployment and positive equity returns suggest resilience, offsetting the risk of imminent manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-06-01,2019-09-01,0.065,0.0,0.0042250000000000005,0.05707529679716385,0.065,0.007924703202836149,0.007924703202836149,0.065,passed,"IPMAN is showing a slight downward trend over three months, though it remains above the stress threshold. Macro indicators like low unemployment and positive equity returns suggest resilience, keeping the probability of stress low.","[""IPMAN 3-month percentage change is negative at -0.89 percent.""]","[""Unemployment rate remains low at 3.6 percent and equity markets show positive 3-month returns.""]",up,,
+adaptive_agent,Adaptive agent,2019-06-01,2019-09-01,0.055,0.0,0.003025,0.05707529679716385,0.055,-0.002075296797163853,-0.002075296797163853,0.055,passed,"The 3-month IPMAN contraction of -0.89% remains well above the -2% stress threshold. Stable macro indicators, including positive equity returns and low unemployment, support a low probability of imminent manufacturing stress.","[""3-month IPMAN contraction of -0.89% is significantly above the -2% stress threshold.""]","[""None are material; macro signals like low unemployment and positive equity returns suggest stability.""]",down,bbffd0ec8923ea457561a000912a4773,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/bbffd0ec8923ea457561a000912a4773
+historical_frequency,Historical frequency,2019-07-01,2019-10-01,0.06172839506172839,0.0,0.0038103947568968143,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-07-01,2019-10-01,0.057547897883679304,0.0,0.003311760550830381,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-07-01,2019-10-01,0.030570708215236664,0.0,0.0009345682007811384,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-07-01,2019-10-01,0.057547897883679304,0.0,0.003311760550830381,0.057547897883679304,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-07-01,2019-10-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is slightly negative but well above the -2% stress threshold. Macro indicators like low unemployment and positive equity returns suggest resilience, offsetting the modest contraction in manufacturing output.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-07-01,2019-10-01,0.065,0.0,0.0042250000000000005,0.057547896840822135,0.065,0.007452103159177867,0.007452103159177867,0.065,passed,"IPMAN shows slight recent recovery, but the 6-month trend remains weak. Macro indicators like low unemployment and positive equity returns suggest resilience, keeping the probability of stress low despite the negative 3-month IPMAN change.","[""IPMAN 3-month change is negative at -0.27 percent.""]","[""Low unemployment rate (3.6%) and positive 3-month SPY/XLI returns indicate underlying economic stability.""]",neutral,,
+adaptive_agent,Adaptive agent,2019-07-01,2019-10-01,0.055,0.0,0.003025,0.057547896840822135,0.055,-0.002547896840822135,-0.002547896840822135,0.055,passed,"The 3-month IPMAN contraction of -0.27% remains well above the -2% stress threshold. Recent 1-month growth of 0.38% and stable macro indicators, including low unemployment and positive equity returns, support a slight downward adjustment from the anchor.","[""3-month IPMAN contraction of -0.27% is significantly above the -2% stress threshold.""]","[""None are material.""]",down,11881dc7efe79cb5806bd480e7bd5e91,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/11881dc7efe79cb5806bd480e7bd5e91
+historical_frequency,Historical frequency,2019-08-01,2019-11-01,0.061619718309859156,0.0,0.0037969896845863917,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-08-01,2019-11-01,0.06051264054082875,0.0,0.003661779665223551,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-08-01,2019-11-01,0.03667762875556946,0.0,0.0013452484511313756,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-08-01,2019-11-01,0.06051264054082875,0.0,0.003661779665223551,0.06051264054082875,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-08-01,2019-11-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is negative but shallow, remaining well above the -2% threshold. Macro indicators like low unemployment and stable credit spreads suggest resilience, offsetting the slight contraction in manufacturing activity.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-08-01,2019-11-01,0.075,0.0,0.005625,0.06051265272350754,0.075,0.014487347276492459,0.014487347276492459,0.075,passed,"IPMAN trends are slightly negative, and the yield curve spread is extremely narrow, signaling potential economic headwinds. While current stress is absent, these factors warrant a modest upward adjustment from the statistical anchor.","[""Yield curve spread at 0.13 indicates potential future economic contraction.""]","[""Low unemployment rate (3.7%) and stable initial jobless claims suggest continued labor market resilience.""]",up,,
+adaptive_agent,Adaptive agent,2019-08-01,2019-11-01,0.0605,0.0,0.00366025,0.06051265272350754,0.0605,-1.2652723507540486e-05,-1.2652723507540486e-05,0.0605,passed,"The current 3-month IPMAN contraction of -0.34% remains well above the -2% stress threshold. Macroeconomic indicators, including a positive yield spread and stable credit spreads, suggest no immediate systemic manufacturing stress.","[""3-month trailing IPMAN contraction of -0.34% is significantly above the -2% stress threshold.""]","[""None are material.""]",neutral,15bc0fe22c8d86a66d2d6a561c1c25d0,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/15bc0fe22c8d86a66d2d6a561c1c25d0
+historical_frequency,Historical frequency,2019-09-01,2019-12-01,0.061511423550087874,0.0,0.003783655227158305,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-09-01,2019-12-01,0.05603117750706408,0.0,0.0031394928528281235,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-09-01,2019-12-01,0.027922233566641808,0.0,0.0007796511273500985,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-09-01,2019-12-01,0.05603117750706408,0.0,0.0031394928528281235,0.05603117750706408,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-09-01,2019-12-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is positive over 1-3 months, and macro indicators like low unemployment and stable credit spreads suggest resilience. While the 6-month trend is slightly negative, current conditions do not signal imminent manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-09-01,2019-12-01,0.056,0.0,0.0031360000000000003,0.056031188892159384,0.056,-3.118889215938253e-05,-3.118889215938253e-05,0.056,passed,"IPMAN shows positive short-term momentum, and macroeconomic indicators like low unemployment and stable credit spreads suggest no immediate manufacturing stress. The statistical anchor remains low, reflecting current stability.","[""None are material""]","[""Positive 3-month IPMAN growth of 0.32% and low unemployment rate of 3.6%.""]",neutral,,
+adaptive_agent,Adaptive agent,2019-09-01,2019-12-01,0.056,0.0,0.0031360000000000003,0.056031188892159384,0.056,-3.118889215938253e-05,-3.118889215938253e-05,0.056,passed,"The 3-month IPMAN change is positive (+0.32%), well above the -2% stress threshold. Macro indicators like low unemployment (3.6%) and stable credit spreads (2.13) suggest a resilient manufacturing environment, keeping stress risk low.","[""Positive 3-month IPMAN growth of 0.32%.""]","[""None are material.""]",neutral,526e5154631bcc44849263accb34dcfd,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/526e5154631bcc44849263accb34dcfd
+historical_frequency,Historical frequency,2019-10-01,2020-01-01,0.06140350877192982,0.0,0.0037703908895044625,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-10-01,2020-01-01,0.06079020629582093,0.0,0.0036954491814884666,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-10-01,2020-01-01,0.10266361385583878,0.0,0.010539817609940771,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-10-01,2020-01-01,0.06079020629582093,0.0,0.0036954491814884666,0.06079020629582093,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-10-01,2020-01-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is negative but mild, remaining well above the -2% stress threshold. Macro indicators like low unemployment and stable equity returns suggest resilience, offsetting the slight contraction in manufacturing activity.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-10-01,2020-01-01,0.085,0.0,0.007225000000000001,0.060790207938376556,0.085,0.02420979206162345,0.02420979206162345,0.085,passed,"IPMAN trends show persistent weakness, with a three-month decline approaching the threshold. While macro indicators like low unemployment and positive equity returns provide some buffer, the downward momentum in manufacturing output increases near-term stress risk.","[""IPMAN 3-month decline of -0.87% indicates weakening momentum.""]","[""Low unemployment rate (3.5%) and positive 3-month SPY returns suggest broader economic resilience.""]",up,,
+adaptive_agent,Adaptive agent,2019-10-01,2020-01-01,0.061,0.0,0.003721,0.060790207938376556,0.061,0.00020979206162344277,0.00020979206162344277,0.061,passed,"The 3-month IPMAN contraction of -0.87% remains well above the -2% stress threshold. Macro indicators, including a positive yield spread and low unemployment, suggest continued stability, keeping the probability near the historical base rate.","[""3-month IPMAN contraction of -0.87% is significantly above the -2% stress threshold.""]","[""None are material.""]",neutral,aef2f5c1bd0ad9f1afcf33fa542b7462,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/aef2f5c1bd0ad9f1afcf33fa542b7462
+historical_frequency,Historical frequency,2019-11-01,2020-02-01,0.06129597197898424,0.0,0.003757196180848421,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-11-01,2020-02-01,0.05884762711069493,0.0,0.003463043216559397,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-11-01,2020-02-01,0.08151677250862122,0.0,0.006644984200222304,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-11-01,2020-02-01,0.05884762711069493,0.0,0.003463043216559397,0.05884762711069493,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-11-01,2020-02-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is negative but remains well above the -2% stress threshold. Macro indicators like low unemployment and positive equity returns suggest resilience, though the narrowing yield spread warrants caution regarding future manufacturing activity.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-11-01,2020-02-01,0.075,0.0,0.005625,0.05884760521945527,0.075,0.016152394780544727,0.016152394780544727,0.075,passed,"IPMAN trends are negative but remain above the -2% stress threshold. Macro indicators like low unemployment and positive equity returns suggest resilience, though the narrowing yield spread warrants caution regarding future manufacturing momentum.","[""IPMAN 3-month change is negative at -0.98%.""]","[""Unemployment rate remains low at 3.6% and equity returns are positive.""]",up,,
+adaptive_agent,Adaptive agent,2019-11-01,2020-02-01,0.065,0.0,0.0042250000000000005,0.05884760521945527,0.065,0.0061523947805447315,0.0061523947805447315,0.065,passed,"The 3-month IPMAN contraction of -0.98% remains well above the -2% stress threshold. Stable macro indicators, including low unemployment and positive equity returns, suggest limited risk of imminent manufacturing stress.","[""3-month IPMAN contraction of -0.98% is significantly above the -2% stress threshold.""]","[""None are material; macro indicators remain stable.""]",neutral,acfbe88090c89386c78da6ec4996e28b,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/acfbe88090c89386c78da6ec4996e28b
+historical_frequency,Historical frequency,2019-12-01,2020-03-01,0.06118881118881119,1.0,0.8813664482370777,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2019-12-01,2020-03-01,0.05487326237415406,1.0,0.8932645501752747,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2019-12-01,2020-03-01,0.0713452696800232,1.0,0.8623996081456689,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2019-12-01,2020-03-01,0.05487326237415406,1.0,0.8932645501752747,0.05487326237415406,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2019-12-01,2020-03-01,0.12,1.0,0.7744,,,,,,,"IPMAN momentum is negative but improving, and macro conditions remain stable with low unemployment and positive equity returns. The yield curve is narrow but not inverted, suggesting low immediate risk of manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2019-12-01,2020-03-01,0.065,1.0,0.8742250000000001,0.054873241784838,0.065,0.010126758215161999,0.010126758215161999,0.065,passed,"IPMAN shows a slight three-month decline, though recent monthly growth is positive. Macro indicators like low unemployment and stable credit spreads suggest limited immediate stress risk, keeping the probability near the historical base rate.","[""IPMAN 3-month percentage change is negative at -0.81 percent.""]","[""Low unemployment rate (3.6%) and positive 1-month IPMAN growth (0.83%).""]",neutral,,
+adaptive_agent,Adaptive agent,2019-12-01,2020-03-01,0.052,1.0,0.898704,0.054873241784838,0.052,-0.0028732417848380057,-0.0028732417848380057,0.052,passed,"The 3-month IPMAN contraction of -0.81% remains well above the -2% stress threshold. Recent 1-month growth of 0.83% suggests stabilization, and stable macro indicators like low unemployment and positive equity returns support a low-stress outlook.","[""3-month trailing IPMAN contraction of -0.81% is significantly above the -2% stress threshold.""]","[""None are material; current macro signals and recent IPMAN growth indicate stability.""]",down,20d555bcffbff26b52f68ead9141f2ce,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/20d555bcffbff26b52f68ead9141f2ce
+historical_frequency,Historical frequency,2020-01-01,2020-04-01,0.06108202443280977,1.0,0.8815669648431909,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-01-01,2020-04-01,0.05236029479277658,1.0,0.8980210108852332,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-01-01,2020-04-01,0.028507640585303307,1.0,0.9437974044011342,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-01-01,2020-04-01,0.05236029479277658,1.0,0.8980210108852332,0.05236029479277658,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-01-01,2020-04-01,0.08,1.0,0.8464,,,,,,,"Positive short-term IPMAN momentum and strong equity returns suggest resilience. While the 6-month trend is slightly negative, the 3-month growth is robust, and macro indicators like low unemployment and stable credit spreads mitigate immediate stress risks.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-01-01,2020-04-01,0.052,1.0,0.898704,0.05236029262447333,0.052,-0.0003602926244733323,-0.0003602926244733323,0.052,passed,"IPMAN shows positive short-term momentum, and macro indicators like low unemployment and strong equity returns suggest a stable environment. No immediate signs of manufacturing stress are present.","[""None are material""]","[""Positive 1-month and 3-month IPMAN growth rates"", ""Low unemployment rate (3.6%) and strong 12-month equity returns""]",neutral,,
+adaptive_agent,Adaptive agent,2020-01-01,2020-04-01,0.045,1.0,0.912025,0.05236029262447333,0.045,-0.007360292624473332,-0.007360292624473332,0.045,passed,"The 3-month IPMAN change is positive (+5.43%), far from the -2% stress threshold. Strong equity returns and stable macro indicators suggest low near-term risk of manufacturing contraction.","[""none are material""]","[""3-month IPMAN change is positive at 5.43%, well above the -2% stress threshold.""]",down,bb38882fc9ab76ad84e9648573d86cae,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/bb38882fc9ab76ad84e9648573d86cae
+historical_frequency,Historical frequency,2020-02-01,2020-05-01,0.06097560975609756,1.0,0.8817668054729326,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-02-01,2020-05-01,0.052140077468077306,1.0,0.8984384327422223,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-02-01,2020-05-01,0.02733415737748146,1.0,0.946078841404574,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-02-01,2020-05-01,0.052140077468077306,1.0,0.8984384327422223,0.052140077468077306,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-02-01,2020-05-01,0.08,1.0,0.8464,,,,,,,"IPMAN momentum is positive over three months, and macro indicators like low unemployment and strong equity returns suggest resilience. The base rate is low, and current signals do not indicate imminent manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-02-01,2020-05-01,0.052,1.0,0.898704,0.052140075286724756,0.052,-0.00014007528672475844,-0.00014007528672475844,0.052,passed,"IPMAN shows positive three-month growth, and macro indicators like low unemployment and strong equity returns suggest stability. No immediate evidence of manufacturing stress is present.","[""none are material""]","[""Positive 3-month IPMAN growth of 0.53% and low unemployment rate of 3.6%.""]",neutral,,
+adaptive_agent,Adaptive agent,2020-02-01,2020-05-01,0.055,1.0,0.893025,0.052140075286724756,0.055,0.0028599247132752442,0.0028599247132752442,0.055,passed,"The 3-month IPMAN change is positive (+0.53%), indicating no immediate stress. While the 6-month trend is slightly negative, the current macro environment (low unemployment, positive equity returns) supports a low probability of reaching the -2% threshold.","[""IPMAN 3-month change is positive at 0.53%.""]","[""IPMAN 6-month change is negative at -0.46%.""]",neutral,240fb86470cc614652445df011d194ef,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/240fb86470cc614652445df011d194ef
+historical_frequency,Historical frequency,2020-03-01,2020-06-01,0.06086956521739131,1.0,0.8819659735349716,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-03-01,2020-06-01,0.054119858444652474,1.0,0.8946892421887642,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-03-01,2020-06-01,0.027216356247663498,1.0,0.9463080175520727,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-03-01,2020-06-01,0.054119858444652474,1.0,0.8946892421887642,0.054119858444652474,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-03-01,2020-06-01,0.08,1.0,0.8464,,,,,,,"IPMAN momentum is slightly negative but well above the -2% stress threshold. Macro indicators like low unemployment and positive equity returns suggest resilience, offsetting the modest contraction in manufacturing output.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-03-01,2020-06-01,0.065,1.0,0.8742250000000001,0.05411986251178315,0.065,0.010880137488216854,0.010880137488216854,0.065,passed,"The 3-month IPMAN decline of -0.076% is well above the -2% stress threshold. Low unemployment and positive equity returns suggest resilience, though the narrow yield spread warrants caution regarding future manufacturing momentum.","[""Narrow yield spread (0.18) suggesting potential future economic headwinds.""]","[""Low unemployment rate (3.5%) and positive 3-month SPY/XLI returns indicate ongoing economic stability.""]",neutral,,
+adaptive_agent,Adaptive agent,2020-03-01,2020-06-01,0.052,1.0,0.898704,0.05411986251178315,0.052,-0.0021198625117831507,-0.0021198625117831507,0.052,passed,"The 3-month IPMAN contraction of -0.076% is well above the -2% stress threshold. Recent 1-month growth is positive, and stable macro indicators like low unemployment and positive equity returns suggest minimal near-term stress risk.","[""3-month IPMAN contraction of -0.076% is significantly above the -2% stress threshold.""]","[""None are material; macro indicators remain supportive.""]",down,8f7f43fb52511a9bc2548c3ec7f049c1,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/8f7f43fb52511a9bc2548c3ec7f049c1
+historical_frequency,Historical frequency,2020-04-01,2020-07-01,0.0625,0.0,0.00390625,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-04-01,2020-07-01,0.21346534350303703,0.0,0.04556745287686959,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-04-01,2020-07-01,0.19660905003547668,0.0,0.038655118555852575,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-04-01,2020-07-01,0.21346534350303703,0.0,0.04556745287686959,0.21346534350303703,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-04-01,2020-07-01,0.85,0.0,0.7224999999999999,,,,,,,"IPMAN momentum is deeply negative (-4.64%), signaling active contraction. High credit spreads, elevated VIX, and poor equity performance (XLI -27%) confirm severe manufacturing sector stress, outweighing the low fed funds rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-04-01,2020-07-01,0.24346532434574816,0.0,0.059275364158780354,0.21346532434574816,0.65,0.43653467565425186,0.03,0.24346532434574816,clamped,"IPMAN has already declined 4.6% over three months, exceeding the stress threshold. High credit spreads, elevated VIX, and sharp declines in XLI and SPY indicate severe manufacturing and broader economic distress.","[""IPMAN 3-month decline of 4.64% exceeds the -2% stress threshold"", ""Credit spread of 3.93 and VIX of 53.54 indicate significant financial market stress""]","[""None are material""]",up,,
+adaptive_agent,Adaptive agent,2020-04-01,2020-07-01,0.24346532434574816,0.0,0.059275364158780354,0.21346532434574816,0.65,0.43653467565425186,0.03,0.24346532434574816,clamped,"The 3-month IPMAN contraction of -4.64% significantly exceeds the -2% stress threshold. High credit spreads (3.93), elevated VIX (53.54), and sharp declines in SPY and XLI returns indicate severe systemic manufacturing and market stress.","[""3-month IPMAN contraction of -4.64% exceeds the -2% stress threshold.""]","[""None are material.""]",up,03f17168789b79285a68adfb004c45bc,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/03f17168789b79285a68adfb004c45bc
+historical_frequency,Historical frequency,2020-05-01,2020-08-01,0.06412478336221837,0.0,0.004111987841251438,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-05-01,2020-08-01,0.2752426138863407,0.0,0.07575849649898522,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-05-01,2020-08-01,0.1895454376935959,0.0,0.03592747295045684,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-05-01,2020-08-01,0.2752426138863407,0.0,0.07575849649898522,0.2752426138863407,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-05-01,2020-08-01,0.85,0.0,0.7224999999999999,,,,,,,"Severe IPMAN contraction (-19% 3m) and poor XLI performance signal deep manufacturing stress. While the yield curve is not inverted, high unemployment and elevated credit spreads confirm a deteriorating environment, making stress highly probable.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-05-01,2020-08-01,0.30524259465373194,0.0,0.0931730415909425,0.27524259465373196,0.85,0.5747574053462681,0.03,0.30524259465373194,clamped,"IPMAN has plummeted 19% over three months, far exceeding the 2% stress threshold. High unemployment (14.8%) and negative industrial sector returns (XLI -20.2%) confirm severe manufacturing contraction, necessitating a significant upward adjustment from the anchor.","[""IPMAN 3-month decline of 19.01% significantly exceeds the 2% stress threshold.""]","[""None are material.""]",up,,
+adaptive_agent,Adaptive agent,2020-05-01,2020-08-01,0.30524259465373194,0.0,0.0931730415909425,0.27524259465373196,0.85,0.5747574053462681,0.03,0.30524259465373194,clamped,"The IPMAN has contracted by 19% over three months, far exceeding the 2% stress threshold. Extremely high unemployment, elevated credit spreads, and poor industrial equity performance signal severe, ongoing manufacturing stress.","[""3-month IPMAN contraction of 19.01% is significantly below the -2% stress threshold.""]","[""None are material.""]",up,6a169cc527a39a0e6aa25c83623e1e6b,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/6a169cc527a39a0e6aa25c83623e1e6b
+historical_frequency,Historical frequency,2020-06-01,2020-09-01,0.0657439446366782,0.0,0.0043222662563906075,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-06-01,2020-09-01,0.11178368837129353,0.0,0.012495592985890465,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-06-01,2020-09-01,0.18593783676624298,0.0,0.03457287914131002,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-06-01,2020-09-01,0.11178368837129353,0.0,0.012495592985890465,0.11178368837129353,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-06-01,2020-09-01,0.65,0.0,0.42250000000000004,,,,,,,"The 3-month IPMAN decline is severe (-15.6%), indicating deep structural stress. While recent 1-month momentum is positive, the macro environment (high unemployment, elevated credit spreads) suggests the manufacturing sector remains vulnerable to further contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-06-01,2020-09-01,0.14178370938216922,0.0,0.02010262024616742,0.11178370938216921,0.35,0.23821629061783078,0.03,0.14178370938216922,clamped,"The 3-month IPMAN decline of -15.6% indicates severe recent contraction. While the most recent month shows a rebound, the deep structural decline and negative XLI returns suggest continued manufacturing stress risk over the next quarter.","[""IPMAN 3-month change of -15.59% significantly exceeds the stress threshold.""]","[""IPMAN 1-month change shows a positive rebound of 4.46%.""]",up,,
+adaptive_agent,Adaptive agent,2020-06-01,2020-09-01,0.14178370938216922,0.0,0.02010262024616742,0.11178370938216921,0.85,0.7382162906178308,0.03,0.14178370938216922,clamped,"The 3-month IPMAN change is -15.59%, far exceeding the -2% stress threshold. Despite a recent 1-month rebound, the severe 3-month contraction indicates high manufacturing stress risk.","[""IPMAN 3-month change is -15.59%, significantly below the -2% stress threshold.""]","[""Recent 1-month IPMAN growth of 4.46%.""]",up,b42f67749decb193ac721c67d1782916,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/b42f67749decb193ac721c67d1782916
+historical_frequency,Historical frequency,2020-07-01,2020-10-01,0.06735751295336788,0.0,0.004537034551263122,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-07-01,2020-10-01,0.07286580139005637,0.0,0.005309425012215141,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-07-01,2020-10-01,0.15703493356704712,0.0,0.024659970360406902,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-07-01,2020-10-01,0.07286580139005637,0.0,0.005309425012215141,0.07286580139005637,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-07-01,2020-10-01,0.35,0.0,0.12249999999999998,,,,,,,"The 3-month IPMAN decline remains significant at -4.65%, signaling ongoing stress. However, the recent 1-month rebound and strong equity performance suggest potential stabilization, tempering the likelihood of continued contraction over the next three months.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-07-01,2020-10-01,0.10286581222778497,0.0,0.010581375325281916,0.07286581222778497,0.35,0.277134187772215,0.03,0.10286581222778497,clamped,"The 3-month IPMAN decline of -4.65% confirms current stress. While recent monthly growth is positive, the deep 6-month contraction and elevated credit spreads suggest persistent manufacturing headwinds, justifying a significant upward adjustment from the base rate.","[""IPMAN 3-month change of -4.65% exceeds the -2% stress threshold.""]","[""Recent 1-month IPMAN growth of 7.86% indicates a potential short-term recovery.""]",up,,
+adaptive_agent,Adaptive agent,2020-07-01,2020-10-01,0.10286581222778497,0.0,0.010581375325281916,0.07286581222778497,0.15,0.07713418777221502,0.03,0.10286581222778497,clamped,"The 3-month IPMAN change is -4.65%, exceeding the -2% stress threshold. Despite recent 1-month growth, the deep 3-month contraction and high unemployment (11%) signal significant manufacturing stress risk.","[""3-month IPMAN change of -4.65% is below the -2% stress threshold.""]","[""Recent 1-month IPMAN growth of 7.86% suggests short-term stabilization.""]",up,69802cbd595ed7cdb77a52e9a2356166,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/69802cbd595ed7cdb77a52e9a2356166
+historical_frequency,Historical frequency,2020-08-01,2020-11-01,0.06724137931034482,0.0,0.004521403091557669,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-08-01,2020-11-01,0.04924637263505067,0.0,0.0024252052177102675,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-08-01,2020-11-01,0.08705852180719376,0.0,0.007579186219253631,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-08-01,2020-11-01,0.04924637263505067,0.0,0.0024252052177102675,0.04924637263505067,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-08-01,2020-11-01,0.12,0.0,0.0144,,,,,,,"Strong recent IPMAN momentum (3-month growth of 16.8%) and positive equity returns outweigh the high unemployment rate and elevated credit spreads. The manufacturing sector is currently recovering from a sharp contraction, reducing near-term stress risk.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-08-01,2020-11-01,0.065,0.0,0.0042250000000000005,0.04924637999651748,0.065,0.015753620003482523,0.015753620003482523,0.065,passed,"Recent IPMAN growth is strong, but high unemployment and elevated credit spreads suggest underlying fragility. The probability is adjusted slightly upward from the anchor to reflect these macroeconomic headwinds.","[""High unemployment rate (10.2%) and elevated credit spreads (2.93%) indicate significant macroeconomic stress.""]","[""Strong short-term IPMAN growth (16.8% over three months) and positive equity market returns (SPY/XLI) suggest current manufacturing momentum.""]",up,,
+adaptive_agent,Adaptive agent,2020-08-01,2020-11-01,0.045,0.0,0.002025,0.04924637999651748,0.045,-0.004246379996517481,-0.004246379996517481,0.045,passed,"The 3-month IPMAN change is strongly positive (+16.8%), far from the -2% stress threshold. Recent momentum is robust, and despite elevated unemployment, the manufacturing sector shows significant recovery, justifying a downward adjustment from the anchor.","[""IPMAN 3-month change is +16.81%, well above the -2% stress threshold.""]","[""High unemployment rate (10.2%) and elevated VIX (30.43) suggest broader economic fragility.""]",down,56d03cdb21c304d4c25ecc633eed8b24,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/56d03cdb21c304d4c25ecc633eed8b24
+historical_frequency,Historical frequency,2020-09-01,2020-12-01,0.06712564543889846,0.0,0.004505852275588709,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-09-01,2020-12-01,0.060800599756671424,0.0,0.0036967129307709534,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-09-01,2020-12-01,0.08454321324825287,0.0,0.007147554906339559,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-09-01,2020-12-01,0.060800599756671424,0.0,0.0036967129307709534,0.060800599756671424,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-09-01,2020-12-01,0.08,0.0,0.0064,,,,,,,"Strong recent IPMAN momentum (13.4% 3-month growth) significantly outweighs the 6-month decline. Low Fed funds rate and positive yield spread support expansion, keeping the probability of stress near the historical base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-09-01,2020-12-01,0.055,0.0,0.003025,0.060800588675724146,0.055,-0.005800588675724146,-0.005800588675724146,0.055,passed,"IPMAN shows strong recent momentum with a 13.4% three-month growth rate. Despite elevated unemployment and credit spreads, the manufacturing sector's short-term recovery trajectory significantly reduces the immediate probability of meeting the stress threshold.","[""High initial jobless claims and elevated credit spreads suggest underlying economic fragility.""]","[""Strong 13.4% three-month IPMAN growth and positive 3-month equity returns for XLI indicate robust short-term manufacturing momentum.""]",down,,
+adaptive_agent,Adaptive agent,2020-09-01,2020-12-01,0.055,0.0,0.003025,0.060800588675724146,0.055,-0.005800588675724146,-0.005800588675724146,0.055,passed,"The 3-month IPMAN change is strongly positive (+13.43%), providing a significant buffer against the -2% stress threshold. Recent momentum is positive, and macro indicators do not signal imminent manufacturing contraction.","[""3-month IPMAN change of +13.43% is well above the -2% stress threshold.""]","[""None are material.""]",down,78e74d67f4a0e5f634335779aaa90169,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/78e74d67f4a0e5f634335779aaa90169
+historical_frequency,Historical frequency,2020-10-01,2021-01-01,0.06701030927835051,0.0,0.0044903815495801885,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-10-01,2021-01-01,0.06807086692442453,0.0,0.004633642923842714,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-10-01,2021-01-01,0.036405500024557114,0.0,0.001325360432038028,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-10-01,2021-01-01,0.06807086692442453,0.0,0.004633642923842714,0.06807086692442453,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-10-01,2021-01-01,0.12,0.0,0.0144,,,,,,,"While the 1-month IPMAN decline is notable, the 3-month momentum remains strongly positive. Macro signals like the positive yield curve and recovering equity markets suggest resilience, offsetting the elevated unemployment and credit spread risks.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-10-01,2021-01-01,0.085,0.0,0.007225000000000001,0.06807085202777803,0.085,0.016929147972221975,0.016929147972221975,0.085,passed,"The 1-month IPMAN decline of 2.6% signals short-term volatility. However, the 3-month trend remains positive at 5.1%, and strong equity returns (SPY/XLI) suggest resilience, keeping the probability of stress relatively low.","[""1-month IPMAN decline of 2.62% exceeds the stress threshold.""]","[""3-month IPMAN growth remains robust at 5.14%.""]",up,,
+adaptive_agent,Adaptive agent,2020-10-01,2021-01-01,0.065,0.0,0.0042250000000000005,0.06807085202777803,0.065,-0.003070852027778029,-0.003070852027778029,0.065,passed,"The 3-month IPMAN growth remains strong at 5.14%, providing a significant buffer against the -2% stress threshold. Despite a recent 1-month decline, the overall trend and stable macro indicators suggest low near-term stress risk.","[""3-month IPMAN growth of 5.14% provides a substantial buffer against the stress threshold.""]","[""1-month IPMAN decline of 2.62% indicates short-term volatility.""]",neutral,a7bf5eebeb6d268146ed354e6c41570f,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/a7bf5eebeb6d268146ed354e6c41570f
+historical_frequency,Historical frequency,2020-11-01,2021-02-01,0.06689536878216124,1.0,0.870684252800179,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-11-01,2021-02-01,0.06009904944188579,1.0,0.8834137968600466,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-11-01,2021-02-01,0.03619914874434471,1.0,0.9289120808811258,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-11-01,2021-02-01,0.06009904944188579,1.0,0.8834137968600466,0.06009904944188579,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-11-01,2021-02-01,0.08,1.0,0.8464,,,,,,,"IPMAN momentum is positive (+2.45%) and well above the stress threshold. While macro indicators like high unemployment and elevated credit spreads suggest caution, the manufacturing sector currently shows resilience, keeping the probability near the historical base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-11-01,2021-02-01,0.05,1.0,0.9025,0.06009903612855533,0.05,-0.010099036128555325,-0.010099036128555325,0.05,passed,"IPMAN shows positive momentum with a 2.45% three-month growth rate. Macro indicators, including positive equity returns and stable credit spreads, suggest a low probability of manufacturing stress in the near term.","[""None are material""]","[""IPMAN 3-month growth of 2.45% exceeds the stress threshold of -2.0%.""]",down,,
+adaptive_agent,Adaptive agent,2020-11-01,2021-02-01,0.055,1.0,0.893025,0.06009903612855533,0.055,-0.005099036128555327,-0.005099036128555327,0.055,passed,"IPMAN shows positive 3-month growth (+2.45%), well above the -2% stress threshold. Macro indicators, including positive equity returns and stable credit spreads, suggest a low risk of manufacturing stress in the near term.","[""3-month IPMAN change is positive at +2.45%""]","[""None are material""]",down,2627cea10216f81891dabb03902fe1a2,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/2627cea10216f81891dabb03902fe1a2
+historical_frequency,Historical frequency,2020-12-01,2021-03-01,0.06678082191780822,0.0,0.004459678176018015,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2020-12-01,2021-03-01,0.061388910139378555,0.0,0.0037685982881006954,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2020-12-01,2021-03-01,0.03597535938024521,0.0,0.001294226482537797,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2020-12-01,2021-03-01,0.061388910139378555,0.0,0.0037685982881006954,0.061388910139378555,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2020-12-01,2021-03-01,0.05,0.0,0.0025000000000000005,,,,,,,"IPMAN momentum is positive and accelerating. Macro indicators, including a positive yield curve and strong industrial sector returns, suggest a low probability of entering a contractionary stress phase within the three-month horizon.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2020-12-01,2021-03-01,0.055,0.0,0.003025,0.06138891199334536,0.055,-0.006388911993345361,-0.006388911993345361,0.055,passed,"IPMAN shows positive momentum over three months, and strong XLI returns suggest industrial optimism. Macro indicators like credit spreads and equity performance remain supportive, reducing the likelihood of a manufacturing contraction.","[""None are material""]","[""Positive 3-month IPMAN growth of 1.55% and strong 3-month XLI returns of 13.57%.""]",down,,
+adaptive_agent,Adaptive agent,2020-12-01,2021-03-01,0.055,0.0,0.003025,0.06138891199334536,0.055,-0.006388911993345361,-0.006388911993345361,0.055,passed,"IPMAN shows positive 3-month growth (+1.55%), remaining well above the -2% stress threshold. Macro indicators, including stable credit spreads and positive equity returns, suggest a low risk of manufacturing contraction in the near term.","[""Positive 3-month IPMAN growth of 1.55%""]","[""None are material""]",down,5036ef21db43c9230538e6f9227a5f90,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/5036ef21db43c9230538e6f9227a5f90
+historical_frequency,Historical frequency,2021-01-01,2021-04-01,0.06666666666666667,0.0,0.0044444444444444444,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-01-01,2021-04-01,0.06272214039432558,0.0,0.003934066895645489,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-01-01,2021-04-01,0.034609921276569366,0.0,0.001197846650770329,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-01-01,2021-04-01,0.06272214039432558,0.0,0.003934066895645489,0.06272214039432558,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-01-01,2021-04-01,0.05,0.0,0.0025000000000000005,,,,,,,"IPMAN momentum is strongly positive over 3 months. Macro indicators, including a positive yield curve and strong equity returns, suggest a low probability of manufacturing stress despite elevated unemployment and credit spreads.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-01-01,2021-04-01,0.05,0.0,0.0025000000000000005,0.06272215280149315,0.05,-0.01272215280149315,-0.01272215280149315,0.05,passed,"IPMAN shows strong positive momentum over three and six months. Macro indicators, including positive equity returns and stable credit spreads, suggest a low probability of manufacturing stress in the near term.","[""None are material""]","[""IPMAN 3-month change is positive at 2.38%, well above the stress threshold.""]",down,,
+adaptive_agent,Adaptive agent,2021-01-01,2021-04-01,0.06,0.0,0.0036,0.06272215280149315,0.06,-0.002722152801493155,-0.002722152801493155,0.06,passed,"The 3-month IPMAN change is positive (+2.38%), far from the -2% stress threshold. Strong recent XLI and SPY returns, combined with stable macro indicators, support a low probability of manufacturing stress.","[""3-month IPMAN change is positive at 2.38%""]","[""None are material""]",down,7082712657ba069a5e1b979093c2fcd3,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/7082712657ba069a5e1b979093c2fcd3
+historical_frequency,Historical frequency,2021-02-01,2021-05-01,0.06655290102389079,0.0,0.004429288634695804,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-02-01,2021-05-01,0.06540022505278902,0.0,0.004277189436955452,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-02-01,2021-05-01,0.034441106021404266,0.0,0.0011861897839776092,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-02-01,2021-05-01,0.06540022505278902,0.0,0.004277189436955452,0.06540022505278902,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-02-01,2021-05-01,0.08,0.0,0.0064,,,,,,,"IPMAN shows strong positive momentum over 3 and 6 months. While VIX and credit spreads suggest elevated financial market volatility, the manufacturing sector is currently expanding, keeping the probability of a 2% decline low.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-02-01,2021-05-01,0.0654,0.0,0.00427716,0.06540022574959833,0.0654,-2.2574959833432384e-07,-2.2574959833432384e-07,0.0654,passed,"IPMAN shows positive momentum with a 2.33% three-month growth rate, well above the stress threshold. Macro indicators like positive equity returns and a stable yield spread suggest low near-term manufacturing stress risk.","[""None are material""]","[""IPMAN 3-month growth of 2.33% remains well above the -2% stress threshold.""]",down,,
+adaptive_agent,Adaptive agent,2021-02-01,2021-05-01,0.06,0.0,0.0036,0.06540022574959833,0.06,-0.005400225749598336,-0.005400225749598336,0.06,passed,"The 3-month IPMAN change is positive (+2.33%), well above the -2% stress threshold. Recent monthly growth remains steady, and macroeconomic indicators do not signal imminent manufacturing contraction.","[""None are material""]","[""3-month IPMAN change of +2.33% is significantly above the -2% stress threshold.""]",down,8170860e1e137832204916d72a12135a,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/8170860e1e137832204916d72a12135a
+historical_frequency,Historical frequency,2021-03-01,2021-06-01,0.06814310051107325,0.0,0.004643482147262232,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-03-01,2021-06-01,0.07739873784055178,0.0,0.005990564619310462,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-03-01,2021-06-01,0.16644157469272614,0.0,0.027702797786194333,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-03-01,2021-06-01,0.07739873784055178,0.0,0.005990564619310462,0.07739873784055178,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-03-01,2021-06-01,0.28,0.0,0.07840000000000001,,,,,,,"IPMAN momentum is currently negative (-2.45%), meeting the stress threshold. However, positive equity returns and a steep yield curve suggest broader economic resilience, likely limiting the duration of this manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-03-01,2021-06-01,0.1073987324941089,0.0,0.011534487741341163,0.0773987324941089,0.25,0.1726012675058911,0.03,0.1073987324941089,clamped,"The IPMAN 3-month decline of 2.45% exceeds the stress threshold. While macro indicators like equity returns are positive, the recent sharp 1-month contraction in IPMAN signals significant near-term manufacturing weakness.","[""IPMAN 3-month percentage change is -2.45%, exceeding the -2.0% stress threshold.""]","[""Positive 3-month and 12-month SPY and XLI returns suggest broader market resilience.""]",up,,
+adaptive_agent,Adaptive agent,2021-03-01,2021-06-01,0.1073987324941089,0.0,0.011534487741341163,0.0773987324941089,0.15,0.07260126750589109,0.03,0.1073987324941089,clamped,"The 3-month IPMAN change is -2.45%, breaching the -2% stress threshold. Despite stable macro indicators, the immediate momentum is negative, necessitating an upward adjustment from the statistical anchor to reflect heightened near-term risk.","[""3-month trailing IPMAN change of -2.45% is below the -2% stress threshold.""]","[""Stable macroeconomic indicators, including a positive yield spread (1.30) and moderate credit spreads (2.12).""]",up,7bf79057404022f66dc53d82e3e13564,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/7bf79057404022f66dc53d82e3e13564
+historical_frequency,Historical frequency,2021-04-01,2021-07-01,0.06802721088435375,0.0,0.004627701420704337,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-04-01,2021-07-01,0.06536474927442257,0.0,0.004272550447708126,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-04-01,2021-07-01,0.051577161997556686,0.0,0.0026602036397222056,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-04-01,2021-07-01,0.06536474927442257,0.0,0.004272550447708126,0.06536474927442257,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-04-01,2021-07-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is slightly negative but improving. Macro indicators like the positive yield curve and strong equity returns suggest resilience, offsetting the risk of a sharp manufacturing contraction in the near term.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-04-01,2021-07-01,0.075,0.0,0.005625,0.06536475695863732,0.075,0.009635243041362679,0.009635243041362679,0.075,passed,"The 3-month IPMAN change is slightly negative, suggesting mild underlying weakness. However, strong recent 1-month growth and robust equity returns (SPY/XLI) indicate resilience, keeping the probability of stress low but slightly elevated above the statistical anchor.","[""3-month IPMAN change is negative at -0.18%""]","[""Strong 1-month IPMAN growth of 3.13% and positive 3-month XLI returns""]",up,,
+adaptive_agent,Adaptive agent,2021-04-01,2021-07-01,0.062,0.0,0.0038439999999999998,0.06536475695863732,0.062,-0.003364756958637319,-0.003364756958637319,0.062,passed,"The 3-month IPMAN change is -0.18%, well above the -2% stress threshold. Recent 1-month growth of 3.13% indicates strong momentum, reducing near-term stress risk despite historical base rates.","[""3-month IPMAN change of -0.18% is significantly above the -2% stress threshold.""]","[""None are material.""]",down,e3978ad8b61afbf3434505e63f6db5d9,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/e3978ad8b61afbf3434505e63f6db5d9
+historical_frequency,Historical frequency,2021-05-01,2021-08-01,0.06791171477079797,0.0,0.004612001003110219,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-05-01,2021-08-01,0.06816208949445193,0.0,0.004646070444249675,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-05-01,2021-08-01,0.07757506519556046,0.0,0.006017890740095455,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-05-01,2021-08-01,0.06816208949445193,0.0,0.004646070444249675,0.06816208949445193,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-05-01,2021-08-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is negative but improving. Macro indicators like strong XLI returns and a positive yield spread suggest resilience, offsetting the moderate contraction signal. The probability remains slightly above the base rate due to recent volatility.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-05-01,2021-08-01,0.085,0.0,0.007225000000000001,0.06816209742820226,0.085,0.01683790257179775,0.01683790257179775,0.085,passed,"The 3-month IPMAN decline of -0.93% is approaching the -2% stress threshold. While macro indicators like XLI returns are strong, the narrowing momentum suggests a slight increase in stress risk from the statistical anchor.","[""IPMAN 3-month percentage change is negative at -0.93%.""]","[""Strong 3-month and 12-month XLI returns indicate robust industrial sector performance.""]",up,,
+adaptive_agent,Adaptive agent,2021-05-01,2021-08-01,0.065,0.0,0.0042250000000000005,0.06816209742820226,0.065,-0.003162097428202254,-0.003162097428202254,0.065,passed,"The 3-month IPMAN contraction of -0.93% remains well above the -2% stress threshold. Recent 1-month growth is positive, and favorable macro indicators like the yield spread and strong equity returns suggest limited near-term stress risk.","[""3-month IPMAN contraction of -0.93% is significantly above the -2% stress threshold.""]","[""Positive 1-month IPMAN growth and strong XLI/SPY returns indicate sector resilience.""]",neutral,e0f9ba80cee0eef43b16f558cdf34d47,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/e0f9ba80cee0eef43b16f558cdf34d47
+historical_frequency,Historical frequency,2021-06-01,2021-09-01,0.06779661016949153,0.0,0.004596380350474002,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-06-01,2021-09-01,0.06527781494113784,0.0,0.004261193123489438,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-06-01,2021-09-01,0.05027211084961891,0.0,0.0025272851292763715,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-06-01,2021-09-01,0.06527781494113784,0.0,0.004261193123489438,0.06527781494113784,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-06-01,2021-09-01,0.05,0.0,0.0025000000000000005,,,,,,,"IPMAN momentum is strongly positive (4.5% 3m change). Macro indicators like the positive yield curve and strong equity returns suggest a robust environment, significantly reducing the likelihood of a 2% decline in the next three months.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-06-01,2021-09-01,0.055,0.0,0.003025,0.06527781572585613,0.055,-0.010277815725856128,-0.010277815725856128,0.055,passed,"IPMAN shows strong positive momentum over three months, and equity returns (SPY, XLI) are robust. Macro indicators suggest a healthy expansionary environment, reducing the likelihood of a manufacturing contraction in the near term.","[""None are material""]","[""IPMAN 3-month change is strongly positive at 4.5%"", ""Strong 3-month and 12-month returns for XLI and SPY""]",down,,
+adaptive_agent,Adaptive agent,2021-06-01,2021-09-01,0.06,0.0,0.0036,0.06527781572585613,0.06,-0.00527781572585613,-0.00527781572585613,0.06,passed,"The 3-month IPMAN change is strongly positive at 4.5%, far from the -2% stress threshold. Macro indicators, including a healthy yield spread and strong equity returns, support a low probability of near-term manufacturing stress.","[""3-month IPMAN change of 4.5% is well above the -2% stress threshold.""]","[""None are material.""]",down,c7285430dd28b8f1705cea2d8dc68bcb,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/c7285430dd28b8f1705cea2d8dc68bcb
+historical_frequency,Historical frequency,2021-07-01,2021-10-01,0.0676818950930626,0.0,0.004580838923388332,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-07-01,2021-10-01,0.06918041954469285,0.0,0.004785930448379721,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-07-01,2021-10-01,0.050676316022872925,0.0,0.002568089005650087,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-07-01,2021-10-01,0.06918041954469285,0.0,0.004785930448379721,0.06918041954469285,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-07-01,2021-10-01,0.075,0.0,0.005625,,,,,,,"IPMAN momentum is positive over 3 months, and macro indicators like the yield curve and equity returns suggest expansion. The base rate is low, and current manufacturing data shows no immediate signs of contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-07-01,2021-10-01,0.065,0.0,0.0042250000000000005,0.06918041302358123,0.065,-0.004180413023581228,-0.004180413023581228,0.065,passed,"IPMAN shows positive momentum over three and six months. Macro indicators, including a positive yield curve spread and strong equity returns, suggest low near-term manufacturing stress risk, justifying a slight downward adjustment from the statistical anchor.","[""IPMAN 3-month change is positive at 1.34 percent.""]","[""None are material; macro and momentum signals are currently supportive of stability.""]",down,,
+adaptive_agent,Adaptive agent,2021-07-01,2021-10-01,0.065,0.0,0.0042250000000000005,0.06918041302358123,0.065,-0.004180413023581228,-0.004180413023581228,0.065,passed,"The 3-month IPMAN change is positive (+1.34%), indicating growth rather than contraction. With strong equity returns and stable macro indicators, the risk of hitting the -2% stress threshold within three months remains low.","[""None are material""]","[""Positive 3-month IPMAN growth of 1.34% and strong 12-month XLI returns.""]",down,06f4c4a631e57646e9f72b0715488b3a,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/06f4c4a631e57646e9f72b0715488b3a
+historical_frequency,Historical frequency,2021-08-01,2021-11-01,0.06756756756756757,0.0,0.004565376186997809,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-08-01,2021-11-01,0.06828587355057798,0.0,0.004662960526565525,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-08-01,2021-11-01,0.051202960312366486,0.0,0.0026217431447497774,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-08-01,2021-11-01,0.06828587355057798,0.0,0.004662960526565525,0.06828587355057798,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-08-01,2021-11-01,0.06,0.0,0.0036,,,,,,,"Positive 3-month IPMAN momentum and a steep yield curve suggest a healthy manufacturing environment. Macro indicators show no immediate signs of contraction, keeping the probability of stress below the historical base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-08-01,2021-11-01,0.068,0.0,0.0046240000000000005,0.0682858669498902,0.068,-0.00028586694989019,-0.00028586694989019,0.068,passed,"IPMAN shows positive momentum with a 2.02% three-month growth rate. Macro indicators, including strong equity returns and stable credit spreads, suggest low near-term manufacturing stress risk, consistent with the statistical anchor.","[""None are material""]","[""IPMAN 3-month growth of 2.02% indicates positive momentum, well above the stress threshold.""]",down,,
+adaptive_agent,Adaptive agent,2021-08-01,2021-11-01,0.062,0.0,0.0038439999999999998,0.0682858669498902,0.062,-0.006285866949890195,-0.006285866949890195,0.062,passed,"The 3-month IPMAN change is positive (+2.02%), indicating robust momentum well above the -2% stress threshold. Macro indicators, including a positive yield spread and stable credit spreads, support a low probability of near-term manufacturing stress.","[""3-month IPMAN change is positive at 2.02%""]","[""none are material""]",down,6a190b7d3080f2ef8783813bcdc2b127,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/6a190b7d3080f2ef8783813bcdc2b127
+historical_frequency,Historical frequency,2021-09-01,2021-12-01,0.06745362563237774,0.0,0.004549991610952967,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-09-01,2021-12-01,0.07058964081532171,0.0,0.004982897390436132,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-09-01,2021-12-01,0.05219452083110809,0.0,0.0027242680047889767,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-09-01,2021-12-01,0.07058964081532171,0.0,0.004982897390436132,0.07058964081532171,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-09-01,2021-12-01,0.09,0.0,0.0081,,,,,,,"IPMAN momentum remains positive over three months, and the yield curve is steep. While recent monthly IPMAN is slightly negative, macro indicators like low credit spreads and strong equity returns suggest limited immediate manufacturing stress risk.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-09-01,2021-12-01,0.075,0.0,0.005625,0.07058963930488979,0.075,0.0044103606951102076,0.0044103606951102076,0.075,passed,"IPMAN remains stable over the three-month window, and macro indicators like the yield curve and equity returns suggest resilience. A slight upward adjustment reflects the recent one-month dip in IPMAN.","[""Recent one-month IPMAN decline of 0.59%.""]","[""Positive three-month IPMAN growth of 0.32% and healthy 12-month XLI returns.""]",neutral,,
+adaptive_agent,Adaptive agent,2021-09-01,2021-12-01,0.0706,0.0,0.004984359999999999,0.07058963930488979,0.0706,1.036069511020643e-05,1.036069511020643e-05,0.0706,passed,"The 3-month IPMAN change is positive (+0.32%), indicating no immediate stress. While the 1-month change is slightly negative, it remains far from the -2% threshold. Macro indicators like yield spread and credit spread remain supportive.","[""3-month IPMAN change is positive at 0.32%""]","[""1-month IPMAN change is slightly negative at -0.59%""]",neutral,551da4b64e401eb8a27e133184f17928,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/551da4b64e401eb8a27e133184f17928
+historical_frequency,Historical frequency,2021-10-01,2022-01-01,0.06734006734006734,0.0,0.004534684669364804,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-10-01,2022-01-01,0.0758459508565538,0.0,0.0057526082613347735,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-10-01,2022-01-01,0.09841671586036682,0.0,0.009685849960740178,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-10-01,2022-01-01,0.0758459508565538,0.0,0.0057526082613347735,0.0758459508565538,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-10-01,2022-01-01,0.15,0.0,0.0225,,,,,,,"IPMAN momentum is negative but remains above the -2% threshold. Macro signals are mixed; while XLI underperformance suggests sector weakness, the positive yield curve and low unemployment provide a buffer against immediate manufacturing stress.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-10-01,2022-01-01,0.10584595533586617,0.0,0.011203366260962177,0.07584595533586617,0.12,0.04415404466413382,0.03,0.10584595533586617,clamped,"The 3-month IPMAN decline is accelerating, and the negative 3-month XLI return suggests weakening industrial sentiment. While the base rate is low, these indicators point to an increased risk of hitting the stress threshold.","[""IPMAN 3-month decline of -0.92% and 1-month decline of -1.22%"", ""XLI 3-month return of -4.14%""]","[""None are material""]",up,,
+adaptive_agent,Adaptive agent,2021-10-01,2022-01-01,0.085,0.0,0.007225000000000001,0.07584595533586617,0.085,0.009154044664133831,0.009154044664133831,0.085,passed,"The 3-month IPMAN contraction of -0.92% is approaching the -2% threshold. While macro signals remain stable, the recent 1-month decline of -1.22% suggests increased downward momentum, warranting a slight upward adjustment from the anchor.","[""Recent 1-month IPMAN decline of -1.22% indicates accelerating short-term contraction.""]","[""3-month IPMAN contraction of -0.92% remains well above the -2% stress threshold.""]",up,b5609d1fbc944e0977e267b913d2b2a9,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/b5609d1fbc944e0977e267b913d2b2a9
+historical_frequency,Historical frequency,2021-11-01,2022-02-01,0.06722689075630252,0.0,0.004519454840759833,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-11-01,2022-02-01,0.069884738949092,0.0,0.004883876737982736,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-11-01,2022-02-01,0.04617028683423996,0.0,0.0021316953863559918,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-11-01,2022-02-01,0.069884738949092,0.0,0.004883876737982736,0.069884738949092,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-11-01,2022-02-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is slightly negative but improving. Macro indicators like the positive yield curve and strong equity returns suggest resilience, offsetting the modest contraction risk. The probability remains near the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-11-01,2022-02-01,0.075,0.0,0.005625,0.06988473673543567,0.075,0.005115263264564324,0.005115263264564324,0.075,passed,"The 3-month IPMAN change is negative, indicating slight momentum weakness. However, positive 1-month growth and favorable macro indicators like low unemployment and positive equity returns suggest the risk of a 2% decline remains low.","[""IPMAN 3-month percentage change is negative at -0.42%""]","[""Positive 1-month IPMAN growth and strong 12-month equity returns (SPY/XLI)""]",neutral,,
+adaptive_agent,Adaptive agent,2021-11-01,2022-02-01,0.065,0.0,0.0042250000000000005,0.06988473673543567,0.065,-0.004884736735435671,-0.004884736735435671,0.065,passed,"The 3-month IPMAN contraction of -0.42% remains well above the -2% stress threshold. Recent 1-month growth of 1.41% and stable macro indicators suggest low near-term risk, justifying a slight downward adjustment from the anchor.","[""3-month IPMAN contraction of -0.42% is significantly above the -2% stress threshold.""]","[""None are material.""]",down,6dca10a076d13b3a8b9bd8cc7c5f0894,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/6dca10a076d13b3a8b9bd8cc7c5f0894
+historical_frequency,Historical frequency,2021-12-01,2022-03-01,0.06711409395973154,0.0,0.004504301608035674,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2021-12-01,2022-03-01,0.07533511831602356,0.0,0.005675380051689269,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2021-12-01,2022-03-01,0.06465872377157211,0.0,0.004180750559768465,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2021-12-01,2022-03-01,0.07533511831602356,0.0,0.005675380051689269,0.07533511831602356,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2021-12-01,2022-03-01,0.09,0.0,0.0081,,,,,,,"IPMAN momentum is positive over 1m and 3m horizons, suggesting resilience. While the yield curve is inverted and credit spreads are elevated, the manufacturing sector shows no immediate signs of the 2% decline threshold.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2021-12-01,2022-03-01,0.085,0.0,0.007225000000000001,0.07533512959779218,0.085,0.009664870402207829,0.009664870402207829,0.085,passed,"IPMAN trends remain positive, but the negative XLI 3-month return and elevated VIX suggest emerging sector-specific caution. The probability is adjusted slightly upward from the anchor to reflect these mixed signals.","[""XLI 3-month return is negative (-3.27%), indicating sector-specific weakness.""]","[""IPMAN 3-month change remains positive at 0.76%, suggesting current production stability.""]",up,,
+adaptive_agent,Adaptive agent,2021-12-01,2022-03-01,0.072,0.0,0.005183999999999999,0.07533512959779218,0.072,-0.003335129597792183,-0.003335129597792183,0.072,passed,"IPMAN shows positive 3-month growth (+0.76%), far from the -2% stress threshold. While XLI return is negative, broader macro indicators remain stable, supporting a slight downward adjustment from the statistical anchor.","[""Positive 3-month IPMAN growth of 0.76%.""]","[""Negative 3-month XLI return of -3.27%.""]",down,933622d6bac7d43b6d358d0f2d1150ec,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/933622d6bac7d43b6d358d0f2d1150ec
+historical_frequency,Historical frequency,2022-01-01,2022-04-01,0.06700167504187604,0.0,0.004489224458417155,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-01-01,2022-04-01,0.07512972840915724,0.0,0.005644476090833729,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-01-01,2022-04-01,0.06415830552577972,0.0,0.004116288167939297,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-01-01,2022-04-01,0.07512972840915724,0.0,0.005644476090833729,0.07512972840915724,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-01-01,2022-04-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum remains positive over three months, offsetting recent minor declines. While the yield curve is steep and credit spreads are moderate, the manufacturing sector shows resilience despite high interest rates and cooling industrial equity performance.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-01-01,2022-04-01,0.085,0.0,0.007225000000000001,0.07512973973141827,0.085,0.009870260268581732,0.009870260268581732,0.085,passed,"IPMAN remains stable over three months, but the negative XLI 3-month return and elevated credit spreads suggest emerging sector-specific headwinds, warranting a slight upward adjustment from the statistical anchor.","[""XLI 3-month return is negative at -3.27 percent, indicating sector-specific weakness.""]","[""IPMAN 3-month change remains positive at 1.89 percent, suggesting current production momentum is not yet in a stressed state.""]",up,,
+adaptive_agent,Adaptive agent,2022-01-01,2022-04-01,0.072,0.0,0.005183999999999999,0.07512973973141827,0.072,-0.0031297397314182795,-0.0031297397314182795,0.072,passed,"The 3-month IPMAN change remains positive at 1.89%, providing a significant buffer against the -2% stress threshold. Despite some volatility in XLI returns, macroeconomic indicators like low unemployment and stable credit spreads suggest limited near-term stress risk.","[""XLI_RETURN_3M is negative at -3.27%""]","[""IPMAN_change_3m_pct is positive at 1.89%, well above the -2% stress threshold""]",down,1e67d6367c659c9761565c202e0ee15e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/1e67d6367c659c9761565c202e0ee15e
+historical_frequency,Historical frequency,2022-02-01,2022-05-01,0.06688963210702341,0.0,0.0044742228834129365,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-02-01,2022-05-01,0.0776119698514409,0.0,0.006023617864220972,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-02-01,2022-05-01,0.13018126785755157,0.0,0.01694716250099959,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-02-01,2022-05-01,0.0776119698514409,0.0,0.006023617864220972,0.0776119698514409,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-02-01,2022-05-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but currently above the -2% threshold. While macro signals like rising inflation and tightening credit spreads suggest headwinds, the manufacturing sector remains resilient, keeping the probability of a sharp decline moderate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-02-01,2022-05-01,0.10761197025694623,0.0,0.01158033614258188,0.07761197025694623,0.11,0.03238802974305377,0.03,0.10761197025694623,clamped,"IPMAN trends are slightly negative, and industrial sector performance (XLI) is weakening. While current stress is low, the combination of tightening credit spreads and negative momentum suggests a moderate increase in near-term risk.","[""XLI_RETURN_3M is negative at -3.23 percent, indicating sector-specific weakness.""]","[""UNRATE remains low at 4.0 percent, suggesting continued labor market resilience.""]",up,,
+adaptive_agent,Adaptive agent,2022-02-01,2022-05-01,0.075,0.0,0.005625,0.07761197025694623,0.075,-0.0026119702569462333,-0.0026119702569462333,0.075,passed,"The 3-month IPMAN contraction of -0.49% remains well above the -2% stress threshold. While recent XLI and SPY returns are negative, the overall macro environment and current IPMAN trajectory suggest low immediate risk of reaching the threshold.","[""Negative 3-month returns for XLI (-3.23%) and SPY (-1.69%) suggest potential sector-specific headwinds.""]","[""Current 3-month IPMAN contraction of -0.49% is significantly above the -2% stress threshold.""]",neutral,20bd50ac5af2b7c930d54722f307a593,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/20bd50ac5af2b7c930d54722f307a593
+historical_frequency,Historical frequency,2022-03-01,2022-06-01,0.0667779632721202,0.0,0.004459296378772634,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-03-01,2022-06-01,0.07876728249363937,0.0,0.006204284791432787,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-03-01,2022-06-01,0.059789858758449554,0.0,0.003574827210355347,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-03-01,2022-06-01,0.07876728249363937,0.0,0.006204284791432787,0.07876728249363937,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-03-01,2022-06-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is slightly negative but far from the -2% threshold. Macro signals show elevated VIX and credit spreads, but low unemployment and positive 12-month returns suggest resilience, keeping stress probability near the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-03-01,2022-06-01,0.10876728314795436,0.0,0.011830321883387278,0.07876728314795436,0.11,0.031232716852045636,0.03,0.10876728314795436,clamped,"IPMAN trends are stable, but elevated VIX, rising credit spreads, and negative 3-month equity returns suggest tightening financial conditions, increasing the risk of manufacturing stress despite low unemployment and moderate inflation.","[""Elevated VIX (30.15) and widening credit spreads (2.34) indicate heightened market stress and tighter financial conditions.""]","[""Low unemployment rate (3.9%) and positive 1-month IPMAN growth (0.58%) suggest underlying resilience in the manufacturing sector.""]",up,,
+adaptive_agent,Adaptive agent,2022-03-01,2022-06-01,0.085,0.0,0.007225000000000001,0.07876728314795436,0.085,0.006232716852045642,0.006232716852045642,0.085,passed,"The 3-month IPMAN contraction is -0.5%, well above the -2% threshold. However, elevated VIX (30.15) and negative 3-month SPY returns suggest increased market volatility and potential headwinds, warranting a slight upward adjustment from the anchor.","[""3-month IPMAN contraction of -0.5% is significantly above the -2% stress threshold.""]","[""Elevated VIX (30.15) and negative 3-month SPY returns indicate heightened market risk.""]",up,841577fe01cddd1cd76140bde6cc860f,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/841577fe01cddd1cd76140bde6cc860f
+historical_frequency,Historical frequency,2022-04-01,2022-07-01,0.06666666666666667,0.0,0.0044444444444444444,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-04-01,2022-07-01,0.07609198780356942,0.0,0.005789990607898557,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-04-01,2022-07-01,0.053971067070961,0.0,0.0029128760807781706,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-04-01,2022-07-01,0.07609198780356942,0.0,0.005789990607898557,0.07609198780356942,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-04-01,2022-07-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is positive and stable. While equity returns and credit spreads suggest some market caution, the lack of contraction in manufacturing data and a healthy labor market keep stress probability near the historical base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-04-01,2022-07-01,0.085,0.0,0.007225000000000001,0.07609198468625333,0.085,0.008908015313746673,0.008908015313746673,0.085,passed,"IPMAN trends remain positive, but negative 3-month equity returns (SPY and XLI) and elevated inflation suggest mounting headwinds, warranting a slight upward adjustment from the statistical anchor.","[""Negative 3-month returns for SPY (-4.61%) and XLI (-2.35%) indicate cooling market sentiment.""]","[""IPMAN 3-month change remains positive at 0.07%.""]",up,,
+adaptive_agent,Adaptive agent,2022-04-01,2022-07-01,0.072,0.0,0.005183999999999999,0.07609198468625333,0.072,-0.004091984686253339,-0.004091984686253339,0.072,passed,"The 3-month IPMAN change is positive (+0.07%), well above the -2% stress threshold. While SPY and XLI returns are negative, the low unemployment rate and stable credit spreads suggest limited near-term manufacturing stress risk.","[""3-month IPMAN change is positive (+0.074%)""]","[""Negative 3-month returns for SPY (-4.6%) and XLI (-2.35%)""]",down,260708c41be37048e7776f0a5ce43661,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/260708c41be37048e7776f0a5ce43661
+historical_frequency,Historical frequency,2022-05-01,2022-08-01,0.06655574043261231,0.0,0.004429666584533265,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-05-01,2022-08-01,0.07567159577713792,0.0,0.005726190407458557,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-05-01,2022-08-01,0.05300641059875488,0.0,0.002809679564563794,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-05-01,2022-08-01,0.07567159577713792,0.0,0.005726190407458557,0.07567159577713792,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-05-01,2022-08-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum remains positive over three months, offsetting the recent one-month dip. Macro signals are mixed; low unemployment and positive yield spread suggest resilience, while negative equity returns and credit spreads indicate moderate caution.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-05-01,2022-08-01,0.095,0.0,0.009025,0.0756715925496873,0.095,0.019328407450312696,0.019328407450312696,0.095,passed,"While the 3-month IPMAN trend remains positive, the recent 1-month decline and negative 3-month equity returns (SPY and XLI) suggest rising headwinds, warranting a slight upward adjustment from the statistical anchor.","[""1-month IPMAN decline of 0.05% and negative 3-month XLI return of -2.35%.""]","[""3-month IPMAN change remains positive at 0.99%.""]",up,,
+adaptive_agent,Adaptive agent,2022-05-01,2022-08-01,0.075,0.0,0.005625,0.0756715925496873,0.075,-0.0006715925496873082,-0.0006715925496873082,0.075,passed,"The 3-month IPMAN change remains positive (+0.99%), providing a significant buffer against the -2% stress threshold. Despite negative 3-month equity returns, macroeconomic indicators like low unemployment and stable credit spreads suggest limited near-term manufacturing stress risk.","[""3-month IPMAN change is positive at 0.99%""]","[""Negative 3-month SPY and XLI returns suggest potential headwinds""]",neutral,0386a049f46baf6ac2c98f97e6b88693,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/0386a049f46baf6ac2c98f97e6b88693
+historical_frequency,Historical frequency,2022-06-01,2022-09-01,0.0664451827242525,0.0,0.004414962307259303,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-06-01,2022-09-01,0.0801586522219964,0.0,0.006425409526046969,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-06-01,2022-09-01,0.13130012154579163,0.0,0.017239721917939654,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-06-01,2022-09-01,0.0801586522219964,0.0,0.006425409526046969,0.0801586522219964,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-06-01,2022-09-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is slightly negative but remains well above the -2% stress threshold. Macro signals show mixed pressure; while equity returns and credit spreads are concerning, low unemployment and a positive yield spread provide some resilience.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-06-01,2022-09-01,0.1101586452218149,0.0,0.012134927117105683,0.0801586452218149,0.12,0.039841354778185095,0.03,0.1101586452218149,clamped,"While the 3-month IPMAN change remains near zero, elevated credit spreads and negative equity returns (SPY/XLI) suggest tightening financial conditions, increasing the risk of a manufacturing downturn over the next quarter.","[""Elevated credit spread of 2.18 and negative 3-month returns for SPY and XLI.""]","[""The 3-month IPMAN change is only -0.04%, well above the -2% stress threshold.""]",up,,
+adaptive_agent,Adaptive agent,2022-06-01,2022-09-01,0.075,0.0,0.005625,0.0801586452218149,0.075,-0.005158645221814903,-0.005158645221814903,0.075,passed,"The 3-month IPMAN contraction of -0.04% remains well above the -2% stress threshold. While equity returns are negative, labor market and credit conditions do not currently signal imminent manufacturing stress.","[""Negative 3-month SPY and XLI returns suggest potential headwinds.""]","[""3-month IPMAN contraction of -0.04% is significantly above the -2% stress threshold.""]",neutral,fba502410d60f19a89973617226b44bd,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/fba502410d60f19a89973617226b44bd
+historical_frequency,Historical frequency,2022-07-01,2022-10-01,0.06633499170812604,0.0,0.004400331124917151,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-07-01,2022-10-01,0.0858334511458165,0.0,0.007367381335601268,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-07-01,2022-10-01,0.17706815898418427,0.0,0.031353132926048355,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-07-01,2022-10-01,0.0858334511458165,0.0,0.007367381335601268,0.0858334511458165,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-07-01,2022-10-01,0.22,0.0,0.0484,,,,,,,"IPMAN momentum is negative but currently above the -2% threshold. High inflation and poor equity returns suggest headwinds, but low unemployment and a positive yield spread provide some resilience against immediate manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-07-01,2022-10-01,0.1158334432390138,0.0,0.013417386572605832,0.0858334432390138,0.15,0.06416655676098619,0.03,0.1158334432390138,clamped,"IPMAN momentum is negative and accelerating. High inflation (8.98%), inverted yield curve, and significant equity market declines (SPY/XLI) suggest heightened manufacturing stress risk despite low unemployment.","[""IPMAN 3-month decline of 1.04% and negative 1-month momentum"", ""Significant 3-month declines in SPY (-16.1%) and XLI (-14.8%)"", ""High CPI inflation (8.98%) and narrow yield spread (0.06%)""]","[""Low unemployment rate (3.6%) and relatively low initial jobless claims (212k)""]",up,,
+adaptive_agent,Adaptive agent,2022-07-01,2022-10-01,0.115,0.0,0.013225,0.0858334432390138,0.115,0.0291665567609862,0.0291665567609862,0.115,passed,"The 3-month IPMAN contraction is accelerating (-1.04%), and negative equity returns (SPY -16.1%) combined with high inflation (8.98%) and widening credit spreads (2.31) increase the risk of hitting the -2% stress threshold.","[""3-month IPMAN contraction of -1.04% is trending toward the -2% threshold."", ""Significant negative equity market performance (SPY -16.1% 3M) and elevated credit spreads (2.31).""]","[""Current 3-month contraction of -1.04% remains above the -2% stress threshold.""]",up,2b41f9cb60f2143c921adedbd3f10f4e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/2b41f9cb60f2143c921adedbd3f10f4e
+historical_frequency,Historical frequency,2022-08-01,2022-11-01,0.06622516556291391,0.0,0.004385772553835359,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-08-01,2022-11-01,0.07675681590951702,0.0,0.005891608788567485,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-08-01,2022-11-01,0.06908271461725235,0.0,0.0047724214588887315,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-08-01,2022-11-01,0.07675681590951702,0.0,0.005891608788567485,0.07675681590951702,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-08-01,2022-11-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but mild (-0.9%). While the yield curve is inverted and inflation is high, low unemployment and stable industrial sector returns suggest resilience, keeping the probability of a sharp 2% decline relatively low.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-08-01,2022-11-01,0.10675682202071068,0.0,0.011397019047961698,0.07675682202071069,0.11,0.033243177979289315,0.03,0.10675682202071068,clamped,"The inverted yield curve and elevated credit spreads signal heightened economic risk. While IPMAN remains stable, the negative 3-month trend and tightening financial conditions suggest an increased probability of manufacturing stress over the next quarter.","[""Inverted yield curve (YC_SPREAD: -0.22)""]","[""Low unemployment rate (UNRATE: 3.5)""]",up,,
+adaptive_agent,Adaptive agent,2022-08-01,2022-11-01,0.072,0.0,0.005183999999999999,0.07675682202071069,0.072,-0.004756822020710691,-0.004756822020710691,0.072,passed,"The 3-month IPMAN contraction of -0.91% remains well above the -2% stress threshold. While the yield curve is inverted, recent monthly IPMAN growth is positive, suggesting a low immediate risk of reaching the stress threshold.","[""Inverted yield curve (YC_SPREAD -0.22)""]","[""3-month IPMAN contraction (-0.91%) is significantly above the -2% stress threshold""]",neutral,dacfdb143f3a07bbb1ea36d328f5a3c3,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/dacfdb143f3a07bbb1ea36d328f5a3c3
+historical_frequency,Historical frequency,2022-09-01,2022-12-01,0.06611570247933884,1.0,0.8721398811556588,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-09-01,2022-12-01,0.07824470950449967,1.0,0.8496328155564442,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-09-01,2022-12-01,0.10023434460163116,1.0,0.8095782346344562,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-09-01,2022-12-01,0.07824470950449967,1.0,0.8496328155564442,0.07824470950449967,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-09-01,2022-12-01,0.28,1.0,0.5184,,,,,,,"IPMAN momentum is negative but currently above the -2% threshold. Inverted yield curve and negative equity returns suggest headwinds, but low unemployment and stable initial claims provide a buffer against immediate manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-09-01,2022-12-01,0.10824471116493489,1.0,0.7952274951653103,0.0782447111649349,0.15,0.0717552888350651,0.03,0.10824471116493489,clamped,"The inverted yield curve and negative equity returns suggest heightened economic risk. While the current IPMAN decline is modest, these macro headwinds increase the likelihood of manufacturing stress over the next three months.","[""Inverted yield curve (YC_SPREAD: -0.30)""]","[""Low unemployment rate (UNRATE: 3.6)""]",up,,
+adaptive_agent,Adaptive agent,2022-09-01,2022-12-01,0.10824471116493489,1.0,0.7952274951653103,0.0782447111649349,0.12,0.0417552888350651,0.03,0.10824471116493489,clamped,"The 3-month IPMAN contraction is -0.48%, approaching the -2% threshold. Inverted yield curve (-0.30) and negative equity returns suggest rising macroeconomic headwinds, warranting an upward adjustment from the anchor.","[""Inverted yield curve (YC_SPREAD -0.30)"", ""Negative 3-month SPY return (-3.88%)""]","[""3-month IPMAN contraction (-0.48%) remains above the -2% stress threshold""]",up,796dee2c948ffab88ed8bc3e041ce481,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/796dee2c948ffab88ed8bc3e041ce481
+historical_frequency,Historical frequency,2022-10-01,2023-01-01,0.066006600660066,0.0,0.004356871330697425,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-10-01,2023-01-01,0.07753301458753294,0.0,0.006011368351030595,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-10-01,2023-01-01,0.10258731245994568,0.0,0.010524156677754526,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-10-01,2023-01-01,0.07753301458753294,0.0,0.006011368351030595,0.07753301458753294,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-10-01,2023-01-01,0.18,0.0,0.0324,,,,,,,"Positive 3-month IPMAN momentum offsets recent weakness, but the inverted yield curve and negative equity returns suggest rising headwinds. The probability is elevated above the base rate due to tightening financial conditions.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-10-01,2023-01-01,0.10753301628213063,0.0,0.011563349590732972,0.07753301628213063,0.12,0.04246698371786936,0.03,0.10753301628213063,clamped,"The inverted yield curve and negative equity returns suggest rising macroeconomic headwinds. While IPMAN remains stable, these leading indicators increase the risk of a manufacturing contraction over the next three months.","[""Inverted yield curve (YC_SPREAD: -0.30) and negative 3-month SPY returns (-3.89%) indicate heightened recessionary risk.""]","[""IPMAN 3-month change remains positive (+5.65%), indicating current manufacturing output has not yet entered a contractionary phase.""]",up,,
+adaptive_agent,Adaptive agent,2022-10-01,2023-01-01,0.085,0.0,0.007225000000000001,0.07753301628213063,0.085,0.0074669837178693715,0.0074669837178693715,0.085,passed,"The 3-month IPMAN trend is positive (+5.6%), providing a strong buffer against the -2% stress threshold. However, the inverted yield curve (-0.30) and rising credit spreads (2.18) signal elevated macroeconomic risk, warranting a slight upward adjustment.","[""Inverted yield curve (-0.30) and elevated credit spreads (2.18) suggest increased macroeconomic risk.""]","[""Strong 3-month IPMAN growth (+5.6%) keeps the sector well above the -2% stress threshold.""]",up,de00480f4428b4154fa7bfa2f7a8bbd6,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/de00480f4428b4154fa7bfa2f7a8bbd6
+historical_frequency,Historical frequency,2022-11-01,2023-02-01,0.06589785831960461,0.0,0.004342527731110683,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-11-01,2023-02-01,0.07701773425561158,0.0,0.005931731389868005,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-11-01,2023-02-01,0.09687554091215134,0.0,0.009384870427021907,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-11-01,2023-02-01,0.07701773425561158,0.0,0.005931731389868005,0.07701773425561158,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-11-01,2023-02-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is positive over 3 months, offsetting the inverted yield curve and negative equity returns. While macro headwinds persist, the manufacturing sector shows short-term resilience, keeping stress probability only slightly above the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-11-01,2023-02-01,0.10701772709201021,0.0,0.011452793911939976,0.07701772709201021,0.12,0.04298227290798978,0.03,0.10701772709201021,clamped,"While IPMAN shows short-term stability, the inverted yield curve and negative equity returns signal significant macroeconomic headwinds, increasing the likelihood of manufacturing stress despite current production levels.","[""Inverted yield curve (YC_SPREAD: -0.41) and negative 3-month SPY returns (-5.87%) suggest heightened recessionary risk.""]","[""Positive 1-month (0.25%) and 3-month (0.22%) IPMAN growth indicates current manufacturing resilience.""]",up,,
+adaptive_agent,Adaptive agent,2022-11-01,2023-02-01,0.085,0.0,0.007225000000000001,0.07701772709201021,0.085,0.007982272907989793,0.007982272907989793,0.085,passed,"While recent IPMAN growth is positive, the inverted yield curve (-0.41) and elevated credit spreads (2.28) signal heightened macroeconomic risk, warranting a slight upward adjustment from the statistical anchor.","[""Inverted yield curve (YC_SPREAD: -0.41)""]","[""Positive 3-month IPMAN growth (0.22%)""]",up,274d9270fe5c234e7004fd41c3fcd68e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/274d9270fe5c234e7004fd41c3fcd68e
+historical_frequency,Historical frequency,2022-12-01,2023-03-01,0.06578947368421052,0.0,0.004328254847645429,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2022-12-01,2023-03-01,0.07245707691959058,0.0,0.005250027995731466,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2022-12-01,2023-03-01,0.1091018095612526,0.0,0.011903204849539828,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2022-12-01,2023-03-01,0.07245707691959058,0.0,0.005250027995731466,0.07245707691959058,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2022-12-01,2023-03-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but currently above the -2% threshold. An inverted yield curve and restrictive rates suggest future downside, though resilient XLI returns and low unemployment provide a buffer against immediate manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2022-12-01,2023-03-01,0.085,0.0,0.007225000000000001,0.07245707768637308,0.085,0.012542922313626928,0.012542922313626928,0.085,passed,"IPMAN trends remain slightly negative, and the inverted yield curve signals potential economic headwinds. While current stress is low, these factors warrant a modest upward adjustment from the statistical anchor.","[""Inverted yield curve (YC_SPREAD: -0.70) suggesting future economic contraction.""]","[""Low unemployment rate (UNRATE: 3.6) and positive 3-month XLI returns indicate resilient industrial demand.""]",up,,
+adaptive_agent,Adaptive agent,2022-12-01,2023-03-01,0.085,0.0,0.007225000000000001,0.07245707768637308,0.085,0.012542922313626928,0.012542922313626928,0.085,passed,"The 3-month IPMAN contraction is -0.56%, well above the -2% threshold. However, the inverted yield curve (-0.70) and elevated credit spread (2.15) suggest rising macroeconomic risks, warranting a slight upward adjustment from the anchor.","[""Inverted yield curve (-0.70) and elevated credit spread (2.15) indicate heightened macroeconomic risk.""]","[""Current 3-month IPMAN contraction of -0.56% remains significantly above the -2% stress threshold.""]",up,306aef9f53e6a27a25396ea4b7ba24a4,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/306aef9f53e6a27a25396ea4b7ba24a4
+historical_frequency,Historical frequency,2023-01-01,2023-04-01,0.06732348111658457,0.0,0.004532451109655119,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-01-01,2023-04-01,0.07902713221659143,0.0,0.006245287626378622,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-01-01,2023-04-01,0.1717711240053177,0.0,0.029505319042050226,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-01-01,2023-04-01,0.07902713221659143,0.0,0.006245287626378622,0.07902713221659143,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-01-01,2023-04-01,0.28,0.0,0.07840000000000001,,,,,,,"The 3-month IPMAN decline has already breached the -2% threshold, signaling current stress. While XLI performance is resilient, the inverted yield curve and restrictive rates suggest persistent headwinds for manufacturing output over the next quarter.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-01-01,2023-04-01,0.1090271330458525,0.0,0.011886915740198022,0.0790271330458525,0.15,0.07097286695414749,0.03,0.1090271330458525,clamped,"The 3-month IPMAN decline of -2.28% already meets the stress threshold. While labor markets remain tight, the inverted yield curve and negative 12-month equity returns suggest persistent manufacturing headwinds, justifying an upward adjustment from the anchor.","[""IPMAN 3-month change of -2.28% exceeds the -2.0% stress threshold.""]","[""Unemployment rate remains low at 3.5%, indicating resilient labor demand.""]",up,,
+adaptive_agent,Adaptive agent,2023-01-01,2023-04-01,0.1090271330458525,0.0,0.011886915740198022,0.0790271330458525,0.12,0.040972866954147494,0.03,0.1090271330458525,clamped,"The 3-month IPMAN change is -2.28%, breaching the -2% stress threshold. While XLI returns are positive, the contraction in manufacturing output signals elevated near-term stress risk despite stable labor and credit conditions.","[""3-month IPMAN change of -2.28% is below the -2% stress threshold.""]","[""Positive 3-month XLI return and low unemployment rate (3.5%) suggest underlying economic resilience.""]",up,7a0f40da240bc404f8b7b5d13bbe1d86,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/7a0f40da240bc404f8b7b5d13bbe1d86
+historical_frequency,Historical frequency,2023-02-01,2023-05-01,0.06721311475409836,0.0,0.004517602794947595,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-02-01,2023-05-01,0.074044053122862,0.0,0.0054825218028612096,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-02-01,2023-05-01,0.14722569286823273,0.0,0.021675404640531193,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-02-01,2023-05-01,0.074044053122862,0.0,0.0054825218028612096,0.074044053122862,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-02-01,2023-05-01,0.18,0.0,0.0324,,,,,,,"While the 3-month IPMAN trend is negative, recent monthly growth is positive. The inverted yield curve and high inflation suggest caution, but low unemployment and resilient industrial sector returns provide a countervailing buffer against immediate stress.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-02-01,2023-05-01,0.10404405750042511,0.0,0.010825165901151767,0.07404405750042511,0.12,0.04595594249957488,0.03,0.10404405750042511,clamped,"While recent IPMAN shows a slight rebound, the inverted yield curve and negative 3-month IPMAN trend suggest persistent underlying manufacturing weakness, warranting a modest upward adjustment from the base rate.","[""Inverted yield curve (YC_SPREAD: -0.69) indicating recession risk"", ""Negative 3-month IPMAN change (-0.82%)""]","[""Positive 1-month IPMAN growth (1.75%)"", ""Strong XLI 3-month return (8.46%)""]",up,,
+adaptive_agent,Adaptive agent,2023-02-01,2023-05-01,0.072,0.0,0.005183999999999999,0.07404405750042511,0.072,-0.00204405750042512,-0.00204405750042512,0.072,passed,"The 3-month IPMAN contraction of -0.82% remains well above the -2% stress threshold. Recent 1-month growth of 1.75% suggests stabilization, though the inverted yield curve and negative 12-month SPY returns warrant caution.","[""3-month IPMAN contraction of -0.82% is significantly above the -2% stress threshold.""]","[""Inverted yield curve (YC_SPREAD -0.69) and negative 12-month SPY return (-8.19%) indicate potential macroeconomic headwinds.""]",down,eab92234c738879322702a484ff40114,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/eab92234c738879322702a484ff40114
+historical_frequency,Historical frequency,2023-03-01,2023-06-01,0.06710310965630115,0.0,0.004502827325545576,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-03-01,2023-06-01,0.07677106373509754,0.0,0.005893796227018408,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-03-01,2023-06-01,0.12418504059314728,0.0,0.015421924307121637,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-03-01,2023-06-01,0.07677106373509754,0.0,0.005893796227018408,0.07677106373509754,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-03-01,2023-06-01,0.18,0.0,0.0324,,,,,,,"Positive short-term IPMAN momentum offsets the negative 6-month trend. However, the inverted yield curve and restrictive Fed funds rate suggest significant downside risks to manufacturing activity over the three-month horizon.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-03-01,2023-06-01,0.10677106951423866,0.0,0.011400061285214385,0.07677106951423866,0.12,0.04322893048576133,0.03,0.10677106951423866,clamped,"The inverted yield curve and negative equity returns suggest heightened macroeconomic risk. While recent IPMAN growth is positive, the persistent yield curve inversion and tightening financial conditions warrant a moderate upward adjustment to the base probability.","[""Inverted yield curve (YC_SPREAD: -0.89)""]","[""Positive 3-month IPMAN growth (2.73%)""]",up,,
+adaptive_agent,Adaptive agent,2023-03-01,2023-06-01,0.072,0.0,0.005183999999999999,0.07677106951423866,0.072,-0.004771069514238668,-0.004771069514238668,0.072,passed,"The 3-month IPMAN change is positive (+2.73%), well above the -2% stress threshold. Despite a negative yield curve, the recent positive momentum in manufacturing output reduces the immediate risk of hitting the stress threshold.","[""Inverted yield curve (YC_SPREAD -0.89) suggests potential future economic headwinds.""]","[""Positive 3-month IPMAN growth of 2.73% is significantly above the -2% stress threshold.""]",down,e853c3200dac64b978e60d2f2837bb3e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/e853c3200dac64b978e60d2f2837bb3e
+historical_frequency,Historical frequency,2023-04-01,2023-07-01,0.06699346405228758,0.0,0.004488124225725148,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-04-01,2023-07-01,0.07604781140620939,0.0,0.005783269619674391,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-04-01,2023-07-01,0.12560927867889404,0.0,0.015777690890232066,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-04-01,2023-07-01,0.07604781140620939,0.0,0.005783269619674391,0.07604781140620939,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-04-01,2023-07-01,0.18,0.0,0.0324,,,,,,,"Positive 3-month IPMAN momentum offsets recent weakness, but the inverted yield curve and restrictive Fed funds rate suggest rising downside risks to manufacturing activity over the three-month horizon.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-04-01,2023-07-01,0.10604781715876732,0.0,0.011246139524139345,0.07604781715876732,0.12,0.04395218284123267,0.03,0.10604781715876732,clamped,"The 3-month IPMAN trend remains positive, but the inverted yield curve, negative equity returns, and elevated credit spreads suggest rising macroeconomic headwinds that increase the risk of manufacturing contraction over the next quarter.","[""Inverted yield curve (-0.89) and negative 3-month SPY returns indicate significant macroeconomic stress.""]","[""IPMAN 3-month change remains positive at 1.15%, indicating current production levels have not yet met the stress threshold.""]",up,,
+adaptive_agent,Adaptive agent,2023-04-01,2023-07-01,0.072,0.0,0.005183999999999999,0.07604781715876732,0.072,-0.004047817158767328,-0.004047817158767328,0.072,passed,"The 3-month IPMAN change is positive (+1.15%), providing a significant buffer against the -2% stress threshold. While the yield curve is inverted, current manufacturing momentum remains resilient, justifying a slight downward adjustment from the anchor.","[""3-month IPMAN change is positive at +1.15%""]","[""Inverted yield curve (YC_SPREAD -0.89) and negative 12-month SPY return""]",down,b835f144ce3fdce612821599fc6c234a,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/b835f144ce3fdce612821599fc6c234a
+historical_frequency,Historical frequency,2023-05-01,2023-08-01,0.06688417618270799,0.0,0.004473493023639523,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-05-01,2023-08-01,0.07281335910451088,0.0,0.005301785264082457,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-05-01,2023-08-01,0.10334307700395584,0.0,0.010679791564645547,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-05-01,2023-08-01,0.07281335910451088,0.0,0.005301785264082457,0.07281335910451088,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-05-01,2023-08-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is positive over 1-3 months, mitigating immediate stress risk. However, the inverted yield curve and restrictive Fed funds rate suggest persistent underlying macro headwinds, keeping the probability slightly above the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-05-01,2023-08-01,0.085,0.0,0.007225000000000001,0.07281335839811412,0.085,0.012186641601885886,0.012186641601885886,0.085,passed,"IPMAN shows modest recent growth, but the inverted yield curve and elevated credit spreads suggest underlying manufacturing headwinds. The probability is adjusted slightly upward to reflect these persistent macro-financial risks.","[""Inverted yield curve (YC_SPREAD: -0.60) and elevated credit spreads (2.08) indicate potential future manufacturing stress.""]","[""Positive 3-month IPMAN growth (0.35%) and low unemployment rate (3.4%) suggest current manufacturing resilience.""]",up,,
+adaptive_agent,Adaptive agent,2023-05-01,2023-08-01,0.065,0.0,0.0042250000000000005,0.07281335839811412,0.065,-0.007813358398114117,-0.007813358398114117,0.065,passed,"The 3-month IPMAN change is positive (+0.35%), well above the -2% stress threshold. While the yield curve remains inverted, recent IPMAN growth and low unemployment suggest manufacturing stability, warranting a slight downward adjustment from the anchor.","[""3-month IPMAN change is positive at 0.35%""]","[""Inverted yield curve (YC_SPREAD -0.60)""]",down,d261ebf530c00d6ad89474534b69ce1e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/d261ebf530c00d6ad89474534b69ce1e
+historical_frequency,Historical frequency,2023-06-01,2023-09-01,0.06677524429967427,0.0,0.004458933251281181,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-06-01,2023-09-01,0.07315804171639624,0.0,0.005352099067777972,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-06-01,2023-09-01,0.051561444997787476,0.0,0.002658582610259863,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-06-01,2023-09-01,0.07315804171639624,0.0,0.005352099067777972,0.07315804171639624,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-06-01,2023-09-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum remains positive over three months, offsetting recent minor declines. While the inverted yield curve and restrictive rates suggest long-term risk, current industrial data does not yet signal an imminent contraction meeting the threshold.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-06-01,2023-09-01,0.085,0.0,0.007225000000000001,0.07315804065830875,0.085,0.011841959341691258,0.011841959341691258,0.085,passed,"The 3-month IPMAN trend remains positive, but the inverted yield curve and negative 3-month XLI return suggest rising industrial headwinds, warranting a slight upward adjustment from the statistical anchor.","[""Inverted yield curve (YC_SPREAD: -0.76) and negative 3-month XLI return (-3.69%) indicate potential manufacturing sector weakness.""]","[""Positive 3-month IPMAN growth (0.12%) and low unemployment rate (3.6%) suggest current resilience in the manufacturing sector.""]",up,,
+adaptive_agent,Adaptive agent,2023-06-01,2023-09-01,0.065,0.0,0.0042250000000000005,0.07315804065830875,0.065,-0.008158040658308746,-0.008158040658308746,0.065,passed,"The 3-month IPMAN change is positive (+0.116%), indicating no immediate stress. Despite a negative yield curve, the overall macro environment and stable IPMAN trends suggest the probability remains close to the statistical anchor.","[""Positive 3-month IPMAN change of 0.116%.""]","[""Inverted yield curve (YC_SPREAD -0.76) and negative 3-month XLI return.""]",neutral,5fe611c1e78a9c8887211a6e61749201,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/5fe611c1e78a9c8887211a6e61749201
+historical_frequency,Historical frequency,2023-07-01,2023-10-01,0.06666666666666667,0.0,0.0044444444444444444,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-07-01,2023-10-01,0.07241875672216055,0.0,0.005244476325183474,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-07-01,2023-10-01,0.0526650995016098,0.0,0.002773612705514461,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-07-01,2023-10-01,0.07241875672216055,0.0,0.005244476325183474,0.07241875672216055,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-07-01,2023-10-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is currently positive, but the inverted yield curve and negative XLI returns suggest underlying manufacturing weakness. The probability is elevated above the base rate due to restrictive monetary policy and sector-specific headwinds.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-07-01,2023-10-01,0.085,0.0,0.007225000000000001,0.07241875566408298,0.085,0.012581244335917022,0.012581244335917022,0.085,passed,"IPMAN remains stable over three months, but the inverted yield curve and negative XLI performance suggest rising industrial sector headwinds, warranting a slight upward adjustment from the statistical anchor.","[""Inverted yield curve (YC_SPREAD: -0.76) and negative 3-month XLI return (-3.69%) indicate sector-specific weakness.""]","[""IPMAN 3-month change remains positive (0.07%) and unemployment (UNRATE: 3.6%) remains low, suggesting resilience.""]",up,,
+adaptive_agent,Adaptive agent,2023-07-01,2023-10-01,0.065,0.0,0.0042250000000000005,0.07241875566408298,0.065,-0.007418755664082982,-0.007418755664082982,0.065,passed,"The 3-month IPMAN change is positive (0.068%), remaining well above the -2% stress threshold. While the yield curve is inverted, current industrial performance and stable macro indicators suggest low near-term risk of manufacturing stress.","[""3-month IPMAN change is positive at 0.0686%""]","[""Inverted yield curve (YC_SPREAD -0.76) suggests potential long-term economic headwinds""]",neutral,612a686a8549694df1270e85fe348c7a,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/612a686a8549694df1270e85fe348c7a
+historical_frequency,Historical frequency,2023-08-01,2023-11-01,0.06655844155844155,0.0,0.004430026142688479,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-08-01,2023-11-01,0.0668659538026789,0.0,0.004471055777941989,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-08-01,2023-11-01,0.05883699655532837,0.0,0.0034617921636517224,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-08-01,2023-11-01,0.0668659538026789,0.0,0.004471055777941989,0.0668659538026789,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-08-01,2023-11-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is slightly negative but far from the -2% threshold. While the yield curve is inverted and rates are restrictive, strong equity returns and low unemployment suggest resilience, keeping stress probability near the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-08-01,2023-11-01,0.085,0.0,0.007225000000000001,0.066865952681743,0.085,0.018134047318257004,0.018134047318257004,0.085,passed,"The IPMAN 3-month trend is negative, and the inverted yield curve signals persistent economic pressure. However, strong equity returns and low unemployment suggest manufacturing remains resilient, keeping the stress probability only modestly above the historical base rate.","[""Negative 3-month IPMAN change of -0.48% and inverted yield curve (-0.91%)""]","[""Strong 3-month XLI return (10.86%) and low unemployment rate (3.5%)""]",up,,
+adaptive_agent,Adaptive agent,2023-08-01,2023-11-01,0.06,0.0,0.0036,0.066865952681743,0.06,-0.006865952681743004,-0.006865952681743004,0.06,passed,"The 3-month IPMAN contraction of -0.48% remains well above the -2% stress threshold. Recent 1-month growth of 0.4% and strong equity market returns suggest manufacturing stability, justifying a slight downward adjustment from the anchor.","[""3-month IPMAN contraction of -0.48% is significantly above the -2% stress threshold.""]","[""None are material; current macro and IPMAN trends indicate stability.""]",down,b1dabd176e16d20282143c73f2899b8d,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/b1dabd176e16d20282143c73f2899b8d
+historical_frequency,Historical frequency,2023-09-01,2023-12-01,0.06645056726094004,0.0,0.004415677889300715,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-09-01,2023-12-01,0.06689872709129302,0.0,0.004475439686435303,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-09-01,2023-12-01,0.06111909821629524,0.0,0.0037355441667731443,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-09-01,2023-12-01,0.06689872709129302,0.0,0.004475439686435303,0.06689872709129302,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-09-01,2023-12-01,0.25,0.0,0.0625,,,,,,,"IPMAN momentum is negative, but the 3-month decline is decelerating. While the inverted yield curve and high rates suggest macro headwinds, strong equity returns in XLI provide a countervailing signal of resilience.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-09-01,2023-12-01,0.09689873199737796,0.0,0.009389364262699679,0.06689873199737796,0.15,0.08310126800262203,0.03,0.09689873199737796,clamped,"The 3-month IPMAN decline of -0.34% is well above the -2% stress threshold. However, the inverted yield curve and elevated credit spreads suggest underlying economic fragility, warranting a modest upward adjustment from the base rate.","[""Inverted yield curve (-0.76%) and elevated credit spreads (1.83%) indicate potential future manufacturing headwinds.""]","[""Current 3-month IPMAN decline of -0.34% remains significantly above the -2% stress threshold.""]",up,,
+adaptive_agent,Adaptive agent,2023-09-01,2023-12-01,0.06,0.0,0.0036,0.06689873199737796,0.06,-0.006898731997377963,-0.006898731997377963,0.06,passed,"The 3-month IPMAN contraction of -0.34% is well above the -2% stress threshold. Despite inverted yield curves, strong equity returns and stable credit spreads suggest low near-term risk of manufacturing stress.","[""3-month IPMAN contraction of -0.34% is significantly above the -2% stress threshold.""]","[""Inverted yield curve (YC_SPREAD -0.76) indicates potential long-term economic headwinds.""]",down,dd1bb466ab3be0565f695ca5c51cff9d,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/dd1bb466ab3be0565f695ca5c51cff9d
+historical_frequency,Historical frequency,2023-10-01,2024-01-01,0.06634304207119741,0.0,0.00440139923126067,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-10-01,2024-01-01,0.06595034836979292,0.0,0.004349448450097048,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-10-01,2024-01-01,0.05175679549574852,0.0,0.0026787658799887343,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-10-01,2024-01-01,0.06595034836979292,0.0,0.004349448450097048,0.06595034836979292,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-10-01,2024-01-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is positive over 3 months, suggesting resilience. While the yield curve is inverted and rates are restrictive, strong equity returns and low unemployment support a low probability of near-term manufacturing stress.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-10-01,2024-01-01,0.06595035322063442,0.0,0.004349449089926445,0.06595035322063442,0.06595035322063442,0.0,0.0,0.06595035322063442,passed,"IPMAN shows positive momentum over three months, and equity returns (SPY, XLI) are strong. Despite the inverted yield curve, current manufacturing data does not indicate imminent stress.","[""Inverted yield curve (YC_SPREAD: -0.76) suggests long-term economic risk.""]","[""Positive 3-month IPMAN growth (0.39%) and strong 3-month XLI returns (12.2%) indicate current sector health.""]",neutral,,
+adaptive_agent,Adaptive agent,2023-10-01,2024-01-01,0.06,0.0,0.0036,0.06595035322063442,0.06,-0.005950353220634419,-0.005950353220634419,0.06,passed,"The 3-month IPMAN change is positive (+0.39%), far from the -2% stress threshold. Recent growth and stable macro indicators, including positive equity returns, suggest low near-term risk of manufacturing stress.","[""3-month IPMAN change is positive at 0.39%""]","[""None are material""]",down,d3d72fc8460876cbe88f3b7de587360e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/d3d72fc8460876cbe88f3b7de587360e
+historical_frequency,Historical frequency,2023-11-01,2024-02-01,0.06623586429725363,0.0,0.0043871897192041985,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-11-01,2024-02-01,0.07423002557906132,0.0,0.005510096697468098,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-11-01,2024-02-01,0.12880657613277435,0.0,0.016591134055048196,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-11-01,2024-02-01,0.07423002557906132,0.0,0.005510096697468098,0.07423002557906132,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-11-01,2024-02-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but remains well above the -2% stress threshold. While restrictive rates and an inverted yield curve suggest headwinds, the labor market remains resilient, keeping the probability of imminent stress relatively low.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-11-01,2024-02-01,0.1042300279363773,0.0,0.010863898723617992,0.0742300279363773,0.12,0.045769972063622696,0.03,0.1042300279363773,clamped,"IPMAN trends remain weak, and negative 3-month returns in SPY and XLI suggest deteriorating industrial sentiment. While current stress is low, the inverted yield curve and equity weakness increase the risk of a contraction.","[""Negative 3-month returns for both SPY (-8.3%) and XLI (-10.6%) indicate cooling industrial outlooks.""]","[""Current IPMAN 3-month decline of -0.54% remains well above the -2% stress threshold.""]",up,,
+adaptive_agent,Adaptive agent,2023-11-01,2024-02-01,0.085,0.0,0.007225000000000001,0.0742300279363773,0.085,0.010769972063622707,0.010769972063622707,0.085,passed,"The 3-month IPMAN contraction of -0.54% is well above the -2% threshold. However, negative equity returns (SPY -8.3%, XLI -10.6%) and a persistent yield curve inversion suggest rising macroeconomic headwinds, warranting a slight upward adjustment.","[""Inverted yield curve (YC_SPREAD -0.19)""]","[""3-month IPMAN contraction (-0.54%) remains significantly above the -2% stress threshold""]",up,59a03f0f3582bac61822c57d5ea5e02a,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/59a03f0f3582bac61822c57d5ea5e02a
+historical_frequency,Historical frequency,2023-12-01,2024-03-01,0.06612903225806452,0.0,0.004373048907388138,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2023-12-01,2024-03-01,0.0683314034561154,0.0,0.004669180698282419,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2023-12-01,2024-03-01,0.055784475058317184,0.0,0.003111907657532012,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2023-12-01,2024-03-01,0.0683314034561154,0.0,0.004669180698282419,0.0683314034561154,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2023-12-01,2024-03-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative (-1.76%) but approaching the -2% threshold. Restrictive Fed funds and an inverted yield curve suggest ongoing pressure, though low unemployment and stable credit spreads provide some resilience against immediate manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2023-12-01,2024-03-01,0.075,0.0,0.005625,0.06833140646786458,0.075,0.0066685935321354195,0.0066685935321354195,0.075,passed,"The 3-month IPMAN decline is nearing the -2% threshold. While macro indicators like low unemployment and stable credit spreads provide support, the inverted yield curve and negative XLI returns suggest elevated manufacturing sector risk.","[""3-month IPMAN change is -1.76%, approaching the -2% stress threshold.""]","[""Low unemployment rate (3.7%) and stable credit spreads indicate broader economic resilience.""]",up,,
+adaptive_agent,Adaptive agent,2023-12-01,2024-03-01,0.065,0.0,0.0042250000000000005,0.06833140646786458,0.065,-0.0033314064678645755,-0.0033314064678645755,0.065,passed,"The 3-month IPMAN contraction of -1.76% is approaching the -2% threshold, but recent 1-month growth (+0.48%) and stable macro conditions (low VIX, moderate credit spreads) suggest a low probability of imminent stress.","[""3-month IPMAN contraction of -1.76% is nearing the -2% stress threshold.""]","[""Recent 1-month IPMAN growth of 0.48% and stable macro indicators like low VIX (12.92) and moderate credit spreads (1.67).""]",neutral,5580d73673df8a61b6e4acd5a1f33c26,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/5580d73673df8a61b6e4acd5a1f33c26
+historical_frequency,Historical frequency,2024-01-01,2024-04-01,0.06602254428341385,0.0,0.0043589763536553425,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-01-01,2024-04-01,0.0633914173682383,0.0,0.004018471795954185,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-01-01,2024-04-01,0.04967780411243439,0.0,0.002467884221433403,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-01-01,2024-04-01,0.0633914173682383,0.0,0.004018471795954185,0.0633914173682383,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-01-01,2024-04-01,0.28,0.0,0.07840000000000001,,,,,,,"The 3-month IPMAN decline (-6.08%) significantly exceeds the stress threshold. While equity markets and industrial sector returns remain resilient, the inverted yield curve and restrictive rates suggest persistent headwinds for manufacturing output.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-01-01,2024-04-01,0.0933914167174621,0.0,0.008721956716494658,0.0633914167174621,0.15,0.0866085832825379,0.03,0.0933914167174621,clamped,"The 3-month IPMAN decline of 6.08% significantly exceeds the stress threshold. While equity markets and unemployment remain resilient, the persistent contraction in industrial production signals elevated near-term stress risk.","[""IPMAN 3-month percentage change of -6.08% exceeds the -2% stress threshold.""]","[""Strong 3-month returns in SPY (11.6%) and XLI (13.1%) suggest resilient market sentiment.""]",up,,
+adaptive_agent,Adaptive agent,2024-01-01,2024-04-01,0.06,0.0,0.0036,0.0633914167174621,0.06,-0.003391416717462098,-0.003391416717462098,0.06,passed,"The 3-month IPMAN contraction of -6.08% is significantly below the -2% threshold, indicating current stress. However, recent monthly stabilization and strong equity market returns suggest a potential recovery, justifying a slight downward adjustment from the anchor.","[""3-month IPMAN contraction of -6.08% exceeds the -2% stress threshold.""]","[""Strong 3-month SPY and XLI returns suggest positive market sentiment and potential manufacturing recovery.""]",down,440a5fcf15a8951632d83d2ce54cb074,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/440a5fcf15a8951632d83d2ce54cb074
+historical_frequency,Historical frequency,2024-02-01,2024-05-01,0.06591639871382636,0.0,0.00434497161940013,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-02-01,2024-05-01,0.06486932846529433,0.0,0.004208029775538246,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-02-01,2024-05-01,0.12811337411403656,0.0,0.016413036626883093,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-02-01,2024-05-01,0.06486932846529433,0.0,0.004208029775538246,0.06486932846529433,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-02-01,2024-05-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but remains above the -2% threshold. While the inverted yield curve and restrictive rates suggest headwinds, strong equity returns and low unemployment provide a buffer against immediate manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-02-01,2024-05-01,0.085,0.0,0.007225000000000001,0.06486932498836359,0.085,0.020130675011636415,0.020130675011636415,0.085,passed,"IPMAN shows a recent monthly decline of 1.54%, approaching the stress threshold. While macro indicators like equity returns remain positive, the inverted yield curve and weakening momentum suggest a slightly elevated risk of manufacturing stress.","[""IPMAN 1-month decline of 1.54% indicates accelerating downward momentum.""]","[""Strong 3-month and 12-month equity returns (SPY and XLI) suggest resilient market sentiment.""]",up,,
+adaptive_agent,Adaptive agent,2024-02-01,2024-05-01,0.075,0.0,0.005625,0.06486932498836359,0.075,0.010130675011636406,0.010130675011636406,0.075,passed,"The 3-month IPMAN contraction is -1.07%, approaching the -2% threshold. While macro signals remain relatively stable, the recent 1-month decline of -1.54% increases the risk of hitting the stress threshold within the three-month horizon.","[""Recent 1-month IPMAN decline of -1.54%.""]","[""3-month trailing IPMAN contraction of -1.07% remains above the -2% threshold.""]",up,f5ffc6c84f11e0f52d81922a7c1edb1e,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/f5ffc6c84f11e0f52d81922a7c1edb1e
+historical_frequency,Historical frequency,2024-03-01,2024-06-01,0.06581059390048154,0.0,0.004331034269534097,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-03-01,2024-06-01,0.06196823334302948,0.0,0.0038400619436561503,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-03-01,2024-06-01,0.0542965866625309,0.0,0.0029481193232017283,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-03-01,2024-06-01,0.06196823334302948,0.0,0.0038400619436561503,0.06196823334302948,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-03-01,2024-06-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is slightly negative but improving. While the yield curve remains inverted and rates are restrictive, strong equity returns and low credit spreads suggest resilience, keeping the probability near the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-03-01,2024-06-01,0.065,0.0,0.0042250000000000005,0.06196823223449796,0.065,0.0030317677655020414,0.0030317677655020414,0.065,passed,"IPMAN trends remain stable with recent monthly growth. While the yield curve is inverted, strong equity returns and low unemployment suggest manufacturing resilience, keeping stress probability near the historical base rate.","[""Inverted yield curve (YC_SPREAD: -0.39) suggests long-term economic headwinds.""]","[""Positive 1-month IPMAN growth (1.26%) and strong 3-month XLI returns (13.65%) indicate current sector momentum.""]",neutral,,
+adaptive_agent,Adaptive agent,2024-03-01,2024-06-01,0.06,0.0,0.0036,0.06196823223449796,0.06,-0.001968232234497963,-0.001968232234497963,0.06,passed,The 3-month IPMAN contraction of -0.3% is well above the -2% stress threshold. Recent 1-month growth of 1.26% and strong equity market returns suggest a low probability of imminent manufacturing stress.,"[""3-month IPMAN contraction of -0.3% is significantly above the -2% stress threshold.""]","[""None are material.""]",down,f79f31b7663c5b8756d0604defcd12de,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/f79f31b7663c5b8756d0604defcd12de
+historical_frequency,Historical frequency,2024-04-01,2024-07-01,0.06570512820512821,0.0,0.004317163872452334,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-04-01,2024-07-01,0.062456501496347114,0.0,0.0039008145791632094,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-04-01,2024-07-01,0.05197451636195183,0.0,0.0027013503510587983,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-04-01,2024-07-01,0.062456501496347114,0.0,0.0039008145791632094,0.062456501496347114,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-04-01,2024-07-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but improving. While the yield curve remains inverted and rates are restrictive, strong equity returns and low unemployment suggest resilience, keeping the probability of a sharp 2% decline relatively low.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-04-01,2024-07-01,0.085,0.0,0.007225000000000001,0.062456513775247524,0.085,0.022543486224752482,0.022543486224752482,0.085,passed,"The 3-month IPMAN decline is modest, and the inverted yield curve persists. However, strong equity returns and low unemployment suggest resilience, keeping the probability of manufacturing stress low but slightly elevated above the statistical anchor.","[""Inverted yield curve (YC_SPREAD: -0.39) suggests ongoing economic pressure.""]","[""Strong 3-month equity returns (SPY: 10.39%, XLI: 10.84%) and low unemployment (3.9%) indicate resilient demand.""]",up,,
+adaptive_agent,Adaptive agent,2024-04-01,2024-07-01,0.06,0.0,0.0036,0.062456513775247524,0.06,-0.0024565137752475263,-0.0024565137752475263,0.06,passed,"The 3-month IPMAN contraction of -0.16% is well above the -2% stress threshold. Recent 1-month growth and stable macro indicators, including positive equity returns and manageable credit spreads, suggest low near-term risk of manufacturing stress.","[""3-month IPMAN contraction of -0.16% is significantly above the -2% stress threshold.""]","[""None are material.""]",down,9791aab98bc691aa121ec5f76daa8df8,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/9791aab98bc691aa121ec5f76daa8df8
+historical_frequency,Historical frequency,2024-05-01,2024-08-01,0.0656,0.0,0.004303360000000001,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-05-01,2024-08-01,0.06416632583558579,0.0,0.004117317371238565,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-05-01,2024-08-01,0.05576099455356598,0.0,0.0031092885136028148,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-05-01,2024-08-01,0.06416632583558579,0.0,0.004117317371238565,0.06416632583558579,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-05-01,2024-08-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum remains positive over three months, offsetting restrictive rates and yield curve inversion. Low jobless claims and strong equity returns suggest resilience, keeping the probability near the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-05-01,2024-08-01,0.075,0.0,0.005625,0.06416632975716552,0.075,0.010833670242834478,0.010833670242834478,0.075,passed,"The three-month IPMAN trend remains positive, but the inverted yield curve and recent slight monthly IPMAN decline suggest rising risks. The probability is adjusted slightly upward from the anchor to reflect these mixed macroeconomic headwinds.","[""Inverted yield curve (YC_SPREAD: -0.35)""]","[""Positive three-month IPMAN change (0.85%)""]",up,,
+adaptive_agent,Adaptive agent,2024-05-01,2024-08-01,0.06,0.0,0.0036,0.06416632975716552,0.06,-0.004166329757165521,-0.004166329757165521,0.06,passed,"The 3-month IPMAN change is positive (+0.85%), providing a significant buffer from the -2% stress threshold. Macro indicators, including strong XLI returns and stable credit spreads, support a low probability of near-term manufacturing stress.","[""3-month IPMAN change is positive at 0.85%""]","[""None are material""]",down,5209d13b591f0f81eea63f3461a2f826,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/5209d13b591f0f81eea63f3461a2f826
+historical_frequency,Historical frequency,2024-06-01,2024-09-01,0.06549520766773163,0.0,0.004289622227439293,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-06-01,2024-09-01,0.06359252391851163,0.0,0.004044009098326473,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-06-01,2024-09-01,0.05088716000318527,0.0,0.002589503053189779,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-06-01,2024-09-01,0.06359252391851163,0.0,0.004044009098326473,0.06359252391851163,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-06-01,2024-09-01,0.08,0.0,0.0064,,,,,,,"IPMAN momentum is positive over 1 and 3 months, suggesting resilience. While the yield curve remains inverted, strong equity returns and stable labor data mitigate immediate manufacturing stress risks despite restrictive interest rates.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-06-01,2024-09-01,0.065,0.0,0.0042250000000000005,0.06359252782137795,0.065,0.0014074721786220545,0.0014074721786220545,0.065,passed,"IPMAN trends are positive over 1m and 3m horizons, and equity returns (SPY, XLI) remain strong. Despite the inverted yield curve, current manufacturing momentum suggests low near-term stress risk.","[""Positive 1m and 3m IPMAN growth rates indicate current stability.""]","[""Inverted yield curve (YC_SPREAD) remains a persistent long-term recessionary signal.""]",neutral,,
+adaptive_agent,Adaptive agent,2024-06-01,2024-09-01,0.06,0.0,0.0036,0.06359252782137795,0.06,-0.00359252782137795,-0.00359252782137795,0.06,passed,"The 3-month IPMAN change is positive (+0.15%), well above the -2% stress threshold. Recent monthly growth and stable macro indicators, including positive equity returns, suggest low near-term risk of manufacturing stress.","[""3-month IPMAN change is positive at 0.15%""]","[""None are material""]",down,69439c3119b4816da6d77bffeb829bce,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/69439c3119b4816da6d77bffeb829bce
+historical_frequency,Historical frequency,2024-07-01,2024-10-01,0.06539074960127592,0.0,0.004275950133416766,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-07-01,2024-10-01,0.0651550052851173,0.0,0.004245174713703664,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-07-01,2024-10-01,0.04917064309120178,0.0,0.0024177521420023496,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-07-01,2024-10-01,0.0651550052851173,0.0,0.004245174713703664,0.0651550052851173,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-07-01,2024-10-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is slightly negative but remains well above the -2% stress threshold. While the yield curve is inverted and XLI returns are weak, low volatility and stable unemployment suggest manufacturing is not currently entering acute stress.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-07-01,2024-10-01,0.075,0.0,0.005625,0.06515500559110911,0.075,0.00984499440889089,0.00984499440889089,0.075,passed,"IPMAN trends remain stable, well above the -2% stress threshold. While the XLI 3-month return is negative, broader market performance and low volatility suggest limited immediate risk of manufacturing stress.","[""XLI 3-month return is negative at -2.91%""]","[""IPMAN 3-month change is only -0.17%, far from the -2% stress threshold""]",neutral,,
+adaptive_agent,Adaptive agent,2024-07-01,2024-10-01,0.065,0.0,0.0042250000000000005,0.06515500559110911,0.065,-0.00015500559110910583,-0.00015500559110910583,0.065,passed,"The 3-month IPMAN contraction of -0.17% remains well above the -2% stress threshold. While XLI returns are negative, broader macro indicators like the VIX and credit spreads suggest stability, keeping risk near the historical base rate.","[""XLI_RETURN_3M is negative at -2.91%""]","[""3-month IPMAN contraction of -0.17% is significantly above the -2% stress threshold""]",neutral,cf79620883d7667b78a8b6480dea1ad1,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/cf79620883d7667b78a8b6480dea1ad1
+historical_frequency,Historical frequency,2024-08-01,2024-11-01,0.06528662420382166,0.0,0.0042623432999310314,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-08-01,2024-11-01,0.06405422930092945,0.0,0.004102944291336049,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-08-01,2024-11-01,0.0499580018222332,0.0,0.0024958019460702557,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-08-01,2024-11-01,0.06405422930092945,0.0,0.004102944291336049,0.06405422930092945,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-08-01,2024-11-01,0.12,0.0,0.0144,,,,,,,"IPMAN momentum is slightly negative but well above the -2% stress threshold. While the yield curve remains inverted and rates are restrictive, industrial equity performance (XLI) and broader market returns remain resilient, suggesting limited immediate manufacturing contraction.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-08-01,2024-11-01,0.075,0.0,0.005625,0.06405423105699405,0.075,0.010945768943005946,0.010945768943005946,0.075,passed,"IPMAN trends are slightly negative, and the yield curve remains inverted. However, strong equity returns and stable unemployment suggest manufacturing stress remains unlikely in the near term, warranting only a modest upward adjustment from the anchor.","[""Negative 3-month IPMAN change and inverted yield curve signal potential headwinds.""]","[""Strong 3-month and 12-month SPY and XLI returns indicate resilient market sentiment.""]",up,,
+adaptive_agent,Adaptive agent,2024-08-01,2024-11-01,0.064,0.0,0.004096,0.06405423105699405,0.064,-5.4231056994050175e-05,-5.4231056994050175e-05,0.064,passed,"The 3-month IPMAN contraction of -0.41% remains well above the -2% stress threshold. Despite recent slight declines, macroeconomic indicators like positive equity returns and stable credit spreads suggest no immediate systemic manufacturing stress.","[""IPMAN 3-month contraction of -0.41% is significantly above the -2% stress threshold.""]","[""Recent 1-month IPMAN decline of -0.78% indicates short-term weakness.""]",neutral,3ba230a4ed3689c2134268834e723993,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/3ba230a4ed3689c2134268834e723993
+historical_frequency,Historical frequency,2024-09-01,2024-12-01,0.06518282988871224,0.0,0.004248801312300798,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-09-01,2024-12-01,0.06333306883277388,0.0,0.004011077607776875,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-09-01,2024-12-01,0.0471268966794014,0.0,0.0022209443906309745,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-09-01,2024-12-01,0.06333306883277388,0.0,0.004011077607776875,0.06333306883277388,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-09-01,2024-12-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is slightly negative but far from the -2% threshold. While the yield curve remains inverted and rates are restrictive, strong equity returns and stable labor data suggest manufacturing resilience, keeping stress probability near the trailing base rate.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-09-01,2024-12-01,0.075,0.0,0.005625,0.06333307054224994,0.075,0.011666929457750053,0.011666929457750053,0.075,passed,"IPMAN trends remain slightly negative over three months, and the inverted yield curve persists. While recent monthly growth is positive, macroeconomic headwinds suggest a modest increase in stress risk above the statistical anchor.","[""Negative 3-month IPMAN change (-0.49%)"", ""Inverted yield curve (YC_SPREAD: -0.20)""]","[""Positive 1-month IPMAN growth (0.48%)"", ""Strong 3-month and 12-month equity returns (SPY/XLI)""]",up,,
+adaptive_agent,Adaptive agent,2024-09-01,2024-12-01,0.065,0.0,0.0042250000000000005,0.06333307054224994,0.065,0.0016669294577500576,0.0016669294577500576,0.065,passed,"The 3-month IPMAN contraction of -0.49% remains well above the -2% stress threshold. While the yield curve is inverted, recent positive 1-month IPMAN growth and strong equity returns suggest low near-term stress risk.","[""Inverted yield curve (YC_SPREAD -0.20)""]","[""3-month IPMAN contraction (-0.49%) is significantly above the -2% stress threshold""]",neutral,953243839344411cd1cdb2f26ab311bb,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/953243839344411cd1cdb2f26ab311bb
+historical_frequency,Historical frequency,2024-10-01,2025-01-01,0.06507936507936508,0.0,0.004235323759133283,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-10-01,2025-01-01,0.06222052092571956,0.0,0.0038713932242679053,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-10-01,2025-01-01,0.1177087351679802,0.0,0.013855346334845697,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-10-01,2025-01-01,0.06222052092571956,0.0,0.0038713932242679053,0.06222052092571956,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-10-01,2025-01-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but remains well above the -2% stress threshold. Favorable equity returns and a positive yield spread suggest resilience, offsetting concerns from restrictive interest rates and recent slight manufacturing weakness.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-10-01,2025-01-01,0.075,0.0,0.005625,0.06222052444420911,0.075,0.012779475555790884,0.012779475555790884,0.075,passed,"IPMAN trends remain slightly negative, suggesting persistent weakness. However, strong equity returns (SPY/XLI) and stable labor market indicators (UNRATE, ICSA) mitigate immediate recessionary stress risks, keeping the probability of a sharp decline low.","[""IPMAN 3-month percentage change is negative at -0.87 percent.""]","[""Strong 3-month and 12-month returns in the industrial sector (XLI) suggest resilient manufacturing outlook.""]",up,,
+adaptive_agent,Adaptive agent,2024-10-01,2025-01-01,0.065,0.0,0.0042250000000000005,0.06222052444420911,0.065,0.002779475555790889,0.002779475555790889,0.065,passed,"The 3-month IPMAN contraction of -0.87% remains well above the -2% stress threshold. While recent trends are slightly negative, strong equity returns and stable credit spreads suggest limited near-term risk of manufacturing stress.","[""3-month IPMAN contraction of -0.87% is significantly above the -2% stress threshold.""]","[""Positive 3-month and 12-month SPY and XLI returns indicate robust broader market and industrial sector sentiment.""]",neutral,bc7d4ace33a734901877c00eb44691ac,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/bc7d4ace33a734901877c00eb44691ac
+historical_frequency,Historical frequency,2024-11-01,2025-02-01,0.06497622820919176,0.0,0.004221910232292967,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-11-01,2025-02-01,0.06360276379598437,0.0,0.00404531156248778,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-11-01,2025-02-01,0.10292858630418777,0.0,0.010594293878578631,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-11-01,2025-02-01,0.06360276379598437,0.0,0.00404531156248778,0.06360276379598437,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-11-01,2025-02-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but remains well above the -2% stress threshold. Macro signals like low jobless claims and positive equity returns suggest resilience, offsetting the restrictive fed funds rate and narrow yield spread.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-11-01,2025-02-01,0.085,0.0,0.007225000000000001,0.06360276186857768,0.085,0.02139723813142233,0.02139723813142233,0.085,passed,"IPMAN trends remain slightly negative, and elevated VIX suggests heightened market caution. However, positive XLI returns and stable unemployment provide a buffer, keeping the probability of reaching the -2% stress threshold relatively low.","[""IPMAN 3-month change is negative at -0.75 percent, indicating a downward trend.""]","[""XLI 3-month return is positive at 5.01 percent, suggesting resilience in industrial equities.""]",up,,
+adaptive_agent,Adaptive agent,2024-11-01,2025-02-01,0.06,0.0,0.0036,0.06360276186857768,0.06,-0.003602761868577678,-0.003602761868577678,0.06,passed,The 3-month IPMAN contraction of -0.75% remains well above the -2% stress threshold. Macro indicators like stable credit spreads and positive equity returns suggest limited near-term risk of manufacturing stress.,"[""3-month IPMAN contraction of -0.75% is significantly above the -2% stress threshold.""]","[""None are material.""]",down,a05b1965afb025f410e2de7d276541d2,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/a05b1965afb025f410e2de7d276541d2
+historical_frequency,Historical frequency,2024-12-01,2025-03-01,0.06487341772151899,0.0,0.004208560326870694,,,,,,,,[],[],,,
+logistic_c_0_001,Logistic (C=0.001),2024-12-01,2025-03-01,0.06305066526254235,0.0,0.003975386390049165,,,,,,,,[],[],,,
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),2024-12-01,2025-03-01,0.1165977343916893,0.0,0.013595031665274926,,,,,,,,[],[],,,
+hybrid_anchor,Hybrid anchor (logistic),2024-12-01,2025-03-01,0.06305066526254235,0.0,0.003975386390049165,0.06305066526254235,,,,,,,[],[],,,
+analyst_agent,Analyst agent,2024-12-01,2025-03-01,0.18,0.0,0.0324,,,,,,,"IPMAN momentum is negative but decelerating, remaining above the -2% threshold. While the yield curve is nearly flat and rates are restrictive, strong equity returns and low jobless claims suggest resilience, keeping stress probability near the trailing average.",[],[],,,
+hybrid_agent_adjusted,Hybrid agent adjusted,2024-12-01,2025-03-01,0.085,0.0,0.007225000000000001,0.06305066331888698,0.085,0.021949336681113027,0.021949336681113027,0.085,passed,"The three-month IPMAN decline is accelerating toward the stress threshold. While equity returns remain resilient, the narrowing yield spread and persistent manufacturing contraction suggest elevated risk compared to the statistical anchor.","[""IPMAN 3-month change is negative (-0.93%) and trending downward.""]","[""Positive 3-month and 12-month returns in XLI and SPY suggest continued market optimism.""]",up,,
+adaptive_agent,Adaptive agent,2024-12-01,2025-03-01,0.065,0.0,0.0042250000000000005,0.06305066331888698,0.065,0.001949336681113023,0.001949336681113023,0.065,passed,"The 3-month IPMAN contraction of -0.93% remains well above the -2% stress threshold. While recent monthly growth is positive, the narrowing yield spread and elevated VIX suggest caution, keeping the probability slightly above the anchor.","[""3-month IPMAN contraction of -0.93% is significantly above the -2% stress threshold.""]","[""Narrowing yield spread (0.12) and elevated VIX (23.16) indicate potential macroeconomic headwinds.""]",up,3927944c90735c53529838adc5e25bac,https://us.cloud.langfuse.com/project/cmqqnar8v0eboad0cc5f3qinp/traces/3927944c90735c53529838adc5e25bac
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/origin_level_probabilities_wide.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/origin_level_probabilities_wide.csv
new file mode 100644
index 000000000..6bce91a93
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/origin_level_probabilities_wide.csv
@@ -0,0 +1,85 @@
+as_of,forecast_date,outcome,adaptive_agent,analyst_agent,historical_frequency,hybrid_agent_adjusted,hybrid_anchor,logistic_c_0_001,xgb_50_depth2_lr0_03_minchild3_l2_5
+2018-01-01,2018-04-01,0.0,0.058,0.05,0.06375227686703097,0.058,0.05827915949664922,0.05827915949664922,0.03169894590973854
+2018-02-01,2018-05-01,0.0,0.055,0.08,0.06363636363636363,0.065,0.05780021418284348,0.05780021418284348,0.05238844081759453
+2018-03-01,2018-06-01,0.0,0.055,0.05,0.06352087114337568,0.058,0.05796100576794664,0.05796100576794664,0.030501529574394226
+2018-04-01,2018-07-01,0.0,0.065,0.08,0.06340579710144928,0.065,0.061187908035554274,0.061187908035554274,0.030335301533341408
+2018-05-01,2018-08-01,0.0,0.055,0.08,0.06329113924050633,0.085,0.059623951120558974,0.059623951120558974,0.04841608554124832
+2018-06-01,2018-09-01,0.0,0.063,0.08,0.0631768953068592,0.075,0.06336615679077409,0.06336615679077409,0.039699967950582504
+2018-07-01,2018-10-01,0.0,0.055,0.08,0.06306306306306306,0.065,0.058598132173098255,0.058598132173098255,0.0340878926217556
+2018-08-01,2018-11-01,0.0,0.055,0.12,0.06294964028776978,0.065,0.05714126279903641,0.05714126279903641,0.029688911512494087
+2018-09-01,2018-12-01,0.0,0.055,0.05,0.06283662477558348,0.0557,0.05571478817502238,0.05571478817502238,0.02956486865878105
+2018-10-01,2019-01-01,0.0,0.05,0.04,0.06272401433691756,0.055,0.055697593735500046,0.055697593735500046,0.02940630353987217
+2018-11-01,2019-02-01,0.0,0.06,0.12,0.0626118067978533,0.09453510993897833,0.06453510920404289,0.06453510920404289,0.04872920736670494
+2018-12-01,2019-03-01,0.0,0.068,0.12,0.0625,0.09427457278729906,0.06427457203620024,0.06427457203620024,0.08102866262197495
+2019-01-01,2019-04-01,0.0,0.065,0.18,0.062388591800356503,0.09827461237705169,0.06827462369664905,0.06827462369664905,0.0733458548784256
+2019-02-01,2019-05-01,0.0,0.064,0.12,0.06227758007117438,0.085,0.06430843461757971,0.06430843461757971,0.11629296839237213
+2019-03-01,2019-06-01,0.0,0.062,0.12,0.06216696269982238,0.085,0.06206808432780956,0.06206808432780956,0.10655960440635681
+2019-04-01,2019-07-01,0.0,0.065,0.18,0.06205673758865248,0.08,0.058074651480429305,0.058074651480429305,0.07374972105026245
+2019-05-01,2019-08-01,0.0,0.065,0.18,0.061946902654867256,0.085,0.059575988727502575,0.059575988727502575,0.08421660214662552
+2019-06-01,2019-09-01,0.0,0.055,0.18,0.061837455830388695,0.065,0.057075291243421276,0.057075291243421276,0.07815449684858322
+2019-07-01,2019-10-01,0.0,0.055,0.08,0.06172839506172839,0.065,0.057547897883679304,0.057547897883679304,0.030570708215236664
+2019-08-01,2019-11-01,0.0,0.0605,0.12,0.061619718309859156,0.075,0.06051264054082875,0.06051264054082875,0.03667762875556946
+2019-09-01,2019-12-01,0.0,0.056,0.08,0.061511423550087874,0.056,0.05603117750706408,0.05603117750706408,0.027922233566641808
+2019-10-01,2020-01-01,0.0,0.061,0.12,0.06140350877192982,0.085,0.06079020629582093,0.06079020629582093,0.10266361385583878
+2019-11-01,2020-02-01,0.0,0.065,0.12,0.06129597197898424,0.075,0.05884762711069493,0.05884762711069493,0.08151677250862122
+2019-12-01,2020-03-01,1.0,0.052,0.12,0.06118881118881119,0.065,0.05487326237415406,0.05487326237415406,0.0713452696800232
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diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/probability_distribution_bins.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/probability_distribution_bins.csv
new file mode 100644
index 000000000..aee627a67
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/probability_distribution_bins.csv
@@ -0,0 +1,36 @@
+model,model_label,probability_bin,origins
+historical_frequency,Historical frequency,0.0-0.2,84
+historical_frequency,Historical frequency,0.2-0.4,0
+historical_frequency,Historical frequency,0.4-0.6,0
+historical_frequency,Historical frequency,0.6-0.8,0
+historical_frequency,Historical frequency,0.8-1.0,0
+logistic_c_0_001,Logistic (C=0.001),0.0-0.2,82
+logistic_c_0_001,Logistic (C=0.001),0.2-0.4,2
+logistic_c_0_001,Logistic (C=0.001),0.4-0.6,0
+logistic_c_0_001,Logistic (C=0.001),0.6-0.8,0
+logistic_c_0_001,Logistic (C=0.001),0.8-1.0,0
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),0.0-0.2,84
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),0.2-0.4,0
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),0.4-0.6,0
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),0.6-0.8,0
+xgb_50_depth2_lr0_03_minchild3_l2_5,XGBoost (50 trees),0.8-1.0,0
+hybrid_anchor,Hybrid anchor (logistic),0.0-0.2,82
+hybrid_anchor,Hybrid anchor (logistic),0.2-0.4,2
+hybrid_anchor,Hybrid anchor (logistic),0.4-0.6,0
+hybrid_anchor,Hybrid anchor (logistic),0.6-0.8,0
+hybrid_anchor,Hybrid anchor (logistic),0.8-1.0,0
+analyst_agent,Analyst agent,0.0-0.2,74
+analyst_agent,Analyst agent,0.2-0.4,7
+analyst_agent,Analyst agent,0.4-0.6,0
+analyst_agent,Analyst agent,0.6-0.8,1
+analyst_agent,Analyst agent,0.8-1.0,2
+hybrid_agent_adjusted,Hybrid agent adjusted,0.0-0.2,82
+hybrid_agent_adjusted,Hybrid agent adjusted,0.2-0.4,2
+hybrid_agent_adjusted,Hybrid agent adjusted,0.4-0.6,0
+hybrid_agent_adjusted,Hybrid agent adjusted,0.6-0.8,0
+hybrid_agent_adjusted,Hybrid agent adjusted,0.8-1.0,0
+adaptive_agent,Adaptive agent,0.0-0.2,82
+adaptive_agent,Adaptive agent,0.2-0.4,2
+adaptive_agent,Adaptive agent,0.4-0.6,0
+adaptive_agent,Adaptive agent,0.6-0.8,0
+adaptive_agent,Adaptive agent,0.8-1.0,0
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/regime_conditional_brier.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/regime_conditional_brier.csv
new file mode 100644
index 000000000..c006874ae
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/regime_conditional_brier.csv
@@ -0,0 +1,50 @@
+regime_dimension,regime,predictor,n_origins,n_events,event_rate,mean_probability,brier,mean_probability_error,model_label
+momentum_regime,expanding,historical_frequency,35,3,0.08571428571428572,0.06560111945213332,0.0792245589735237,-0.020113166262152416,Historical frequency
+momentum_regime,expanding,logistic_c_0_001,35,3,0.08571428571428572,0.06543866889788003,0.08065718525046155,-0.02027561681640567,Logistic (C=0.001)
+momentum_regime,expanding,xgb_50_depth2_lr0_03_minchild3_l2_5,35,3,0.08571428571428572,0.054925139674118585,0.08421308055664148,-0.030789146040167126,XGBoost (50 trees)
+momentum_regime,expanding,hybrid_anchor,35,3,0.08571428571428572,0.06543866889788003,0.08065718525046155,-0.02027561681640567,Hybrid anchor (logistic)
+momentum_regime,expanding,analyst_agent,35,3,0.08571428571428572,0.09700000000000002,0.08313214285714286,0.011285714285714286,Analyst agent
+momentum_regime,expanding,hybrid_agent_adjusted,35,3,0.08571428571428572,0.07224057095050804,0.0824453555257701,-0.013473714763777673,Hybrid agent adjusted
+momentum_regime,expanding,adaptive_agent,35,3,0.08571428571428572,0.06233142857142857,0.08082923885714287,-0.023382857142857132,Adaptive agent
+momentum_regime,slowing,historical_frequency,43,3,0.06976744186046512,0.0645946465680896,0.06519233680307199,-0.005172795292375528,Historical frequency
+momentum_regime,slowing,logistic_c_0_001,43,3,0.06976744186046512,0.06597082012802619,0.06546533244765217,-0.003796621732438918,Logistic (C=0.001)
+momentum_regime,slowing,xgb_50_depth2_lr0_03_minchild3_l2_5,43,3,0.06976744186046512,0.0791550700141247,0.06803168328942107,0.009387628153659577,XGBoost (50 trees)
+momentum_regime,slowing,hybrid_anchor,43,3,0.06976744186046512,0.06597082012802619,0.06546533244765217,-0.003796621732438918,Hybrid anchor (logistic)
+momentum_regime,slowing,analyst_agent,43,3,0.06976744186046512,0.15488372093023253,0.07370697674418604,0.08511627906976746,Analyst agent
+momentum_regime,slowing,hybrid_agent_adjusted,43,3,0.06976744186046512,0.08532249673586542,0.06618078417016608,0.01555505487540029,Hybrid agent adjusted
+momentum_regime,slowing,adaptive_agent,43,3,0.06976744186046512,0.06734290025964965,0.06459611035268163,-0.0024245416008154643,Adaptive agent
+momentum_regime,stressed,historical_frequency,6,0,0.0,0.06586547042998704,0.004342245317637086,0.06586547042998704,Historical frequency
+momentum_regime,stressed,logistic_c_0_001,6,0,0.0,0.13829721953464513,0.025227803269941586,0.13829721953464513,Logistic (C=0.001)
+momentum_regime,stressed,xgb_50_depth2_lr0_03_minchild3_l2_5,6,0,0.0,0.1778899927934011,0.03183725963937848,0.1778899927934011,XGBoost (50 trees)
+momentum_regime,stressed,hybrid_anchor,6,0,0.0,0.13829721953464513,0.025227803269941586,0.13829721953464513,Hybrid anchor (logistic)
+momentum_regime,stressed,analyst_agent,6,0,0.0,0.5433333333333334,0.35779999999999995,0.5433333333333334,Analyst agent
+momentum_regime,stressed,hybrid_agent_adjusted,6,0,0.0,0.16829721769156594,0.03442563413378523,0.16829721769156594,Hybrid agent adjusted
+momentum_regime,stressed,adaptive_agent,6,0,0.0,0.16829721769156594,0.03442563413378523,0.16829721769156594,Adaptive agent
+financial_regime,restrictive,historical_frequency,43,2,0.046511627906976744,0.06560547855810421,0.044632020273532545,0.019093850651127463,Historical frequency
+financial_regime,restrictive,logistic_c_0_001,43,2,0.046511627906976744,0.07744605905430024,0.047573344678974436,0.030934431147323502,Logistic (C=0.001)
+financial_regime,restrictive,xgb_50_depth2_lr0_03_minchild3_l2_5,43,2,0.046511627906976744,0.09062469508065733,0.05055283427318876,0.0441130671736806,XGBoost (50 trees)
+financial_regime,restrictive,hybrid_anchor,43,2,0.046511627906976744,0.07744605905430024,0.047573344678974436,0.030934431147323502,Hybrid anchor (logistic)
+financial_regime,restrictive,analyst_agent,43,2,0.046511627906976744,0.20558139534883715,0.1013906976744186,0.15906976744186047,Analyst agent
+financial_regime,restrictive,hybrid_agent_adjusted,43,2,0.046511627906976744,0.09632896890754362,0.050367712050567746,0.04981734100056689,Hybrid agent adjusted
+financial_regime,restrictive,adaptive_agent,43,2,0.046511627906976744,0.0812704484841912,0.047736390982015826,0.034758820577214466,Adaptive agent
+financial_regime,supportive,historical_frequency,41,4,0.0975609756097561,0.06457966409351121,0.08982943040852069,-0.0329813115162449,Historical frequency
+financial_regime,supportive,logistic_c_0_001,41,4,0.0975609756097561,0.06406588914155825,0.09131033603509624,-0.03349508646819785,Logistic (C=0.001)
+financial_regime,supportive,xgb_50_depth2_lr0_03_minchild3_l2_5,41,4,0.0975609756097561,0.06089087725594276,0.09487980207357834,-0.03667009835381333,XGBoost (50 trees)
+financial_regime,supportive,hybrid_anchor,41,4,0.0975609756097561,0.06406588914155825,0.09131033603509624,-0.03349508646819785,Hybrid anchor (logistic)
+financial_regime,supportive,analyst_agent,41,4,0.0975609756097561,0.10914634146341462,0.09429329268292683,0.01158536585365854,Analyst agent
+financial_regime,supportive,hybrid_agent_adjusted,41,4,0.0975609756097561,0.0747542679520735,0.0920025207157901,-0.0228067076576826,Hybrid agent adjusted
+financial_regime,supportive,adaptive_agent,41,4,0.0975609756097561,0.06323167640229534,0.0917206121400327,-0.03432929920746076,Adaptive agent
+volatility_regime,elevated,historical_frequency,27,2,0.07407407407407407,0.06588372554324781,0.06856474129963636,-0.008190348530826261,Historical frequency
+volatility_regime,elevated,logistic_c_0_001,27,2,0.07407407407407407,0.08515065187939387,0.07329781301441418,0.011076577805319796,Logistic (C=0.001)
+volatility_regime,elevated,xgb_50_depth2_lr0_03_minchild3_l2_5,27,2,0.07407407407407407,0.10095590201240999,0.07661974051474932,0.026881827938335913,XGBoost (50 trees)
+volatility_regime,elevated,hybrid_anchor,27,2,0.07407407407407407,0.08515065187939387,0.07329781301441418,0.011076577805319796,Hybrid anchor (logistic)
+volatility_regime,elevated,analyst_agent,27,2,0.07407407407407407,0.22629629629629625,0.14278148148148148,0.15222222222222223,Analyst agent
+volatility_regime,elevated,hybrid_agent_adjusted,27,2,0.07407407407407407,0.10651889044326868,0.0764669824547542,0.0324448163691946,Hybrid agent adjusted
+volatility_regime,elevated,adaptive_agent,27,2,0.07407407407407407,0.09214818089883253,0.07365168089732681,0.018074106824758452,Adaptive agent
+volatility_regime,normal,historical_frequency,57,4,0.07017543859649122,0.0647358108099079,0.06580588602493097,-0.005439627786583345,Historical frequency
+volatility_regime,normal,logistic_c_0_001,57,4,0.07017543859649122,0.06417218234026603,0.06684801135518709,-0.00600325625622518,Logistic (C=0.001)
+volatility_regime,normal,xgb_50_depth2_lr0_03_minchild3_l2_5,57,4,0.07017543859649122,0.06434348248468157,0.07008966254150172,-0.005831956111809663,XGBoost (50 trees)
+volatility_regime,normal,hybrid_anchor,57,4,0.07017543859649122,0.06417218234026603,0.06684801135518709,-0.00600325625622518,Hybrid anchor (logistic)
+volatility_regime,normal,analyst_agent,57,4,0.07017543859649122,0.12640350877192982,0.07667938596491226,0.0562280701754386,Analyst agent
+volatility_regime,normal,hybrid_agent_adjusted,57,4,0.07017543859649122,0.07598351942265151,0.0679527445832183,0.005808080826160269,Hybrid agent adjusted
+volatility_regime,normal,adaptive_agent,57,4,0.07017543859649122,0.06314258128150618,0.06709850045158242,-0.007032857314985039,Adaptive agent
diff --git a/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/regime_origin_counts.csv b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/regime_origin_counts.csv
new file mode 100644
index 000000000..92af8da0c
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/compare_all/tables/regime_origin_counts.csv
@@ -0,0 +1,8 @@
+regime_dimension,regime,origins,stress_events,observed_event_rate
+momentum_regime,expanding,35,3.0,0.08571428571428572
+momentum_regime,slowing,43,3.0,0.06976744186046512
+momentum_regime,stressed,6,0.0,0.0
+financial_regime,restrictive,43,2.0,0.046511627906976744
+financial_regime,supportive,41,4.0,0.0975609756097561
+volatility_regime,elevated,27,2.0,0.07407407407407407
+volatility_regime,normal,57,4.0,0.07017543859649122
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/active_feature_target_correlation.png b/implementations/manufacturing_stress_forecasting/reports/data_understanding/active_feature_target_correlation.png
new file mode 100644
index 000000000..a0d5f4da2
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/data_understanding/active_feature_target_correlation.png differ
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/annual_target_rate.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/annual_target_rate.csv
new file mode 100644
index 000000000..29dd504a6
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/annual_target_rate.csv
@@ -0,0 +1,28 @@
+,year,stress_months,observed_months,stress_rate
+0,2000,0.0,12,0.0
+1,2001,0.0,12,0.0
+2,2002,0.0,12,0.0
+3,2003,0.0,12,0.0
+4,2004,0.0,12,0.0
+5,2005,0.0,12,0.0
+6,2006,0.0,12,0.0
+7,2007,0.0,12,0.0
+8,2008,8.0,12,0.6666666666666666
+9,2009,6.0,12,0.5
+10,2010,0.0,12,0.0
+11,2011,0.0,12,0.0
+12,2012,0.0,12,0.0
+13,2013,0.0,12,0.0
+14,2014,0.0,12,0.0
+15,2015,0.0,12,0.0
+16,2016,0.0,12,0.0
+17,2017,0.0,12,0.0
+18,2018,0.0,12,0.0
+19,2019,0.0,12,0.0
+20,2020,4.0,12,0.3333333333333333
+21,2021,1.0,12,0.08333333333333333
+22,2022,1.0,12,0.08333333333333333
+23,2023,0.0,12,0.0
+24,2024,0.0,12,0.0
+25,2025,0.0,12,0.0
+26,2026,0.0,8,0.0
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/availability_audit.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/availability_audit.csv
new file mode 100644
index 000000000..ed587067c
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/availability_audit.csv
@@ -0,0 +1,16 @@
+,series_id,origin,latest_visible_reference_month,latest_visible_release_date,snapshot_value
+0,ipman_change_1m_pct,2026-10-01,2026-08-01,2026-09-01,-0.30061813850166264
+1,ipman_change_3m_pct,2026-10-01,2026-08-01,2026-09-01,0.1180041139790422
+2,ipman_change_6m_pct,2026-10-01,2026-08-01,2026-09-01,1.3950252217777015
+3,FEDFUNDS,2026-10-01,2026-08-01,2026-08-03,3.63
+4,YC_SPREAD,2026-10-01,2026-09-01,2026-09-22,0.20000000000000018
+5,CPIAUCSL,2026-10-01,2026-08-01,2026-08-03,334.131
+6,CPI_YOY,2026-10-01,2026-08-01,2026-08-03,3.7129581058388483
+7,UNRATE,2026-10-01,2026-08-01,2026-08-03,4.1
+8,ICSA,2026-10-01,2026-09-01,2026-09-14,196000.0
+9,VIXCLS,2026-10-01,2026-09-01,2026-09-23,14.21
+10,CREDIT_SPREAD,2026-10-01,2026-09-01,2026-10-01,1.47
+11,SPY_RETURN_3M,2026-10-01,2026-09-01,2026-10-01,2.37740595421152
+12,SPY_RETURN_12M,2026-10-01,2026-09-01,2026-10-01,15.713135389757271
+13,XLI_RETURN_3M,2026-10-01,2026-09-01,2026-10-01,-9.610335438366956
+14,XLI_RETURN_12M,2026-10-01,2026-09-01,2026-10-01,9.504512938016262
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/coverage.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/coverage.csv
new file mode 100644
index 000000000..30d4c395e
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/coverage.csv
@@ -0,0 +1,20 @@
+,series_id,category,observations,missing_values,missing_rate,reference_start,reference_end,first_release,last_release
+0,CPIAUCSL,Active macro or market feature,955,0,0.0,1947-01-01,2026-08-01,1947-01-02,2026-08-03
+1,CPI_YOY,Active macro or market feature,943,0,0.0,1948-01-01,2026-08-01,1948-01-02,2026-08-03
+2,CREDIT_SPREAD,Active macro or market feature,489,0,0.0,1986-01-01,2026-09-01,1986-02-03,2026-10-01
+3,FEDFUNDS,Active macro or market feature,866,0,0.0,1954-07-01,2026-08-01,1954-07-02,2026-08-03
+4,ICSA,Active macro or market feature,717,0,0.0,1967-01-01,2026-09-01,1967-01-30,2026-09-14
+5,SPY_RETURN_12M,Active macro or market feature,334,0,0.0,1999-01-01,2026-10-01,1999-02-01,2026-10-05
+6,SPY_RETURN_3M,Active macro or market feature,343,0,0.0,1998-04-01,2026-10-01,1998-05-01,2026-10-05
+7,UNRATE,Active macro or market feature,943,0,0.0,1948-01-01,2026-08-01,1948-01-02,2026-08-03
+8,VIXCLS,Active macro or market feature,441,0,0.0,1990-01-01,2026-09-01,1990-02-01,2026-09-23
+9,XLI_RETURN_12M,Active macro or market feature,323,0,0.0,1999-12-01,2026-10-01,2000-01-03,2026-10-05
+10,XLI_RETURN_3M,Active macro or market feature,332,0,0.0,1999-03-01,2026-10-01,1999-04-01,2026-10-05
+11,YC_SPREAD,Active macro or market feature,604,0,0.0,1976-06-01,2026-09-01,1976-07-01,2026-09-22
+17,manufacturing_stress,Binary target,653,0,0.0,1972-04-01,2026-08-01,1972-05-01,2026-09-01
+16,ipman_us_manufacturing_production,IPMAN level,656,0,0.0,1972-01-01,2026-08-01,1972-02-01,2026-09-01
+13,ipman_change_1m_pct,IPMAN momentum feature,655,0,0.0,1972-02-01,2026-08-01,1972-03-01,2026-09-01
+14,ipman_change_3m_pct,IPMAN momentum feature,653,0,0.0,1972-04-01,2026-08-01,1972-05-01,2026-09-01
+15,ipman_change_6m_pct,IPMAN momentum feature,650,0,0.0,1972-07-01,2026-08-01,1972-08-01,2026-09-01
+12,fed_funds_rate_pct,Registered macro or market input,866,0,0.0,1954-07-01,2026-08-01,1954-07-02,2026-08-03
+18,treasury_10y_minus_2y_pct_points,Registered macro or market input,604,0,0.0,1976-06-01,2026-09-01,1976-07-01,2026-09-22
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/cutoff_safe_release_availability.png b/implementations/manufacturing_stress_forecasting/reports/data_understanding/cutoff_safe_release_availability.png
new file mode 100644
index 000000000..dbac42467
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/data_understanding/cutoff_safe_release_availability.png differ
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/feature_and_target_history.png b/implementations/manufacturing_stress_forecasting/reports/data_understanding/feature_and_target_history.png
new file mode 100644
index 000000000..06266b4a9
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/data_understanding/feature_and_target_history.png differ
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/feature_coverage.png b/implementations/manufacturing_stress_forecasting/reports/data_understanding/feature_coverage.png
new file mode 100644
index 000000000..7304848cf
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/data_understanding/feature_coverage.png differ
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/lag_correlations.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/lag_correlations.csv
new file mode 100644
index 000000000..460307d1d
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/lag_correlations.csv
@@ -0,0 +1,1111 @@
+,series_id,outcome,lag_months,overlapping_months,pearson_correlation,spearman_correlation
+0,ipman_change_1m_pct,target,-18,302,0.05918288049011979,0.10095968072053678
+1,ipman_change_1m_pct,ipman_change_3m,-18,302,-0.10617549857837463,-0.19896108332093468
+2,ipman_change_1m_pct,target,-17,303,0.07929506262843117,0.14603788405196746
+3,ipman_change_1m_pct,ipman_change_3m,-17,303,-0.0723193741883081,-0.185917058070394
+4,ipman_change_1m_pct,target,-16,304,0.06558084562106507,0.10447761947118442
+5,ipman_change_1m_pct,ipman_change_3m,-16,304,-0.04061453983044364,-0.12725292373931094
+6,ipman_change_1m_pct,target,-15,305,0.07508144801771686,0.12411081819523666
+7,ipman_change_1m_pct,ipman_change_3m,-15,305,-0.02333643574296575,-0.06525402223011724
+8,ipman_change_1m_pct,target,-14,306,0.10450580370530942,0.17722508919643998
+9,ipman_change_1m_pct,ipman_change_3m,-14,306,-0.05672541787701753,-0.027595890174300083
+10,ipman_change_1m_pct,target,-13,307,0.09681495006028663,0.17976530479170336
+11,ipman_change_1m_pct,ipman_change_3m,-13,307,-0.06804505951751716,-0.09369611036597049
+12,ipman_change_1m_pct,target,-12,308,0.13137121494972656,0.1843534775020024
+13,ipman_change_1m_pct,ipman_change_3m,-12,308,-0.05357771297361804,-0.0834070897320004
+14,ipman_change_1m_pct,target,-11,309,0.05844103248313492,0.12327550096486012
+15,ipman_change_1m_pct,ipman_change_3m,-11,309,-0.05452662766907028,-0.06347489028353419
+16,ipman_change_1m_pct,target,-10,310,0.05315984802241153,0.09082550913438142
+17,ipman_change_1m_pct,ipman_change_3m,-10,310,0.03290749277216477,0.014517488517385801
+18,ipman_change_1m_pct,target,-9,311,0.01266751444450551,0.07782124882479673
+19,ipman_change_1m_pct,ipman_change_3m,-9,311,0.04588947989502998,0.031773355780201545
+20,ipman_change_1m_pct,target,-8,312,-0.02092813360640002,0.04939815781061854
+21,ipman_change_1m_pct,ipman_change_3m,-8,312,0.037082351975375835,0.07594833179107344
+22,ipman_change_1m_pct,target,-7,313,-0.014986691342219353,0.03946956509201561
+23,ipman_change_1m_pct,ipman_change_3m,-7,313,-0.03829589856744754,0.00012914173187671897
+24,ipman_change_1m_pct,target,-6,314,-0.056043963273787335,-0.04186658170054719
+25,ipman_change_1m_pct,ipman_change_3m,-6,314,0.011623133056746693,0.13926282097869977
+26,ipman_change_1m_pct,target,-5,315,-0.08468022895858757,-0.016035196066461296
+27,ipman_change_1m_pct,ipman_change_3m,-5,315,0.07691330659244816,0.15692017676215048
+28,ipman_change_1m_pct,target,-4,316,-0.04430609425219205,-0.03405201081135354
+29,ipman_change_1m_pct,ipman_change_3m,-4,316,0.07239045816320686,0.2409360661566867
+30,ipman_change_1m_pct,target,-3,317,-0.015018174766125776,-0.06933446333415151
+31,ipman_change_1m_pct,ipman_change_3m,-3,317,-0.0002052367171411223,0.2062335801289706
+32,ipman_change_1m_pct,target,-2,318,0.0072270151067657645,-0.07887771632896462
+33,ipman_change_1m_pct,ipman_change_3m,-2,318,-0.09155659129930706,0.221755907573049
+34,ipman_change_1m_pct,target,-1,319,-0.13542502785713567,-0.12231216031579063
+35,ipman_change_1m_pct,ipman_change_3m,-1,319,-0.020203590931094015,0.17819727036138877
+36,ipman_change_1m_pct,target,0,320,-0.35323010152665596,-0.29753824808308965
+37,ipman_change_1m_pct,ipman_change_3m,0,320,0.5337656389885242,0.6034563325813729
+38,ipman_change_1m_pct,target,1,319,-0.36545506581868115,-0.26807567628340334
+39,ipman_change_1m_pct,ipman_change_3m,1,319,0.7131292224820325,0.551374061039806
+40,ipman_change_1m_pct,target,2,318,-0.4077108301221437,-0.2858788073031884
+41,ipman_change_1m_pct,ipman_change_3m,2,318,0.5887702833188263,0.6354146706075676
+42,ipman_change_1m_pct,target,3,317,-0.24958107218721243,-0.26145756725598235
+43,ipman_change_1m_pct,ipman_change_3m,3,317,-0.020792139205753814,0.19759071001650746
+44,ipman_change_1m_pct,target,4,316,-0.1775253208476956,-0.2000377538876166
+45,ipman_change_1m_pct,ipman_change_3m,4,316,-0.10677120032440761,0.21753098955510808
+46,ipman_change_1m_pct,target,5,315,-0.1956633477847994,-0.2419596549314249
+47,ipman_change_1m_pct,ipman_change_3m,5,315,-0.006035709868271211,0.23321700203099865
+48,ipman_change_1m_pct,target,6,314,-0.1640204518688889,-0.2339349204298616
+49,ipman_change_1m_pct,ipman_change_3m,6,314,0.07096389418826457,0.21078102757759257
+50,ipman_change_1m_pct,target,7,313,-0.08970463706567465,-0.17060105058087335
+51,ipman_change_1m_pct,ipman_change_3m,7,313,0.07423916682748323,0.12907324762496614
+52,ipman_change_1m_pct,target,8,312,-0.04057646853689964,-0.19613974424804423
+53,ipman_change_1m_pct,ipman_change_3m,8,312,0.014042980060317385,0.11685285734267171
+54,ipman_change_1m_pct,target,9,311,-0.045694974824687214,-0.11957899209663887
+55,ipman_change_1m_pct,ipman_change_3m,9,311,-0.03727728132053798,-0.012745645600121277
+56,ipman_change_1m_pct,target,10,310,-0.19829230912331935,-0.1231059808784265
+57,ipman_change_1m_pct,ipman_change_3m,10,310,0.02330997574913015,0.050852598580294714
+58,ipman_change_1m_pct,target,11,309,-0.1090563079486996,-0.1782776084527702
+59,ipman_change_1m_pct,ipman_change_3m,11,309,0.05187176743486684,0.0517784439174981
+60,ipman_change_1m_pct,target,12,308,-0.02757488397297325,-0.08150676256117469
+61,ipman_change_1m_pct,ipman_change_3m,12,308,0.03639322580799435,0.028182816203435414
+62,ipman_change_1m_pct,target,13,307,-0.02672925603258959,-0.08519118006698784
+63,ipman_change_1m_pct,ipman_change_3m,13,307,-0.05483933437831835,-0.03666713946461025
+64,ipman_change_1m_pct,target,14,306,-0.016458932987728162,-0.061519857820723674
+65,ipman_change_1m_pct,ipman_change_3m,14,306,-0.053034899076378976,-0.06266070693776522
+66,ipman_change_1m_pct,target,15,305,-0.01286872795156551,-0.04347639570717987
+67,ipman_change_1m_pct,ipman_change_3m,15,305,-0.06681345218113942,-0.08803650882268352
+68,ipman_change_1m_pct,target,16,304,0.00267066004369056,-0.02086528435169819
+69,ipman_change_1m_pct,ipman_change_3m,16,304,-0.057450245567915365,-0.03125005339187931
+70,ipman_change_1m_pct,target,17,303,0.009715298546907586,0.005622686482750047
+71,ipman_change_1m_pct,ipman_change_3m,17,303,-0.02701802556090927,-0.0555032629439312
+72,ipman_change_1m_pct,target,18,302,0.03708591264087289,0.05498560523357525
+73,ipman_change_1m_pct,ipman_change_3m,18,302,-0.03955565414680773,-0.08479875291658888
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+688,VIXCLS,target,-7,314,0.3663310447012248,0.29651979040106946
+689,VIXCLS,ipman_change_3m,-7,314,-0.15760327326549628,-0.11538044854960046
+690,VIXCLS,target,-6,315,0.3762277655811869,0.2863435564566893
+691,VIXCLS,ipman_change_3m,-6,315,-0.16275334229395003,-0.0828920346632813
+692,VIXCLS,target,-5,316,0.4154028054109751,0.32285381552715475
+693,VIXCLS,ipman_change_3m,-5,316,-0.2202688722776221,-0.12838067045287413
+694,VIXCLS,target,-4,317,0.4414620945894445,0.30817761440948127
+695,VIXCLS,ipman_change_3m,-4,317,-0.22695031126494536,-0.11847483811804461
+696,VIXCLS,target,-3,318,0.4523616836616457,0.3243308076679576
+697,VIXCLS,ipman_change_3m,-3,318,-0.19782388251335092,-0.1367906305522615
+698,VIXCLS,target,-2,319,0.45336950554272576,0.3281794314019867
+699,VIXCLS,ipman_change_3m,-2,319,-0.2087465193738323,-0.10438807033126962
+700,VIXCLS,target,-1,320,0.44952245384710926,0.31430967431284385
+701,VIXCLS,ipman_change_3m,-1,320,-0.24229414076333503,-0.10458436785430285
+702,VIXCLS,target,0,320,0.5166819806390913,0.3442173089517716
+703,VIXCLS,ipman_change_3m,0,320,-0.2907173373613759,-0.09935297963483235
+704,VIXCLS,target,1,319,0.5332954638071139,0.3404668512769006
+705,VIXCLS,ipman_change_3m,1,319,-0.3495192900968617,-0.11422518340542728
+706,VIXCLS,target,2,318,0.49822260652750344,0.31621724602301193
+707,VIXCLS,ipman_change_3m,2,318,-0.34056461904006435,-0.11690147848598559
+708,VIXCLS,target,3,317,0.45120627771785765,0.27776397675886805
+709,VIXCLS,ipman_change_3m,3,317,-0.20404979691811842,-0.09113933873778918
+710,VIXCLS,target,4,316,0.3665318850009091,0.23615626543742382
+711,VIXCLS,ipman_change_3m,4,316,-0.0010312733099116875,-0.04567241368505442
+712,VIXCLS,target,5,315,0.26118225501184605,0.18125543644204753
+713,VIXCLS,ipman_change_3m,5,315,0.10664384416951886,-0.014276919485720602
+714,VIXCLS,target,6,314,0.2253820279694512,0.16315057960336593
+715,VIXCLS,ipman_change_3m,6,314,0.07671510748557261,0.004871126116035433
+716,VIXCLS,target,7,313,0.1970574886130596,0.16004730924547295
+717,VIXCLS,ipman_change_3m,7,313,0.025851363793912414,-0.007899775596148703
+718,VIXCLS,target,8,312,0.1713068889763144,0.17260338412638074
+719,VIXCLS,ipman_change_3m,8,312,-0.013039869408937633,-0.028506720107298374
+720,VIXCLS,target,9,311,0.07762338457829812,0.14045818592842832
+721,VIXCLS,ipman_change_3m,9,311,0.018209592482078936,-0.03571015029305917
+722,VIXCLS,target,10,310,0.0695620357982987,0.13777924169938216
+723,VIXCLS,ipman_change_3m,10,310,0.047098446738394,-0.004601716761807944
+724,VIXCLS,target,11,309,0.08256675689547463,0.10852988146498906
+725,VIXCLS,ipman_change_3m,11,309,0.04741031951752663,0.0069990930830153165
+726,VIXCLS,target,12,308,0.03356015791555041,0.07602375988844427
+727,VIXCLS,ipman_change_3m,12,308,0.049823369808792504,0.017703631030996838
+728,VIXCLS,target,13,307,-0.003993754666676036,0.04661696319850156
+729,VIXCLS,ipman_change_3m,13,307,0.07372554018482204,0.05705129527780155
+730,VIXCLS,target,14,306,-0.03955820666473008,-0.005388613661809229
+731,VIXCLS,ipman_change_3m,14,306,0.1326188976629611,0.14110540254117376
+732,VIXCLS,target,15,305,-0.028380661156365745,0.0030087541067680477
+733,VIXCLS,ipman_change_3m,15,305,0.12190218222328347,0.16798940260484402
+734,VIXCLS,target,16,304,-0.04660632493784888,-0.016404990366589926
+735,VIXCLS,ipman_change_3m,16,304,0.0842799411561539,0.18401063227076359
+736,VIXCLS,target,17,303,-0.0554305138738913,-0.02241481686223221
+737,VIXCLS,ipman_change_3m,17,303,0.07154607394622081,0.17196583563684262
+738,VIXCLS,target,18,302,-0.0864275067514987,-0.07247425611864035
+739,VIXCLS,ipman_change_3m,18,302,0.08108808247567492,0.16987543949126657
+740,CREDIT_SPREAD,target,-18,303,0.022724725905696862,0.061395604469361556
+741,CREDIT_SPREAD,ipman_change_3m,-18,303,0.013293535622804538,0.013759240688368778
+742,CREDIT_SPREAD,target,-17,304,0.031036612865073452,0.07386242521819877
+743,CREDIT_SPREAD,ipman_change_3m,-17,304,-0.009910460036573285,-0.019894445188315256
+744,CREDIT_SPREAD,target,-16,305,0.0311427344100642,0.07514586659839126
+745,CREDIT_SPREAD,ipman_change_3m,-16,305,-0.03133909840825251,-0.04998320318745685
+746,CREDIT_SPREAD,target,-15,306,0.029934317846359103,0.07454468226332903
+747,CREDIT_SPREAD,ipman_change_3m,-15,306,-0.045487994294632635,-0.06705034082362153
+748,CREDIT_SPREAD,target,-14,307,0.03828912065570891,0.07737450641388605
+749,CREDIT_SPREAD,ipman_change_3m,-14,307,-0.049607207997088336,-0.06506569043734832
+750,CREDIT_SPREAD,target,-13,308,0.05014197377491422,0.07891583697883058
+751,CREDIT_SPREAD,ipman_change_3m,-13,308,-0.057271326056573924,-0.0649804296730916
+752,CREDIT_SPREAD,target,-12,309,0.08494182817809284,0.09946401043618165
+753,CREDIT_SPREAD,ipman_change_3m,-12,309,-0.07533755559859363,-0.08462341477174017
+754,CREDIT_SPREAD,target,-11,310,0.11520803369792897,0.10293386836006463
+755,CREDIT_SPREAD,ipman_change_3m,-11,310,-0.08567876220905181,-0.08100828177463627
+756,CREDIT_SPREAD,target,-10,311,0.14890088155121853,0.11958265358610076
+757,CREDIT_SPREAD,ipman_change_3m,-10,311,-0.10397102539887243,-0.08594747455564856
+758,CREDIT_SPREAD,target,-9,312,0.21190922833908513,0.141515498754206
+759,CREDIT_SPREAD,ipman_change_3m,-9,312,-0.12074093710744946,-0.07935346396433068
+760,CREDIT_SPREAD,target,-8,313,0.2705771781121816,0.16612418073439625
+761,CREDIT_SPREAD,ipman_change_3m,-8,313,-0.149680476470983,-0.08595307379836306
+762,CREDIT_SPREAD,target,-7,314,0.3336160468332677,0.19567100212340857
+763,CREDIT_SPREAD,ipman_change_3m,-7,314,-0.18043496219276306,-0.09857877961286277
+764,CREDIT_SPREAD,target,-6,315,0.3939160333050837,0.23215934469988894
+765,CREDIT_SPREAD,ipman_change_3m,-6,315,-0.22699263000030456,-0.13603259453054287
+766,CREDIT_SPREAD,target,-5,316,0.4427853198587291,0.2675085641583444
+767,CREDIT_SPREAD,ipman_change_3m,-5,316,-0.2698737789453895,-0.17080671649391355
+768,CREDIT_SPREAD,target,-4,317,0.47707950759092466,0.2750065053569034
+769,CREDIT_SPREAD,ipman_change_3m,-4,317,-0.29221678534457923,-0.17948868886794517
+770,CREDIT_SPREAD,target,-3,318,0.5039034667135512,0.29385995998511655
+771,CREDIT_SPREAD,ipman_change_3m,-3,318,-0.3035959998012159,-0.17735326613967015
+772,CREDIT_SPREAD,target,-2,319,0.5100657623359914,0.3000317000116499
+773,CREDIT_SPREAD,ipman_change_3m,-2,319,-0.3194544110335134,-0.17727830700024744
+774,CREDIT_SPREAD,target,-1,320,0.5244167200832474,0.3149469353425525
+775,CREDIT_SPREAD,ipman_change_3m,-1,320,-0.3417134093002619,-0.19228325037835534
+776,CREDIT_SPREAD,target,0,320,0.5516751617126009,0.33528217868587923
+777,CREDIT_SPREAD,ipman_change_3m,0,320,-0.35812612032919267,-0.20063904535567706
+778,CREDIT_SPREAD,target,1,319,0.5409821417607552,0.3266436930601839
+779,CREDIT_SPREAD,ipman_change_3m,1,319,-0.354922836605398,-0.19115347165472493
+780,CREDIT_SPREAD,target,2,318,0.50225811406186,0.3019739376068056
+781,CREDIT_SPREAD,ipman_change_3m,2,318,-0.28789227603605205,-0.17001779978974155
+782,CREDIT_SPREAD,target,3,317,0.44880024018448045,0.27153284545672257
+783,CREDIT_SPREAD,ipman_change_3m,3,317,-0.18725403197158688,-0.13786238669124856
+784,CREDIT_SPREAD,target,4,316,0.36906852816104235,0.23338452448122107
+785,CREDIT_SPREAD,ipman_change_3m,4,316,-0.07518841463282472,-0.08719897761570067
+786,CREDIT_SPREAD,target,5,315,0.330393052328914,0.22607461754181127
+787,CREDIT_SPREAD,ipman_change_3m,5,315,-0.04162721449279797,-0.04987964331722301
+788,CREDIT_SPREAD,target,6,314,0.27027485313670924,0.195095707147302
+789,CREDIT_SPREAD,ipman_change_3m,6,314,-0.03559076048265703,-0.012787508913050446
+790,CREDIT_SPREAD,target,7,313,0.21245255025304777,0.1763173685300081
+791,CREDIT_SPREAD,ipman_change_3m,7,313,-0.021783562101093484,0.019111422287873627
+792,CREDIT_SPREAD,target,8,312,0.14467033715722036,0.1500879707428908
+793,CREDIT_SPREAD,ipman_change_3m,8,312,0.006588396432218791,0.03823595190539484
+794,CREDIT_SPREAD,target,9,311,0.08090688672131949,0.11753864634187744
+795,CREDIT_SPREAD,ipman_change_3m,9,311,0.04633345577973132,0.060123947787539475
+796,CREDIT_SPREAD,target,10,310,0.054012267934007256,0.09713796524184773
+797,CREDIT_SPREAD,ipman_change_3m,10,310,0.06613207421422618,0.07713017458034516
+798,CREDIT_SPREAD,target,11,309,0.03084389069764552,0.05323428076022657
+799,CREDIT_SPREAD,ipman_change_3m,11,309,0.08130298061510047,0.10561910413184081
+800,CREDIT_SPREAD,target,12,308,-0.024287899360828903,-0.003038071355643371
+801,CREDIT_SPREAD,ipman_change_3m,12,308,0.11143988708196971,0.14993120944784152
+802,CREDIT_SPREAD,target,13,307,-0.05647215011615448,-0.04982049193184717
+803,CREDIT_SPREAD,ipman_change_3m,13,307,0.14554444031303365,0.20776289545934803
+804,CREDIT_SPREAD,target,14,306,-0.08842625838143275,-0.09250707226050639
+805,CREDIT_SPREAD,ipman_change_3m,14,306,0.18230392005678056,0.25976649364976034
+806,CREDIT_SPREAD,target,15,305,-0.10392897283134653,-0.11313226465678501
+807,CREDIT_SPREAD,ipman_change_3m,15,305,0.1779614704266728,0.2681232565985793
+808,CREDIT_SPREAD,target,16,304,-0.1331327524274929,-0.14810260365162775
+809,CREDIT_SPREAD,ipman_change_3m,16,304,0.18016804632364233,0.27196311505116294
+810,CREDIT_SPREAD,target,17,303,-0.16633573946441893,-0.19231688153802376
+811,CREDIT_SPREAD,ipman_change_3m,17,303,0.1856096659170318,0.2663106828323873
+812,CREDIT_SPREAD,target,18,302,-0.20125533252455943,-0.24217312910832003
+813,CREDIT_SPREAD,ipman_change_3m,18,302,0.20423965358160626,0.26776009091288683
+814,SPY_RETURN_3M,target,-18,304,0.03916970840823535,0.03719463732259243
+815,SPY_RETURN_3M,ipman_change_3m,-18,304,-0.09004016873976449,-0.15799511357520565
+816,SPY_RETURN_3M,target,-17,305,0.053107592167098254,0.04844082843498934
+817,SPY_RETURN_3M,ipman_change_3m,-17,305,-0.020470735600374042,-0.077972051633423
+818,SPY_RETURN_3M,target,-16,306,0.010176552826189197,0.031732870700713914
+819,SPY_RETURN_3M,ipman_change_3m,-16,306,-0.01475488620885924,-0.03068003941043497
+820,SPY_RETURN_3M,target,-15,307,0.06496690416665296,0.07282952281950533
+821,SPY_RETURN_3M,ipman_change_3m,-15,307,-0.04936929028555716,-0.07392071427327224
+822,SPY_RETURN_3M,target,-14,308,0.08643178493892349,0.10240213259958493
+823,SPY_RETURN_3M,ipman_change_3m,-14,308,-0.07075009629778554,-0.10592295026689891
+824,SPY_RETURN_3M,target,-13,309,0.1575199948638914,0.15114520154184405
+825,SPY_RETURN_3M,ipman_change_3m,-13,309,-0.07548595979285254,-0.07846913499904418
+826,SPY_RETURN_3M,target,-12,310,0.15927775777551675,0.1784229710943583
+827,SPY_RETURN_3M,ipman_change_3m,-12,310,-0.0635542609928479,-0.031494335982147445
+828,SPY_RETURN_3M,target,-11,311,0.1761794072212775,0.1632348146081102
+829,SPY_RETURN_3M,ipman_change_3m,-11,311,-0.059941209257123484,-0.03398546272729449
+830,SPY_RETURN_3M,target,-10,312,0.188700507475354,0.20020782783244812
+831,SPY_RETURN_3M,ipman_change_3m,-10,312,-0.059951781348183374,-0.08074501030851257
+832,SPY_RETURN_3M,target,-9,313,0.145796882954401,0.14183019544053962
+833,SPY_RETURN_3M,ipman_change_3m,-9,313,-0.05726876507145344,-0.048425801422280944
+834,SPY_RETURN_3M,target,-8,314,0.11540575585746414,0.14444690043762673
+835,SPY_RETURN_3M,ipman_change_3m,-8,314,-0.08943270464814342,-0.06428130520436334
+836,SPY_RETURN_3M,target,-7,315,0.06161004962299796,0.10437194582544898
+837,SPY_RETURN_3M,ipman_change_3m,-7,315,-0.04041844408302944,-0.014929951663000119
+838,SPY_RETURN_3M,target,-6,316,0.029302234937233388,0.09360741047305136
+839,SPY_RETURN_3M,ipman_change_3m,-6,316,0.004764042038578103,-0.04049492016588611
+840,SPY_RETURN_3M,target,-5,317,-0.011139361822935246,0.054021330327835834
+841,SPY_RETURN_3M,ipman_change_3m,-5,317,0.0519969283422011,0.05528377982323234
+842,SPY_RETURN_3M,target,-4,318,-0.0485948301175396,0.03640509984413751
+843,SPY_RETURN_3M,ipman_change_3m,-4,318,0.006909557865377258,0.06004251500927602
+844,SPY_RETURN_3M,target,-3,319,-0.026519829089088773,0.05940073916599247
+845,SPY_RETURN_3M,ipman_change_3m,-3,319,0.009545940842579894,0.10045875968533743
+846,SPY_RETURN_3M,target,-2,320,-0.08083097775489943,-0.013975493099252686
+847,SPY_RETURN_3M,ipman_change_3m,-2,320,0.043038047528207175,0.07632862625611578
+848,SPY_RETURN_3M,target,-1,320,-0.14038060380238754,-0.06386800346358477
+849,SPY_RETURN_3M,ipman_change_3m,-1,320,0.1721631161182753,0.12451220226760026
+850,SPY_RETURN_3M,target,0,320,-0.23084631597529415,-0.14897875643803363
+851,SPY_RETURN_3M,ipman_change_3m,0,320,0.3013941804906713,0.1536163683239094
+852,SPY_RETURN_3M,target,1,319,-0.3050334474907048,-0.21415160101214778
+853,SPY_RETURN_3M,ipman_change_3m,1,319,0.4315724447113804,0.22135284201809902
+854,SPY_RETURN_3M,target,2,318,-0.3625475081482454,-0.2528602283747846
+855,SPY_RETURN_3M,ipman_change_3m,2,318,0.4087617027351583,0.24433069460151183
+856,SPY_RETURN_3M,target,3,317,-0.37294215280605897,-0.26301723821032935
+857,SPY_RETURN_3M,ipman_change_3m,3,317,0.28455358506060596,0.2869532328922555
+858,SPY_RETURN_3M,target,4,316,-0.31373438192799735,-0.229957930751149
+859,SPY_RETURN_3M,ipman_change_3m,4,316,0.12846482081236815,0.24178527218897783
+860,SPY_RETURN_3M,target,5,315,-0.25708705533474285,-0.17381007164896442
+861,SPY_RETURN_3M,ipman_change_3m,5,315,0.07034195425882497,0.1845513566226297
+862,SPY_RETURN_3M,target,6,314,-0.25747075266858366,-0.17494764037067143
+863,SPY_RETURN_3M,ipman_change_3m,6,314,0.10205547566541588,0.1392139819237355
+864,SPY_RETURN_3M,target,7,313,-0.21468864492940057,-0.17450463394162213
+865,SPY_RETURN_3M,ipman_change_3m,7,313,0.1262799661205618,0.13566260665933036
+866,SPY_RETURN_3M,target,8,312,-0.1637124811083231,-0.17202182014050693
+867,SPY_RETURN_3M,ipman_change_3m,8,312,0.10274894325945207,0.1341659429503445
+868,SPY_RETURN_3M,target,9,311,-0.13619555334542605,-0.16352682679882238
+869,SPY_RETURN_3M,ipman_change_3m,9,311,0.07156054908107562,0.13490062473570408
+870,SPY_RETURN_3M,target,10,310,-0.13763638543035595,-0.16272292347339093
+871,SPY_RETURN_3M,ipman_change_3m,10,310,0.07087270088590804,0.1457434520650579
+872,SPY_RETURN_3M,target,11,309,-0.13039145352075102,-0.14067563148382362
+873,SPY_RETURN_3M,ipman_change_3m,11,309,0.1181538918051598,0.1989644555256832
+874,SPY_RETURN_3M,target,12,308,-0.04619044139463613,-0.06579818650393011
+875,SPY_RETURN_3M,ipman_change_3m,12,308,0.06320741814177043,0.15699044322427516
+876,SPY_RETURN_3M,target,13,307,-0.030651753735624374,-0.04855301521300356
+877,SPY_RETURN_3M,ipman_change_3m,13,307,-0.022118782679862878,0.0627917142575123
+878,SPY_RETURN_3M,target,14,306,-0.01921592096882488,-0.032780654167246924
+879,SPY_RETURN_3M,ipman_change_3m,14,306,-0.08282069006789775,-0.05708754372684087
+880,SPY_RETURN_3M,target,15,305,-0.06256863721243106,-0.07416561620636566
+881,SPY_RETURN_3M,ipman_change_3m,15,305,-0.052650028921139506,-0.119142600957553
+882,SPY_RETURN_3M,target,16,304,-0.0709948100309409,-0.08950904591453136
+883,SPY_RETURN_3M,ipman_change_3m,16,304,0.03159789222772717,-0.12449704849691182
+884,SPY_RETURN_3M,target,17,303,-0.058652741919595984,-0.07962939775570338
+885,SPY_RETURN_3M,ipman_change_3m,17,303,0.022966972631933206,-0.11143704122939307
+886,SPY_RETURN_3M,target,18,302,-0.0007913408400071482,-0.005345822731042039
+887,SPY_RETURN_3M,ipman_change_3m,18,302,-0.02751698436761049,-0.10408794064154826
+888,SPY_RETURN_12M,target,-18,304,0.20299222378927462,0.18310043007178628
+889,SPY_RETURN_12M,ipman_change_3m,-18,304,-0.13144899714587913,-0.1169777633501055
+890,SPY_RETURN_12M,target,-17,305,0.2085987944304142,0.1927102522522402
+891,SPY_RETURN_12M,ipman_change_3m,-17,305,-0.11991672888748567,-0.08980569794785905
+892,SPY_RETURN_12M,target,-16,306,0.19744271621599838,0.18590672363342775
+893,SPY_RETURN_12M,ipman_change_3m,-16,306,-0.09474208296927228,-0.04824846583050372
+894,SPY_RETURN_12M,target,-15,307,0.19060765373906668,0.1712759739109021
+895,SPY_RETURN_12M,ipman_change_3m,-15,307,-0.07950697776632591,-0.04237969988113694
+896,SPY_RETURN_12M,target,-14,308,0.17258172365403382,0.15827131348242648
+897,SPY_RETURN_12M,ipman_change_3m,-14,308,-0.08317685110325393,-0.021324805929973382
+898,SPY_RETURN_12M,target,-13,309,0.1651088030904192,0.1608774779338067
+899,SPY_RETURN_12M,ipman_change_3m,-13,309,-0.08720529300074245,-0.03129085133469725
+900,SPY_RETURN_12M,target,-12,310,0.15266902657189513,0.1465826876013684
+901,SPY_RETURN_12M,ipman_change_3m,-12,310,-0.059094782880583854,-0.001553035528885031
+902,SPY_RETURN_12M,target,-11,311,0.10192198848276267,0.1124246934241904
+903,SPY_RETURN_12M,ipman_change_3m,-11,311,-0.03434198685281346,0.010600560107872628
+904,SPY_RETURN_12M,target,-10,312,0.03807720091643334,0.06320058425770314
+905,SPY_RETURN_12M,ipman_change_3m,-10,312,0.03340186126201672,0.03462063962562201
+906,SPY_RETURN_12M,target,-9,313,-0.03128928000158046,0.024433540295057284
+907,SPY_RETURN_12M,ipman_change_3m,-9,313,0.12980699092738796,0.06855743206753565
+908,SPY_RETURN_12M,target,-8,314,-0.12667346943935434,-0.038413667745862884
+909,SPY_RETURN_12M,ipman_change_3m,-8,314,0.22864561379835815,0.12364323845571683
+910,SPY_RETURN_12M,target,-7,315,-0.228326301841238,-0.11510980033424002
+911,SPY_RETURN_12M,ipman_change_3m,-7,315,0.27697976147384407,0.18492415429446793
+912,SPY_RETURN_12M,target,-6,316,-0.27664355773875404,-0.149315930252295
+913,SPY_RETURN_12M,ipman_change_3m,-6,316,0.289696735905116,0.2290650557232037
+914,SPY_RETURN_12M,target,-5,317,-0.3224230650484899,-0.20346616540799062
+915,SPY_RETURN_12M,ipman_change_3m,-5,317,0.3061716532603723,0.2660205578480485
+916,SPY_RETURN_12M,target,-4,318,-0.3623413203674919,-0.24806265707749517
+917,SPY_RETURN_12M,ipman_change_3m,-4,318,0.3002640829241287,0.28290281273375467
+918,SPY_RETURN_12M,target,-3,319,-0.3981999261609911,-0.2710246491498002
+919,SPY_RETURN_12M,ipman_change_3m,-3,319,0.3113404137815396,0.297986041284675
+920,SPY_RETURN_12M,target,-2,320,-0.4006171860465149,-0.2767147633652032
+921,SPY_RETURN_12M,ipman_change_3m,-2,320,0.31849510259242886,0.27729799607418043
+922,SPY_RETURN_12M,target,-1,320,-0.3986378936489074,-0.2976780030140822
+923,SPY_RETURN_12M,ipman_change_3m,-1,320,0.3248734154577327,0.28150433597984353
+924,SPY_RETURN_12M,target,0,320,-0.43429000234132537,-0.3143088398021929
+925,SPY_RETURN_12M,ipman_change_3m,0,320,0.32579639940381405,0.2760642193771424
+926,SPY_RETURN_12M,target,1,319,-0.4218677427497892,-0.3146975330283431
+927,SPY_RETURN_12M,ipman_change_3m,1,319,0.3242490781939696,0.28025115336842726
+928,SPY_RETURN_12M,target,2,318,-0.39528237762913315,-0.2942040045543671
+929,SPY_RETURN_12M,ipman_change_3m,2,318,0.30565715872637234,0.29538266400398994
+930,SPY_RETURN_12M,target,3,317,-0.3487154431955128,-0.25677855439294145
+931,SPY_RETURN_12M,ipman_change_3m,3,317,0.22118367009363468,0.2693649940969457
+932,SPY_RETURN_12M,target,4,316,-0.29946932251231856,-0.2333773795355527
+933,SPY_RETURN_12M,ipman_change_3m,4,316,0.12005349952740214,0.2127046242367889
+934,SPY_RETURN_12M,target,5,315,-0.2509914009205269,-0.2031602073063266
+935,SPY_RETURN_12M,ipman_change_3m,5,315,0.0812929714218995,0.15889128206309533
+936,SPY_RETURN_12M,target,6,314,-0.22425846950926837,-0.19580899551355574
+937,SPY_RETURN_12M,ipman_change_3m,6,314,0.07930713003875414,0.1099287666878884
+938,SPY_RETURN_12M,target,7,313,-0.20941887513905963,-0.2186006682019326
+939,SPY_RETURN_12M,ipman_change_3m,7,313,0.09880939901599692,0.0944828304110777
+940,SPY_RETURN_12M,target,8,312,-0.17878519962645784,-0.21488198647619067
+941,SPY_RETURN_12M,ipman_change_3m,8,312,0.08059661325549092,0.07394550119758918
+942,SPY_RETURN_12M,target,9,311,-0.12302505255816018,-0.16425685727560285
+943,SPY_RETURN_12M,ipman_change_3m,9,311,0.032034887168756576,0.04032617108024224
+944,SPY_RETURN_12M,target,10,310,-0.09904975868165422,-0.14350136984398226
+945,SPY_RETURN_12M,ipman_change_3m,10,310,-0.02090133974006217,-0.005178732013256491
+946,SPY_RETURN_12M,target,11,309,-0.06775892180875645,-0.09982956238422284
+947,SPY_RETURN_12M,ipman_change_3m,11,309,-0.05445903039656101,-0.06174993187207406
+948,SPY_RETURN_12M,target,12,308,-0.030700518665673814,-0.0644644394802018
+949,SPY_RETURN_12M,ipman_change_3m,12,308,-0.07534615574455486,-0.1022039849401413
+950,SPY_RETURN_12M,target,13,307,0.00016454555982024592,-0.025467992642403705
+951,SPY_RETURN_12M,ipman_change_3m,13,307,-0.10003860403735071,-0.15509472963008122
+952,SPY_RETURN_12M,target,14,306,0.03291456283818532,0.0019458835807041551
+953,SPY_RETURN_12M,ipman_change_3m,14,306,-0.13266848371034823,-0.21744152565709363
+954,SPY_RETURN_12M,target,15,305,0.012425368888290638,-0.011433239009500589
+955,SPY_RETURN_12M,ipman_change_3m,15,305,-0.11855848636940108,-0.23877624388840954
+956,SPY_RETURN_12M,target,16,304,0.023694821646120596,-0.0027215588284823728
+957,SPY_RETURN_12M,ipman_change_3m,16,304,-0.10427548700026648,-0.24451174194209757
+958,SPY_RETURN_12M,target,17,303,0.034854123801279266,0.002431431992540561
+959,SPY_RETURN_12M,ipman_change_3m,17,303,-0.10449408612542183,-0.22115202109266346
+960,SPY_RETURN_12M,target,18,302,0.07022027662454627,0.05086168484105711
+961,SPY_RETURN_12M,ipman_change_3m,18,302,-0.12630514940851287,-0.19265160108231016
+962,XLI_RETURN_3M,target,-18,304,0.052617973870799827,0.041881766416089844
+963,XLI_RETURN_3M,ipman_change_3m,-18,304,-0.07449851619620275,-0.15901767484772636
+964,XLI_RETURN_3M,target,-17,305,0.06901004328210313,0.048290391079601175
+965,XLI_RETURN_3M,ipman_change_3m,-17,305,-0.016453944299316663,-0.07372946590197771
+966,XLI_RETURN_3M,target,-16,306,0.03376073915316376,0.038019571499911954
+967,XLI_RETURN_3M,ipman_change_3m,-16,306,-0.016019552373280687,-0.010553913454098465
+968,XLI_RETURN_3M,target,-15,307,0.07596722811962148,0.06746784015794666
+969,XLI_RETURN_3M,ipman_change_3m,-15,307,-0.04913935961357149,-0.042265233010586524
+970,XLI_RETURN_3M,target,-14,308,0.12104015194827013,0.11144197353818794
+971,XLI_RETURN_3M,ipman_change_3m,-14,308,-0.0962337424228138,-0.10405053028625971
+972,XLI_RETURN_3M,target,-13,309,0.19959665107456806,0.17709793858707776
+973,XLI_RETURN_3M,ipman_change_3m,-13,309,-0.13111603024373683,-0.09093268147449168
+974,XLI_RETURN_3M,target,-12,310,0.21386009414663001,0.22478946687216855
+975,XLI_RETURN_3M,ipman_change_3m,-12,310,-0.13050473400510862,-0.027991899482690732
+976,XLI_RETURN_3M,target,-11,311,0.21375866991208314,0.20265646035425489
+977,XLI_RETURN_3M,ipman_change_3m,-11,311,-0.10009622218852968,0.006924353522216814
+978,XLI_RETURN_3M,target,-10,312,0.20917465330910348,0.22229171014778346
+979,XLI_RETURN_3M,ipman_change_3m,-10,312,-0.05221072091330549,-0.05022643957646988
+980,XLI_RETURN_3M,target,-9,313,0.14448264242557476,0.1554204486224058
+981,XLI_RETURN_3M,ipman_change_3m,-9,313,-0.016261546104644968,-0.04097862821739015
+982,XLI_RETURN_3M,target,-8,314,0.11467301551122656,0.1575391991824714
+983,XLI_RETURN_3M,ipman_change_3m,-8,314,-0.06392803247010605,-0.07344599262956643
+984,XLI_RETURN_3M,target,-7,315,0.05882744363330716,0.12756571156443763
+985,XLI_RETURN_3M,ipman_change_3m,-7,315,-0.05447505784708433,-0.010295896154156253
+986,XLI_RETURN_3M,target,-6,316,0.02338283677879345,0.092040163113533
+987,XLI_RETURN_3M,ipman_change_3m,-6,316,-0.026349255883682646,-0.022936929433754895
+988,XLI_RETURN_3M,target,-5,317,-0.02183297893091568,0.04891695265906394
+989,XLI_RETURN_3M,ipman_change_3m,-5,317,0.04668090000338642,0.08140526596141429
+990,XLI_RETURN_3M,target,-4,318,-0.07123541837902662,0.018908075112846617
+991,XLI_RETURN_3M,ipman_change_3m,-4,318,0.044420326053023644,0.07059644718117428
+992,XLI_RETURN_3M,target,-3,319,-0.056333407728730515,0.03468553800000033
+993,XLI_RETURN_3M,ipman_change_3m,-3,319,0.0673512091407305,0.1073645531436683
+994,XLI_RETURN_3M,target,-2,320,-0.12332346498614838,-0.044162558193638485
+995,XLI_RETURN_3M,ipman_change_3m,-2,320,0.10785531356213786,0.07053547886209827
+996,XLI_RETURN_3M,target,-1,320,-0.1849120847221159,-0.08035908532070295
+997,XLI_RETURN_3M,ipman_change_3m,-1,320,0.2364495467252973,0.13635228371370814
+998,XLI_RETURN_3M,target,0,320,-0.27204342490113737,-0.16938297636294256
+999,XLI_RETURN_3M,ipman_change_3m,0,320,0.3555789336156362,0.17150667975273196
+1000,XLI_RETURN_3M,target,1,319,-0.34322764609411915,-0.22650920159514384
+1001,XLI_RETURN_3M,ipman_change_3m,1,319,0.4581284750630959,0.27285074229609035
+1002,XLI_RETURN_3M,target,2,318,-0.38527112529763125,-0.25342464852740687
+1003,XLI_RETURN_3M,ipman_change_3m,2,318,0.4007021777247137,0.28631655191445354
+1004,XLI_RETURN_3M,target,3,317,-0.3728774445249906,-0.25720391919867247
+1005,XLI_RETURN_3M,ipman_change_3m,3,317,0.25598758867384525,0.298664410421622
+1006,XLI_RETURN_3M,target,4,316,-0.30189981165799656,-0.22254912505160765
+1007,XLI_RETURN_3M,ipman_change_3m,4,316,0.0962461374630199,0.21582991540265029
+1008,XLI_RETURN_3M,target,5,315,-0.23972541592255692,-0.17996644156733793
+1009,XLI_RETURN_3M,ipman_change_3m,5,315,0.031464979512713466,0.15648556608808162
+1010,XLI_RETURN_3M,target,6,314,-0.23914895698851688,-0.18688896779728797
+1011,XLI_RETURN_3M,ipman_change_3m,6,314,0.05755074562822736,0.10405335085337528
+1012,XLI_RETURN_3M,target,7,313,-0.19047796210532628,-0.16105895792126518
+1013,XLI_RETURN_3M,ipman_change_3m,7,313,0.08021260860829155,0.10558862801389408
+1014,XLI_RETURN_3M,target,8,312,-0.14835542562202364,-0.16533853996612913
+1015,XLI_RETURN_3M,ipman_change_3m,8,312,0.06335594318648781,0.08787918059771198
+1016,XLI_RETURN_3M,target,9,311,-0.12112742051660455,-0.1546204549821008
+1017,XLI_RETURN_3M,ipman_change_3m,9,311,0.039333223857658105,0.10645799589892527
+1018,XLI_RETURN_3M,target,10,310,-0.12673821740838243,-0.14438174634609258
+1019,XLI_RETURN_3M,ipman_change_3m,10,310,0.0399150979290991,0.11913017120449375
+1020,XLI_RETURN_3M,target,11,309,-0.1117637252445986,-0.11678731670355169
+1021,XLI_RETURN_3M,ipman_change_3m,11,309,0.10031299876757888,0.18364807757228677
+1022,XLI_RETURN_3M,target,12,308,-0.024199972158800143,-0.04016060482559698
+1023,XLI_RETURN_3M,ipman_change_3m,12,308,0.04294873840921038,0.13561799511010478
+1024,XLI_RETURN_3M,target,13,307,0.008331318757862392,-0.009085073398752199
+1025,XLI_RETURN_3M,ipman_change_3m,13,307,-0.05189015188699841,0.05374800201395335
+1026,XLI_RETURN_3M,target,14,306,0.02614166556474343,0.01092688472241564
+1027,XLI_RETURN_3M,ipman_change_3m,14,306,-0.13795702934427798,-0.08357839210869909
+1028,XLI_RETURN_3M,target,15,305,-0.014211879495791878,-0.022415165952836682
+1029,XLI_RETURN_3M,ipman_change_3m,15,305,-0.1002529289343211,-0.11500109966333384
+1030,XLI_RETURN_3M,target,16,304,-0.04351876751134729,-0.05790872396159715
+1031,XLI_RETURN_3M,ipman_change_3m,16,304,0.019278028514476107,-0.08703944164908294
+1032,XLI_RETURN_3M,target,17,303,-0.03993269180283564,-0.052275787839622063
+1033,XLI_RETURN_3M,ipman_change_3m,17,303,0.046242040790516754,-0.034104752406227026
+1034,XLI_RETURN_3M,target,18,302,-0.0003227913552041395,-0.0003054755846309736
+1035,XLI_RETURN_3M,ipman_change_3m,18,302,0.014685047096626293,-0.03230004415591941
+1036,XLI_RETURN_12M,target,-18,304,0.25171030943108696,0.2381363974922076
+1037,XLI_RETURN_12M,ipman_change_3m,-18,304,-0.14566882939979584,-0.10794556591120717
+1038,XLI_RETURN_12M,target,-17,305,0.2607730756365007,0.25198257027517745
+1039,XLI_RETURN_12M,ipman_change_3m,-17,305,-0.15058776571921922,-0.09592617029555567
+1040,XLI_RETURN_12M,target,-16,306,0.2501386670716112,0.23934368042661108
+1041,XLI_RETURN_12M,ipman_change_3m,-16,306,-0.14143661667573004,-0.04291193275516005
+1042,XLI_RETURN_12M,target,-15,307,0.23404187131350626,0.20806307439437413
+1043,XLI_RETURN_12M,ipman_change_3m,-15,307,-0.12506126274211846,-0.022042337811642276
+1044,XLI_RETURN_12M,target,-14,308,0.21684365996651567,0.17901848940708914
+1045,XLI_RETURN_12M,ipman_change_3m,-14,308,-0.12276707838418381,0.024578438543841282
+1046,XLI_RETURN_12M,target,-13,309,0.20073988709104776,0.15379945873965203
+1047,XLI_RETURN_12M,ipman_change_3m,-13,309,-0.11514784828103321,0.030140201170580125
+1048,XLI_RETURN_12M,target,-12,310,0.1796999995775001,0.12780132222301488
+1049,XLI_RETURN_12M,ipman_change_3m,-12,310,-0.06756235501183724,0.08043177247656902
+1050,XLI_RETURN_12M,target,-11,311,0.10900018475482191,0.08117938901798682
+1051,XLI_RETURN_12M,ipman_change_3m,-11,311,-0.012318794333080172,0.11194697326322677
+1052,XLI_RETURN_12M,target,-10,312,0.018840451202556115,0.010896652458224678
+1053,XLI_RETURN_12M,ipman_change_3m,-10,312,0.09631660070413225,0.14392603790403324
+1054,XLI_RETURN_12M,target,-9,313,-0.07150682625984216,-0.043228571291255194
+1055,XLI_RETURN_12M,ipman_change_3m,-9,313,0.21836867888040434,0.17460157819023125
+1056,XLI_RETURN_12M,target,-8,314,-0.18412137755736424,-0.102148704492744
+1057,XLI_RETURN_12M,ipman_change_3m,-8,314,0.3184347827133412,0.22267915163460636
+1058,XLI_RETURN_12M,target,-7,315,-0.2930450263282255,-0.18010961296078848
+1059,XLI_RETURN_12M,ipman_change_3m,-7,315,0.3466945033378838,0.2735186955536871
+1060,XLI_RETURN_12M,target,-6,316,-0.33552336805583227,-0.21571022748280025
+1061,XLI_RETURN_12M,ipman_change_3m,-6,316,0.3388054407667074,0.30131906454232055
+1062,XLI_RETURN_12M,target,-5,317,-0.3800921927058514,-0.27407672315933507
+1063,XLI_RETURN_12M,ipman_change_3m,-5,317,0.34999038328836984,0.32815481899434856
+1064,XLI_RETURN_12M,target,-4,318,-0.41592212410663304,-0.3114188192093469
+1065,XLI_RETURN_12M,ipman_change_3m,-4,318,0.3410806658200014,0.3256726956280965
+1066,XLI_RETURN_12M,target,-3,319,-0.4417176931277496,-0.3342169248583028
+1067,XLI_RETURN_12M,ipman_change_3m,-3,319,0.3431874267171757,0.334914162772816
+1068,XLI_RETURN_12M,target,-2,320,-0.4391335052901926,-0.33233722590022885
+1069,XLI_RETURN_12M,ipman_change_3m,-2,320,0.33639729290895776,0.29553767614918114
+1070,XLI_RETURN_12M,target,-1,320,-0.42860906972517754,-0.33429379493412426
+1071,XLI_RETURN_12M,ipman_change_3m,-1,320,0.32547110810815943,0.28662694459906835
+1072,XLI_RETURN_12M,target,0,320,-0.45911907345807623,-0.34281884572466836
+1073,XLI_RETURN_12M,ipman_change_3m,0,320,0.3165390399079357,0.2628629185831893
+1074,XLI_RETURN_12M,target,1,319,-0.4373751989015888,-0.34404683441295875
+1075,XLI_RETURN_12M,ipman_change_3m,1,319,0.30348714557869133,0.2734747441887975
+1076,XLI_RETURN_12M,target,2,318,-0.39889089266607164,-0.3183329660789699
+1077,XLI_RETURN_12M,ipman_change_3m,2,318,0.27619380937677135,0.28620609424091975
+1078,XLI_RETURN_12M,target,3,317,-0.3381014549668138,-0.2684051924162552
+1079,XLI_RETURN_12M,ipman_change_3m,3,317,0.18019030316778015,0.2619874947166346
+1080,XLI_RETURN_12M,target,4,316,-0.2753896022501771,-0.23024288481651597
+1081,XLI_RETURN_12M,ipman_change_3m,4,316,0.0685608147450708,0.19063477485391792
+1082,XLI_RETURN_12M,target,5,315,-0.2190215768053332,-0.19442675230584322
+1083,XLI_RETURN_12M,ipman_change_3m,5,315,0.020094114719892395,0.12946867693300007
+1084,XLI_RETURN_12M,target,6,314,-0.18741271633401876,-0.1771057115923491
+1085,XLI_RETURN_12M,ipman_change_3m,6,314,0.01953630910312936,0.08193468551621622
+1086,XLI_RETURN_12M,target,7,313,-0.17071666763001375,-0.18216722350161052
+1087,XLI_RETURN_12M,ipman_change_3m,7,313,0.05254665675154215,0.08098712809137912
+1088,XLI_RETURN_12M,target,8,312,-0.13754521179924087,-0.16446680776947115
+1089,XLI_RETURN_12M,ipman_change_3m,8,312,0.04859296011252524,0.08375617068589342
+1090,XLI_RETURN_12M,target,9,311,-0.08436404105109357,-0.1192869799059267
+1091,XLI_RETURN_12M,ipman_change_3m,9,311,0.01268184331183518,0.06281665642727773
+1092,XLI_RETURN_12M,target,10,310,-0.06277404141111345,-0.09346663864071238
+1093,XLI_RETURN_12M,ipman_change_3m,10,310,-0.03751304203449603,0.03214366636019508
+1094,XLI_RETURN_12M,target,11,309,-0.028032747726766158,-0.04187828023208166
+1095,XLI_RETURN_12M,ipman_change_3m,11,309,-0.06385327889215663,-0.023276566840613192
+1096,XLI_RETURN_12M,target,12,308,-0.0006980296542255602,-0.019265234787186745
+1097,XLI_RETURN_12M,ipman_change_3m,12,308,-0.07995566646839819,-0.05376790124268066
+1098,XLI_RETURN_12M,target,13,307,0.02028768038540266,0.006404232067972862
+1099,XLI_RETURN_12M,ipman_change_3m,13,307,-0.09639833842097217,-0.09773231175798718
+1100,XLI_RETURN_12M,target,14,306,0.05060440725152459,0.04565342247036672
+1101,XLI_RETURN_12M,ipman_change_3m,14,306,-0.13290229180654783,-0.16733457092988313
+1102,XLI_RETURN_12M,target,15,305,0.022323153034527136,0.02256560330822485
+1103,XLI_RETURN_12M,ipman_change_3m,15,305,-0.11519477581412892,-0.19124879459980715
+1104,XLI_RETURN_12M,target,16,304,0.025788483704296025,0.02237726147863284
+1105,XLI_RETURN_12M,ipman_change_3m,16,304,-0.0948497490221164,-0.20305999538694164
+1106,XLI_RETURN_12M,target,17,303,0.029781389758326532,0.020059313938459628
+1107,XLI_RETURN_12M,ipman_change_3m,17,303,-0.08151258393445271,-0.17098619935189754
+1108,XLI_RETURN_12M,target,18,302,0.05554920997104633,0.050556209256426135
+1109,XLI_RETURN_12M,ipman_change_3m,18,302,-0.0996956065849052,-0.15260368526566853
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/model_input_inventory.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/model_input_inventory.csv
new file mode 100644
index 000000000..dc5386c97
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/model_input_inventory.csv
@@ -0,0 +1,16 @@
+,series_id,family,transformation
+0,ipman_change_1m_pct,IPMAN momentum,trailing 1-month percent change
+1,ipman_change_3m_pct,IPMAN momentum,trailing 3-month percent change
+2,ipman_change_6m_pct,IPMAN momentum,trailing 6-month percent change
+3,FEDFUNDS,macro/market,monthly level or trailing return from data service
+4,YC_SPREAD,macro/market,monthly level or trailing return from data service
+5,CPIAUCSL,macro/market,monthly level or trailing return from data service
+6,CPI_YOY,macro/market,monthly level or trailing return from data service
+7,UNRATE,macro/market,monthly level or trailing return from data service
+8,ICSA,macro/market,monthly level or trailing return from data service
+9,VIXCLS,macro/market,monthly level or trailing return from data service
+10,CREDIT_SPREAD,macro/market,monthly level or trailing return from data service
+11,SPY_RETURN_3M,macro/market,monthly level or trailing return from data service
+12,SPY_RETURN_12M,macro/market,monthly level or trailing return from data service
+13,XLI_RETURN_3M,macro/market,monthly level or trailing return from data service
+14,XLI_RETURN_12M,macro/market,monthly level or trailing return from data service
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/pearson_correlation.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/pearson_correlation.csv
new file mode 100644
index 000000000..d2fbf43aa
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/pearson_correlation.csv
@@ -0,0 +1,16 @@
+,ipman_change_1m_pct,ipman_change_3m_pct,ipman_change_6m_pct,FEDFUNDS,YC_SPREAD,CPIAUCSL,CPI_YOY,UNRATE,ICSA,VIXCLS,CREDIT_SPREAD,SPY_RETURN_3M,SPY_RETURN_12M,XLI_RETURN_3M,XLI_RETURN_12M
+ipman_change_1m_pct,1.0,0.5337656389885242,0.37988184345898274,-0.009533611629831113,0.024714346740195882,-0.004389877228350195,0.009837183105730238,-0.006623766178406295,-0.3500832062820553,-0.21610249028037956,-0.22423028604295653,0.2879758328559701,0.22028083485517297,0.3035598973808261,0.20945689300194742
+ipman_change_3m_pct,0.5337656389885242,1.0,0.6420449843162942,0.007447241773084335,0.018915170244317683,-0.012004258000337342,0.05650309262288925,-0.07881750348086873,-0.3110770137937919,-0.2907173373613759,-0.3581261203291927,0.30139418049067124,0.32579639940381405,0.3555789336156362,0.3165390399079356
+ipman_change_6m_pct,0.37988184345898274,0.6420449843162942,1.0,0.04766502140005976,0.008917556216651449,-0.03493512881121786,0.14024836380422837,-0.13611145210594275,-0.30376073685147875,-0.3481692248770284,-0.4768219766631255,0.21444801424366636,0.4578550584039485,0.2972465097674346,0.47904750006492064
+FEDFUNDS,-0.009533611629831113,0.007447241773084335,0.04766502140005976,1.0,-0.7225110542595956,0.08018275925101602,0.3325522759523678,-0.608503366899898,-0.1967663607676307,-0.1700541946120379,-0.4716332485641383,-0.05177959184171633,-0.03491762321724274,-0.003916581159387676,0.009623535610365061
+YC_SPREAD,0.024714346740195882,0.018915170244317683,0.008917556216651449,-0.7225110542595956,1.0,-0.43082896984221936,-0.42259427300901886,0.692367751115584,0.1083160284174071,0.23845508052860792,0.550771268189254,-0.017607792768222896,-0.07008358660167227,0.0080763865411501,0.013974122613995374
+CPIAUCSL,-0.004389877228350195,-0.012004258000337342,-0.03493512881121786,0.08018275925101602,-0.43082896984221936,1.0,0.2716889874048007,-0.27008917291211587,-0.08085007832470513,-0.15032095193144096,-0.38880399449199254,0.1905350926550328,0.34178029491056,0.12464345823595037,0.23624628088524102
+CPI_YOY,0.009837183105730238,0.05650309262288925,0.14024836380422837,0.3325522759523678,-0.42259427300901886,0.2716889874048007,1.0,-0.4382964069168062,-0.1984813138734117,-0.011727206790550095,-0.3787109758266469,-0.15403987661724614,0.08023524661639336,-0.10873580848260578,0.1365794886371103
+UNRATE,-0.006623766178406295,-0.07881750348086873,-0.13611145210594275,-0.608503366899898,0.692367751115584,-0.27008917291211587,-0.4382964069168062,1.0,0.3646085989530247,0.3074124312026046,0.5241748834625456,0.10218316645626337,-0.021633640608774825,0.0665879457020658,-0.04180589574616615
+ICSA,-0.3500832062820553,-0.3110770137937919,-0.30376073685147875,-0.1967663607676307,0.1083160284174071,-0.08085007832470513,-0.1984813138734117,0.3646085989530247,1.0,0.4144748109036352,0.2976883081158959,-0.14686248548181602,-0.13093099273625003,-0.19968001265566934,-0.19852180932957797
+VIXCLS,-0.21610249028037956,-0.2907173373613759,-0.3481692248770284,-0.1700541946120379,0.23845508052860792,-0.15032095193144096,-0.011727206790550095,0.3074124312026046,0.4144748109036352,1.0,0.6959997352488033,-0.5770085879765605,-0.5847195706892242,-0.5288974448433599,-0.5782955327269617
+CREDIT_SPREAD,-0.22423028604295653,-0.3581261203291927,-0.4768219766631255,-0.4716332485641383,0.550771268189254,-0.38880399449199254,-0.3787109758266469,0.5241748834625456,0.2976883081158959,0.6959997352488033,1.0,-0.41633480774696136,-0.6522746677186666,-0.3883259898021005,-0.6153869172144719
+SPY_RETURN_3M,0.2879758328559701,0.30139418049067124,0.21444801424366636,-0.05177959184171633,-0.017607792768222896,0.1905350926550328,-0.15403987661724614,0.10218316645626337,-0.14686248548181602,-0.5770085879765605,-0.41633480774696136,1.0,0.49004685224207856,0.8967131305881793,0.40873301178680643
+SPY_RETURN_12M,0.22028083485517297,0.32579639940381405,0.4578550584039485,-0.03491762321724274,-0.07008358660167227,0.34178029491056,0.08023524661639336,-0.021633640608774825,-0.13093099273625003,-0.5847195706892242,-0.6522746677186666,0.49004685224207856,1.0,0.44107949800167834,0.9242079679551487
+XLI_RETURN_3M,0.3035598973808261,0.3555789336156362,0.2972465097674346,-0.003916581159387676,0.0080763865411501,0.12464345823595037,-0.10873580848260578,0.0665879457020658,-0.19968001265566934,-0.5288974448433599,-0.3883259898021005,0.8967131305881793,0.44107949800167834,1.0,0.44151633897173037
+XLI_RETURN_12M,0.20945689300194742,0.3165390399079356,0.47904750006492064,0.009623535610365061,0.013974122613995374,0.23624628088524102,0.1365794886371103,-0.04180589574616615,-0.19852180932957797,-0.5782955327269617,-0.6153869172144719,0.40873301178680643,0.9242079679551487,0.44151633897173037,1.0
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/regime_summary.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/regime_summary.csv
new file mode 100644
index 000000000..398da2e49
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/regime_summary.csv
@@ -0,0 +1,31 @@
+,series_id,regime,months,mean,median,std
+0,ipman_change_1m_pct,no_stress,300,0.148637846147753,0.06800489640652607,0.618667900040921
+1,ipman_change_1m_pct,stress,20,-1.7298039282022266,-1.1812055570873559,4.289818864939371
+2,ipman_change_3m_pct,no_stress,300,0.4799472568834384,0.29897728989450867,1.584920429488408
+3,ipman_change_3m_pct,stress,20,-5.527493749886937,-4.645435857850266,4.511106327479949
+4,ipman_change_6m_pct,no_stress,300,0.7891855800073515,0.6221550630042927,2.1522510884330046
+5,ipman_change_6m_pct,stress,20,-8.352793019136653,-8.43529772117863,4.842207763426674
+6,FEDFUNDS,no_stress,300,2.1259666666666663,1.53,2.0465883346667946
+7,FEDFUNDS,stress,20,0.8859999999999999,0.215,1.1094303896476263
+8,YC_SPREAD,no_stress,300,1.0156333333333334,0.945,0.966351929887284
+9,YC_SPREAD,stress,20,1.462,1.4849999999999999,0.8174131210876884
+10,CPIAUCSL,no_stress,299,236.02372575250834,233.456,44.41593514687624
+11,CPIAUCSL,stress,20,229.82755000000003,217.22899999999998,25.198767005080143
+12,CPI_YOY,no_stress,299,2.6593451140011646,2.4709963021052994,1.6459534752832323
+13,CPI_YOY,stress,20,1.8415411698848572,0.9082868768998664,2.509132148988838
+14,UNRATE,no_stress,299,5.466220735785954,4.9,1.771671971690381
+15,UNRATE,stress,20,7.749999999999998,7.05,2.84983840723793
+16,ICSA,no_stress,300,327880.0,312000.0,122094.48175456739
+17,ICSA,stress,20,1040800.0,600500.0,1356271.498980705
+18,VIXCLS,no_stress,300,18.774733333333334,17.07,6.120017672900212
+19,VIXCLS,stress,20,35.29,32.29,12.234243572427095
+20,CREDIT_SPREAD,no_stress,300,2.3442333333333334,2.28,0.5546450962837958
+21,CREDIT_SPREAD,stress,20,4.037000000000001,3.785,1.2737226831945054
+22,SPY_RETURN_3M,no_stress,300,2.7525784904903854,3.5026090249230535,6.4075508003239925
+23,SPY_RETURN_3M,stress,20,-4.298747462643152,-7.792884218527634,15.019439482021662
+24,SPY_RETURN_12M,no_stress,300,11.57698533967373,13.666800128881306,14.493724902563065
+25,SPY_RETURN_12M,stress,20,-18.02432517062333,-20.271759868309438,20.351240356939766
+26,XLI_RETURN_3M,no_stress,300,3.2218009240990626,3.8807504566830575,7.446874149634055
+27,XLI_RETURN_3M,stress,20,-6.712318095211077,-8.919550114656154,18.596553889826527
+28,XLI_RETURN_12M,no_stress,300,12.851542327779184,13.4933729277539,16.389873390072342
+29,XLI_RETURN_12M,stress,20,-22.626010798928696,-21.568744760823716,20.56971097929642
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/release_gap.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/release_gap.csv
new file mode 100644
index 000000000..6ce286501
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/release_gap.csv
@@ -0,0 +1,20 @@
+,series_id,category,observations,missing_values,missing_rate,reference_start,reference_end,median_release_delay_days
+0,CPIAUCSL,Active macro or market feature,955,0,0.0,1947-01-01,2026-08-01,1.0
+1,CPI_YOY,Active macro or market feature,943,0,0.0,1948-01-01,2026-08-01,1.0
+2,CREDIT_SPREAD,Active macro or market feature,489,0,0.0,1986-01-01,2026-09-01,31.0
+3,FEDFUNDS,Active macro or market feature,866,0,0.0,1954-07-01,2026-08-01,1.0
+4,ICSA,Active macro or market feature,717,0,0.0,1967-01-01,2026-09-01,28.0
+5,SPY_RETURN_12M,Active macro or market feature,334,0,0.0,1999-01-01,2026-10-01,31.0
+6,SPY_RETURN_3M,Active macro or market feature,343,0,0.0,1998-04-01,2026-10-01,31.0
+7,UNRATE,Active macro or market feature,943,0,0.0,1948-01-01,2026-08-01,1.0
+8,VIXCLS,Active macro or market feature,441,0,0.0,1990-01-01,2026-09-01,31.0
+9,XLI_RETURN_12M,Active macro or market feature,323,0,0.0,1999-12-01,2026-10-01,31.0
+10,XLI_RETURN_3M,Active macro or market feature,332,0,0.0,1999-03-01,2026-10-01,31.0
+11,YC_SPREAD,Active macro or market feature,604,0,0.0,1976-06-01,2026-09-01,31.0
+17,manufacturing_stress,Binary target,653,0,0.0,1972-04-01,2026-08-01,31.0
+16,ipman_us_manufacturing_production,IPMAN level,656,0,0.0,1972-01-01,2026-08-01,31.0
+13,ipman_change_1m_pct,IPMAN momentum feature,655,0,0.0,1972-02-01,2026-08-01,31.0
+14,ipman_change_3m_pct,IPMAN momentum feature,653,0,0.0,1972-04-01,2026-08-01,31.0
+15,ipman_change_6m_pct,IPMAN momentum feature,650,0,0.0,1972-07-01,2026-08-01,31.0
+12,fed_funds_rate_pct,Registered macro or market input,866,0,0.0,1954-07-01,2026-08-01,1.0
+18,treasury_10y_minus_2y_pct_points,Registered macro or market input,604,0,0.0,1976-06-01,2026-09-01,31.0
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/robustness_summary.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/robustness_summary.csv
new file mode 100644
index 000000000..678940b34
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/robustness_summary.csv
@@ -0,0 +1,7 @@
+,series_id,point_estimate,bootstrap_ci_low,bootstrap_ci_high,loo_min,loo_max,loo_swing,sign_stable_under_loo
+5,UNRATE,0.28694679822391594,0.1265627849303519,0.4394932605289431,0.2434553301419189,0.30813656894995917,0.06468123880804028,True
+3,ICSA,0.44128880591364505,0.33309226227418703,0.5880448843611513,0.39455080490850414,0.4575012977330617,0.06295049282455756,True
+4,SPY_RETURN_12M,-0.43429000234132537,-0.5540152450163299,-0.28037073493757253,-0.461929805871413,-0.4115598561209363,0.050369949750476706,True
+0,CREDIT_SPREAD,0.5516751617126009,0.39086672990620625,0.6721048056213613,0.5242379842715567,0.573169462312008,0.04893147804045139,True
+2,XLI_RETURN_12M,-0.45911907345807623,-0.568316951232531,-0.3187307676879995,-0.48141220821745884,-0.43654165277878676,0.04487055543867208,True
+1,VIXCLS,0.5166819806390913,0.36190665146213324,0.636732076030752,0.4857269831804124,0.527930271268892,0.04220328808847962,True
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/service_summary.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/service_summary.csv
new file mode 100644
index 000000000..71a80ae0f
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/service_summary.csv
@@ -0,0 +1,20 @@
+,series_id,description,source,units,frequency,n_obs,start,end
+0,CPIAUCSL,Consumer Price Index for All Urban Consumers,FRED (CPIAUCSL),Index,MS,955,1947-01-01,2026-08-01
+1,CPI_YOY,Consumer Price Index year-over-year change,Derived from FRED (CPIAUCSL),Percent,MS,943,1948-01-01,2026-08-01
+2,CREDIT_SPREAD,Moody's Baa corporate bond yield minus 10-year Treasury yield (credit-spread proxy),FRED (BAA10Y),Percentage points,MS,489,1986-01-01,2026-09-01
+3,FEDFUNDS,Effective federal funds rate,FRED (FEDFUNDS),Percent,MS,866,1954-07-01,2026-08-01
+4,ICSA,Initial claims for unemployment insurance,FRED (ICSA),Number,MS,717,1967-01-01,2026-09-01
+5,SPY_RETURN_12M,SPY trailing 12-month adjusted-close return,"Yahoo Finance (SPY), derived",Percent,MS,334,1999-01-01,2026-10-01
+6,SPY_RETURN_3M,SPY trailing 3-month adjusted-close return,"Yahoo Finance (SPY), derived",Percent,MS,343,1998-04-01,2026-10-01
+7,UNRATE,U.S. unemployment rate,FRED (UNRATE),Percent,MS,943,1948-01-01,2026-08-01
+8,VIXCLS,CBOE volatility index,FRED (VIXCLS),Index,MS,441,1990-01-01,2026-09-01
+9,XLI_RETURN_12M,XLI trailing 12-month adjusted-close return,"Yahoo Finance (XLI), derived",Percent,MS,323,1999-12-01,2026-10-01
+10,XLI_RETURN_3M,XLI trailing 3-month adjusted-close return,"Yahoo Finance (XLI), derived",Percent,MS,332,1999-03-01,2026-10-01
+11,YC_SPREAD,10-year minus 2-year Treasury yield spread,FRED (DGS10 minus DGS2),Percentage points,MS,604,1976-06-01,2026-09-01
+12,fed_funds_rate_pct,Month-end effective federal funds rate,"FRED (FEDFUNDS), derived monthly",Percent,MS,866,1954-07-01,2026-08-01
+13,ipman_change_1m_pct,Trailing 1-month percentage change in IPMAN,Derived from FRED IPMAN,Percent,MS,655,1972-02-01,2026-08-01
+14,ipman_change_3m_pct,Trailing 3-month percentage change in IPMAN,Derived from FRED IPMAN,Percent,MS,653,1972-04-01,2026-08-01
+15,ipman_change_6m_pct,Trailing 6-month percentage change in IPMAN,Derived from FRED IPMAN,Percent,MS,650,1972-07-01,2026-08-01
+16,ipman_us_manufacturing_production,U.S. manufacturing industrial production index (IPMAN),FRED (IPMAN),Index,MS,656,1972-01-01,2026-08-01
+17,manufacturing_stress,Binary U.S. manufacturing stress label: 1 when trailing 3-month IPMAN change is at or below -2.0%,Derived from FRED IPMAN,"Binary event (0=no stress, 1=stress)",MS,653,1972-04-01,2026-08-01
+18,treasury_10y_minus_2y_pct_points,Month-end 10-year minus 2-year Treasury yield spread,"FRED (DGS10 minus DGS2), derived monthly",Percentage points,MS,604,1976-06-01,2026-09-01
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/spearman_correlation.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/spearman_correlation.csv
new file mode 100644
index 000000000..d61af4bb3
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/spearman_correlation.csv
@@ -0,0 +1,16 @@
+,ipman_change_1m_pct,ipman_change_3m_pct,ipman_change_6m_pct,FEDFUNDS,YC_SPREAD,CPIAUCSL,CPI_YOY,UNRATE,ICSA,VIXCLS,CREDIT_SPREAD,SPY_RETURN_3M,SPY_RETURN_12M,XLI_RETURN_3M,XLI_RETURN_12M
+ipman_change_1m_pct,1.0,0.6034563325813729,0.48367379075967537,-0.11924925480981409,0.06872509550024178,-0.005677203610790011,-0.02035085695844525,0.167159996610206,0.055548988996464654,-0.08228367582597548,-0.1437757543044475,0.1604308879969531,0.2218117364427387,0.17821939179093546,0.1866771159874608
+ipman_change_3m_pct,0.6034563325813729,1.0,0.7571865936190784,-0.0917218832843526,0.1251132413001479,-0.1205870208202457,0.022662030272600175,0.22009299701024934,0.09830527812195004,-0.09935297963483235,-0.20063904535567706,0.1536163683239094,0.2760642193771424,0.17150667975273196,0.2628629185831893
+ipman_change_6m_pct,0.48367379075967537,0.7571865936190784,1.0,-0.06420843204790525,0.14076757159745545,-0.2133931730126109,0.11298872378835952,0.22644251621564543,0.12246698804960252,-0.15363618560624043,-0.23327010765070538,0.13105059619722847,0.34447016084141446,0.14666122227756132,0.3803624791257727
+FEDFUNDS,-0.11924925480981409,-0.0917218832843526,-0.06420843204790525,1.0,-0.6912460301330114,-0.10964719527867965,0.45420752933261777,-0.6883292387743639,-0.42392559149085685,-0.14674322261395395,-0.4948507753067115,-0.12072883500265912,-0.18455537491116328,-0.07912457121164873,-0.0964943444758319
+YC_SPREAD,0.06872509550024178,0.1251132413001479,0.14076757159745545,-0.6912460301330114,1.0,-0.3664782229105664,-0.44115309697655875,0.8321546555605804,0.6685893956640341,0.2555971281368221,0.6294276855556363,0.025133893360662282,0.010458956179962503,0.047228021064538946,0.08672193645520698
+CPIAUCSL,-0.005677203610790011,-0.1205870208202457,-0.2133931730126109,-0.10964719527867965,-0.3664782229105664,1.0,0.085590768456774,-0.3808734787306133,-0.5923869321266148,-0.1725863273584107,-0.3979654618102347,0.21249413611328213,0.3829401833638386,0.11912516168439226,0.23803905709083784
+CPI_YOY,-0.02035085695844525,0.022662030272600175,0.11298872378835952,0.45420752933261777,-0.44115309697655875,0.085590768456774,1.0,-0.46722830508532953,-0.30657839887970445,-0.012541437740610764,-0.45007590021982585,-0.13678323637828432,0.07001686986471511,-0.12839247951892918,0.12491093277025497
+UNRATE,0.167159996610206,0.22009299701024934,0.22644251621564543,-0.6883292387743639,0.8321546555605804,-0.3808734787306133,-0.46722830508532953,1.0,0.8342388266194574,0.24733242352940862,0.6057158560500727,0.10202740597264133,0.0020776748148357677,0.1077140145148845,0.013795568461697872
+ICSA,0.055548988996464654,0.09830527812195004,0.12246698804960252,-0.42392559149085685,0.6685893956640341,-0.5923869321266148,-0.30657839887970445,0.8342388266194574,1.0,0.4493746675407134,0.6406852821983352,-0.03584766064779213,-0.22460574535375022,0.006207504135431726,-0.17948773537275234
+VIXCLS,-0.08228367582597548,-0.09935297963483235,-0.15363618560624043,-0.14674322261395395,0.2555971281368221,-0.1725863273584107,-0.012541437740610764,0.24733242352940862,0.4493746675407134,1.0,0.5662995168865088,-0.4123682989181586,-0.4607131393215058,-0.3245747421090349,-0.47788444907224575
+CREDIT_SPREAD,-0.1437757543044475,-0.20063904535567706,-0.23327010765070538,-0.4948507753067115,0.6294276855556363,-0.3979654618102347,-0.45007590021982585,0.6057158560500727,0.6406852821983352,0.5662995168865088,1.0,-0.2554777795614799,-0.5320281244435171,-0.19917077191342722,-0.5014190087975375
+SPY_RETURN_3M,0.1604308879969531,0.1536163683239094,0.13105059619722847,-0.12072883500265912,0.025133893360662282,0.21249413611328213,-0.13678323637828432,0.10202740597264133,-0.03584766064779213,-0.4123682989181586,-0.2554777795614799,1.0,0.44993611978891945,0.8506448495692467,0.36014331308348313
+SPY_RETURN_12M,0.2218117364427387,0.2760642193771424,0.34447016084141446,-0.18455537491116328,0.010458956179962503,0.3829401833638386,0.07001686986471511,0.0020776748148357677,-0.22460574535375022,-0.4607131393215058,-0.5320281244435171,0.44993611978891945,1.0,0.3764209505526371,0.8940460120830556
+XLI_RETURN_3M,0.17821939179093546,0.17150667975273196,0.14666122227756132,-0.07912457121164873,0.047228021064538946,0.11912516168439226,-0.12839247951892918,0.1077140145148845,0.006207504135431726,-0.3245747421090349,-0.19917077191342722,0.8506448495692467,0.3764209505526371,1.0,0.37028399332101797
+XLI_RETURN_12M,0.1866771159874608,0.2628629185831893,0.3803624791257727,-0.0964943444758319,0.08672193645520698,0.23803905709083784,0.12491093277025497,0.013795568461697872,-0.17948773537275234,-0.47788444907224575,-0.5014190087975375,0.36014331308348313,0.8940460120830556,0.37028399332101797,1.0
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/summary_statistics.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/summary_statistics.csv
new file mode 100644
index 000000000..d6fdd90e3
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/summary_statistics.csv
@@ -0,0 +1,16 @@
+,count,mean,std,min,5%,25%,50%,75%,95%,max
+ipman_change_1m_pct,320.0,0.03123523525087929,1.2892743017156176,-15.371556283705957,-0.9729159598537085,-0.3093564121659842,0.043088583236861044,0.4837519667045209,1.1637758307417512,7.858988928367627
+ipman_change_3m_pct,320.0,0.10448219396029004,2.3849098797521755,-19.01307109340218,-2.179607462268447,-0.465315144030784,0.22181753928502967,0.9332415934250882,2.2104496213519425,16.81344675032277
+ipman_change_6m_pct,320.0,0.21781191756085105,3.263535365093738,-18.58538529843845,-4.273020196627525,-0.5308504234802558,0.47502110774468864,1.607360786897416,3.461830098755522,19.67475052812029
+FEDFUNDS,320.0,2.04846875,2.0222763716065884,0.05,0.08,0.15,1.375,3.895,5.33,6.54
+YC_SPREAD,321.0,1.0409034267912771,0.96234940056586,-1.06,-0.3899999999999997,0.22000000000000064,0.9900000000000002,1.87,2.54,2.84
+CPIAUCSL,319.0,235.63525078369906,43.46151800025622,169.3,177.21,202.4,232.282,256.736,319.8367,334.131
+CPI_YOY,319.0,2.6080721394484185,1.718825753585582,-1.9587610037622771,0.03241419258328327,1.6618436137341597,2.420260782347028,3.3404339383611203,5.985542903353175,8.979361103105798
+UNRATE,319.0,5.609404388714734,1.932388448482793,3.4,3.6,4.2,5.0,6.2,9.5,14.8
+ICSA,321.0,371887.85046728974,391184.099732054,190000.0,207000.0,237000.0,318000.0,392000.0,620000.0,5946000.0
+VIXCLS,321.0,19.7895015576324,7.743576797951889,9.51,11.57,14.21,17.52,23.54,33.4,59.89
+CREDIT_SPREAD,321.0,2.4469781931464176,0.7447557671129342,1.42,1.59,1.87,2.31,2.86,3.45,6.1
+SPY_RETURN_3M,322.0,2.3150884658168396,7.3825715997082035,-29.644011395465753,-11.352583973519632,-1.475517434709675,3.0502744084196842,7.009729191896458,12.891172962607488,26.054705724712115
+SPY_RETURN_12M,322.0,9.758944553635292,16.478377585066536,-43.424418557066005,-22.328992068296202,2.680612501615748,12.900244465959254,19.229841615596378,32.253588643980024,56.23113123462973
+XLI_RETURN_3M,322.0,2.53862803197189,8.862384067929405,-34.025244291655746,-12.79416538731909,-2.4292257524111074,3.545513701566838,8.112924976970021,15.493408826709134,32.78905137233576
+XLI_RETURN_12M,322.0,10.63137459343425,18.67579116307224,-51.67091651730529,-21.857337516928737,1.554410265352879,12.514602809073004,21.92237383425188,36.620484762757535,73.44763956813587
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/target_summary.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/target_summary.csv
new file mode 100644
index 000000000..6ea1a4b07
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/target_summary.csv
@@ -0,0 +1,2 @@
+,stress_months,observed_months,stress_base_rate,lookback_months,threshold_pct
+0,20,320,0.0625,3,-2.0
diff --git a/implementations/manufacturing_stress_forecasting/reports/data_understanding/variable_dictionary.csv b/implementations/manufacturing_stress_forecasting/reports/data_understanding/variable_dictionary.csv
new file mode 100644
index 000000000..0b3582cbc
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/data_understanding/variable_dictionary.csv
@@ -0,0 +1,20 @@
+,series_id,description,source,units,frequency,n_obs,start,end,category,observations,missing_values,missing_rate,reference_start,reference_end,first_release,last_release
+0,CPIAUCSL,Consumer Price Index for All Urban Consumers,FRED (CPIAUCSL),Index,MS,955,1947-01-01,2026-08-01,Active macro or market feature,955,0,0.0,1947-01-01,2026-08-01,1947-01-02,2026-08-03
+1,CPI_YOY,Consumer Price Index year-over-year change,Derived from FRED (CPIAUCSL),Percent,MS,943,1948-01-01,2026-08-01,Active macro or market feature,943,0,0.0,1948-01-01,2026-08-01,1948-01-02,2026-08-03
+2,CREDIT_SPREAD,Moody's Baa corporate bond yield minus 10-year Treasury yield (credit-spread proxy),FRED (BAA10Y),Percentage points,MS,489,1986-01-01,2026-09-01,Active macro or market feature,489,0,0.0,1986-01-01,2026-09-01,1986-02-03,2026-10-01
+3,FEDFUNDS,Effective federal funds rate,FRED (FEDFUNDS),Percent,MS,866,1954-07-01,2026-08-01,Active macro or market feature,866,0,0.0,1954-07-01,2026-08-01,1954-07-02,2026-08-03
+4,ICSA,Initial claims for unemployment insurance,FRED (ICSA),Number,MS,717,1967-01-01,2026-09-01,Active macro or market feature,717,0,0.0,1967-01-01,2026-09-01,1967-01-30,2026-09-14
+5,SPY_RETURN_12M,SPY trailing 12-month adjusted-close return,"Yahoo Finance (SPY), derived",Percent,MS,334,1999-01-01,2026-10-01,Active macro or market feature,334,0,0.0,1999-01-01,2026-10-01,1999-02-01,2026-10-05
+6,SPY_RETURN_3M,SPY trailing 3-month adjusted-close return,"Yahoo Finance (SPY), derived",Percent,MS,343,1998-04-01,2026-10-01,Active macro or market feature,343,0,0.0,1998-04-01,2026-10-01,1998-05-01,2026-10-05
+7,UNRATE,U.S. unemployment rate,FRED (UNRATE),Percent,MS,943,1948-01-01,2026-08-01,Active macro or market feature,943,0,0.0,1948-01-01,2026-08-01,1948-01-02,2026-08-03
+8,VIXCLS,CBOE volatility index,FRED (VIXCLS),Index,MS,441,1990-01-01,2026-09-01,Active macro or market feature,441,0,0.0,1990-01-01,2026-09-01,1990-02-01,2026-09-23
+9,XLI_RETURN_12M,XLI trailing 12-month adjusted-close return,"Yahoo Finance (XLI), derived",Percent,MS,323,1999-12-01,2026-10-01,Active macro or market feature,323,0,0.0,1999-12-01,2026-10-01,2000-01-03,2026-10-05
+10,XLI_RETURN_3M,XLI trailing 3-month adjusted-close return,"Yahoo Finance (XLI), derived",Percent,MS,332,1999-03-01,2026-10-01,Active macro or market feature,332,0,0.0,1999-03-01,2026-10-01,1999-04-01,2026-10-05
+11,YC_SPREAD,10-year minus 2-year Treasury yield spread,FRED (DGS10 minus DGS2),Percentage points,MS,604,1976-06-01,2026-09-01,Active macro or market feature,604,0,0.0,1976-06-01,2026-09-01,1976-07-01,2026-09-22
+17,manufacturing_stress,Binary U.S. manufacturing stress label: 1 when trailing 3-month IPMAN change is at or below -2.0%,Derived from FRED IPMAN,"Binary event (0=no stress, 1=stress)",MS,653,1972-04-01,2026-08-01,Binary target,653,0,0.0,1972-04-01,2026-08-01,1972-05-01,2026-09-01
+16,ipman_us_manufacturing_production,U.S. manufacturing industrial production index (IPMAN),FRED (IPMAN),Index,MS,656,1972-01-01,2026-08-01,IPMAN level,656,0,0.0,1972-01-01,2026-08-01,1972-02-01,2026-09-01
+13,ipman_change_1m_pct,Trailing 1-month percentage change in IPMAN,Derived from FRED IPMAN,Percent,MS,655,1972-02-01,2026-08-01,IPMAN momentum feature,655,0,0.0,1972-02-01,2026-08-01,1972-03-01,2026-09-01
+14,ipman_change_3m_pct,Trailing 3-month percentage change in IPMAN,Derived from FRED IPMAN,Percent,MS,653,1972-04-01,2026-08-01,IPMAN momentum feature,653,0,0.0,1972-04-01,2026-08-01,1972-05-01,2026-09-01
+15,ipman_change_6m_pct,Trailing 6-month percentage change in IPMAN,Derived from FRED IPMAN,Percent,MS,650,1972-07-01,2026-08-01,IPMAN momentum feature,650,0,0.0,1972-07-01,2026-08-01,1972-08-01,2026-09-01
+12,fed_funds_rate_pct,Month-end effective federal funds rate,"FRED (FEDFUNDS), derived monthly",Percent,MS,866,1954-07-01,2026-08-01,Registered macro or market input,866,0,0.0,1954-07-01,2026-08-01,1954-07-02,2026-08-03
+18,treasury_10y_minus_2y_pct_points,Month-end 10-year minus 2-year Treasury yield spread,"FRED (DGS10 minus DGS2), derived monthly",Percentage points,MS,604,1976-06-01,2026-09-01,Registered macro or market input,604,0,0.0,1976-06-01,2026-09-01,1976-07-01,2026-09-22
diff --git a/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.csv b/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.csv
new file mode 100644
index 000000000..0033ed87d
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.csv
@@ -0,0 +1,16 @@
+candidate,mean_brier,delta_vs_baseline,brier_skill,scored,skipped
+historical_frequency,0.035598323764741764,0.0,0.0,28,0
+logistic_c_0_001,0.037980926440430766,0.0023826026756890023,-0.06693019287747592,28,0
+xgb_50_depth2_lr0_03_minchild3_l2_5,0.04216220222804962,0.006563878463307853,-0.1843872904434063,28,0
+xgb_25_depth1_lr0_03,0.042621683593163096,0.007023359828421331,-0.19729467811002932,28,0
+xgb_25_depth1_lr0_05,0.04849049696927073,0.012892173204528963,-0.3621567490011417,28,0
+logistic_c_0_003,0.05162493536217167,0.016026611597429907,-0.45020691713870553,28,0
+xgb_50_depth1_lr0_03,0.05208227693716998,0.016483953172428213,-0.46305419551115734,28,0
+xgb_50_depth2_lr0_03,0.06432119853052291,0.02872287476578115,-0.8068603161093113,28,0
+xgb_50_depth1_lr0_05,0.06815507475485386,0.03255675099011209,-0.9145585394770135,28,0
+logistic_c_0_01,0.07156008314641618,0.03596175938167442,-1.010209346353904,28,0
+xgb_100_depth2_lr0_02,0.07486682958634613,0.03926850582160437,-1.1030998560808003,28,0
+logistic_c_0_03,0.08658870705719919,0.05099038329245743,-1.4323815814878529,28,0
+xgb_100_depth2_lr0_05,0.10357647436934099,0.06797815060459922,-1.9095885259611003,28,0
+logistic_c_0_1,0.1313974166211522,0.09579909285641044,-2.6911124661238772,28,0
+logistic_c_1_0,0.2326936666367168,0.19709534287197505,-5.536646730180802,28,0
diff --git a/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.md b/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.md
new file mode 100644
index 000000000..56457c26b
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.md
@@ -0,0 +1,17 @@
+| candidate                           |   mean_brier |   delta_vs_baseline |   brier_skill |   scored |   skipped |
+|:------------------------------------|-------------:|--------------------:|--------------:|---------:|----------:|
+| historical_frequency                |       0.0356 |              0.0000 |        0.0000 |       28 |         0 |
+| logistic_c_0_001                    |       0.0380 |              0.0024 |       -0.0669 |       28 |         0 |
+| xgb_50_depth2_lr0_03_minchild3_l2_5 |       0.0422 |              0.0066 |       -0.1844 |       28 |         0 |
+| xgb_25_depth1_lr0_03                |       0.0426 |              0.0070 |       -0.1973 |       28 |         0 |
+| xgb_25_depth1_lr0_05                |       0.0485 |              0.0129 |       -0.3622 |       28 |         0 |
+| logistic_c_0_003                    |       0.0516 |              0.0160 |       -0.4502 |       28 |         0 |
+| xgb_50_depth1_lr0_03                |       0.0521 |              0.0165 |       -0.4631 |       28 |         0 |
+| xgb_50_depth2_lr0_03                |       0.0643 |              0.0287 |       -0.8069 |       28 |         0 |
+| xgb_50_depth1_lr0_05                |       0.0682 |              0.0326 |       -0.9146 |       28 |         0 |
+| logistic_c_0_01                     |       0.0716 |              0.0360 |       -1.0102 |       28 |         0 |
+| xgb_100_depth2_lr0_02               |       0.0749 |              0.0393 |       -1.1031 |       28 |         0 |
+| logistic_c_0_03                     |       0.0866 |              0.0510 |       -1.4324 |       28 |         0 |
+| xgb_100_depth2_lr0_05               |       0.1036 |              0.0680 |       -1.9096 |       28 |         0 |
+| logistic_c_0_1                      |       0.1314 |              0.0958 |       -2.6911 |       28 |         0 |
+| logistic_c_1_0                      |       0.2327 |              0.1971 |       -5.5366 |       28 |         0 |
\ No newline at end of file
diff --git a/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.png b/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.png
new file mode 100644
index 000000000..959e99378
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/param_sweep/confirmation_2018_2024_results.png differ
diff --git a/implementations/manufacturing_stress_forecasting/reports/param_sweep/manufacturing_stress_parameter_sweep_report.md b/implementations/manufacturing_stress_forecasting/reports/param_sweep/manufacturing_stress_parameter_sweep_report.md
new file mode 100644
index 000000000..d507c1741
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/reports/param_sweep/manufacturing_stress_parameter_sweep_report.md
@@ -0,0 +1,115 @@
+# Manufacturing Stress Parameter Sweep Report
+
+## Executive Summary
+
+This report evaluates statistical forecasting candidates for the binary `manufacturing_stress` target. The target represents a manufacturing stress episode defined from a trailing three-month decline in industrial production. The forecast horizon is three monthly periods.
+
+The sweep found that the logistic candidate with `C=1.0` had the best pooled Brier score. However, the result is provisional because only five stress events occurred across the tuning period and they appeared in only one of the three evaluation folds. No XGBoost candidate improved on the historical-frequency baseline in pooled Brier score.
+
+## Evaluation Approach
+
+The experiment uses rolling-origin, chronological backtesting. At each forecast origin, the model is trained only on information available up to that origin, then evaluated on the future three-month horizon. This prevents future observations from entering the training data for an earlier forecast.
+
+The tuning period is divided into three folds:
+
+- 2000-2005
+- 2006-2011
+- 2012-2017
+
+The backtest stride is three months. Each candidate produces 72 scored forecast origins, with no skipped origins.
+
+The primary metric is the Brier score:
+
+$$
+\mathrm{Brier} = \frac{1}{N}\sum_{i=1}^{N}(p_i-y_i)^2
+$$
+
+where $p_i$ is the predicted probability and $y_i$ is the observed binary outcome. Lower values are better. Brier skill is measured relative to the historical-frequency baseline.
+
+## Models Evaluated
+
+The sweep evaluated 15 models in total:
+
+- 1 historical-frequency baseline
+- 6 logistic-regression candidates with different regularization strengths
+- 8 XGBoost candidates using shallow trees, conservative learning rates, and additional regularization settings
+
+The XGBoost parameter expansion included `min_child_weight` and `reg_lambda`.
+
+## Results
+
+| Candidate | Mean Brier | Brier skill | Folds beating baseline | Event folds | Interpretation |
+|---|---:|---:|---:|---:|---|
+| `logistic_c_1_0` | 0.0468 | +28.8% | 3 | 1 | Best overall; provisional |
+| `logistic_c_0_1` | 0.0523 | +20.5% | 3 | 1 | Strong logistic alternative |
+| `logistic_c_0_03` | 0.0537 | +18.5% | 3 | 1 | Strong logistic alternative |
+| `historical_frequency` | 0.0658 | 0.0% | 0 | 1 | Baseline |
+| `xgb_25_depth1_lr0_03` | 0.0723 | -9.8% | 2 | 1 | Best XGBoost, but below baseline |
+| `xgb_100_depth2_lr0_02` | 0.0728 | -10.6% | 2 | 1 | Below baseline |
+| `xgb_50_depth2_lr0_03` | 0.0736 | -11.7% | 2 | 1 | Below baseline |
+| `xgb_50_depth2_lr0_03_minchild3_l2_5` | 0.0743 | -12.9% | 1 | 1 | Stronger regularization did not help |
+
+The best XGBoost candidate still had a higher Brier score than the historical-frequency baseline. This means the new XGBoost settings should not be promoted based on this sweep.
+
+## Interpretation
+
+The automatic tuning rule selected `logistic_c_1_0` because it improved pooled Brier score, beat the baseline in all three folds, and improved on the event-bearing fold. That selection is useful as a development signal, but it is not conclusive evidence of generalization.
+
+The main limitation is event scarcity. Only five stress events occurred across all 72 forecast origins, and all events were concentrated in one fold. The apparently strong performance in the event-free folds can largely reflect conservative low probabilities rather than demonstrated event discrimination.
+
+The 2018-2024 window has already been inspected and therefore is not an untouched confirmation set. It should not be used to select or retune the candidate.
+
+## Historical Confirmation Diagnostic: 2018-2024
+
+For completeness, the baseline and all 14 swept candidates were also evaluated on the fixed 2018-2024 window. This is a historical diagnostic only, not a protected confirmation evaluation. The run used the same three-month forecast horizon and stride of three months. Each model produced 28 scored origins with no skipped origins.
+
+| Candidate | Mean Brier | Brier skill vs baseline |
+|---|---:|---:|
+| `historical_frequency` | 0.0356 | 0.0% |
+| `logistic_c_0_001` | 0.0380 | -6.7% |
+| `xgb_50_depth2_lr0_03_minchild3_l2_5` | 0.0422 | -18.4% |
+| `xgb_25_depth1_lr0_03` | 0.0426 | -19.7% |
+| `xgb_25_depth1_lr0_05` | 0.0485 | -36.2% |
+| `logistic_c_0_003` | 0.0516 | -45.0% |
+| `xgb_50_depth1_lr0_03` | 0.0521 | -46.3% |
+| `xgb_50_depth2_lr0_03` | 0.0643 | -80.7% |
+| `xgb_50_depth1_lr0_05` | 0.0682 | -91.5% |
+| `logistic_c_0.01` | 0.0716 | -101.0% |
+| `xgb_100_depth2_lr0_02` | 0.0749 | -110.3% |
+| `logistic_c_0.03` | 0.0866 | -143.2% |
+| `xgb_100_depth2_lr0_05` | 0.1036 | -191.0% |
+| `logistic_c_0.1` | 0.1314 | -269.1% |
+| `logistic_c_1.0` | 0.2327 | -553.7% |
+
+The historical-frequency baseline ranked first. The tuning winner, `logistic_c_1_0`, ranked last in this diagnostic. The selected development configuration is now `logistic_c_0_001` with `xgb_50_depth2_lr0_03_minchild3_l2_5`; these are operational choices for the fixed workbench, not a claim that either model outperforms the baseline.
+
+The machine-readable exports are available in [`confirmation_2018_2024_results.csv`](confirmation_2018_2024_results.csv) and [`confirmation_2018_2024_results.md`](confirmation_2018_2024_results.md).
+
+The corresponding confirmation plots are available in [`confirmation_2018_2024_results.png`](confirmation_2018_2024_results.png):
+
+![2018-2024 confirmation results](confirmation_2018_2024_results.png)
+
+## Recommendation
+
+1. Use `logistic_c_0_001` as the selected logistic candidate.
+2. Use `xgb_50_depth2_lr0_03_minchild3_l2_5` as the selected XGBoost candidate.
+3. Use the logistic prediction alone as the hybrid numerical anchor.
+4. Keep the historical-frequency model as the benchmark; it still ranked first in the inspected confirmation diagnostic.
+5. Treat the selected configuration as frozen for a fresh prospective evaluation, without claiming confirmation superiority.
+6. Revisit model selection when more stress events are available across multiple chronological folds.
+
+## Reproducing the Plot
+
+Run the parameter-sweep notebook through the comparison cell, then run the plotting cell. The plotting cell produces three views:
+
+1. Pooled Brier score by candidate.
+2. Brier score across the three chronological folds.
+3. Pooled Brier skill relative to historical frequency.
+
+The exported figure is saved as `parameter_sweep_results.png` in this report directory.
+
+![Parameter sweep results](parameter_sweep_results.png)
+
+## Scope and Caveats
+
+This is a historical tuning analysis, not a protected prospective evaluation. The results describe calibration and probabilistic accuracy under the selected historical backtest design. They do not establish that the provisional logistic candidate will outperform the baseline in future data.
diff --git a/implementations/manufacturing_stress_forecasting/reports/param_sweep/parameter_sweep_results.png b/implementations/manufacturing_stress_forecasting/reports/param_sweep/parameter_sweep_results.png
new file mode 100644
index 000000000..ab48af7bc
Binary files /dev/null and b/implementations/manufacturing_stress_forecasting/reports/param_sweep/parameter_sweep_results.png differ
diff --git a/implementations/manufacturing_stress_forecasting/run_agent_backtest.py b/implementations/manufacturing_stress_forecasting/run_agent_backtest.py
new file mode 100644
index 000000000..6a07ea4df
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/run_agent_backtest.py
@@ -0,0 +1,147 @@
+"""Run the cached, token-limited manufacturing-stress agent backtest."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from datetime import datetime
+from pathlib import Path
+
+import yaml
+from aieng.forecasting.data import DataService
+from aieng.forecasting.evaluation import BacktestSpec, backtest
+from aieng.forecasting.evaluation.artifacts import load_backtest_result, save_backtest_result
+from aieng.forecasting.evaluation.backtest import BacktestResult
+from aieng.forecasting.evaluation.predictor import Predictor
+from aieng.forecasting.methods import HistoricalFrequencyPredictor
+from manufacturing_stress_forecasting.analyst_agent import build_manufacturing_stress_agent_predictor
+from manufacturing_stress_forecasting.data import build_manufacturing_stress_service
+from manufacturing_stress_forecasting.predictors import (
+    ManufacturingStressLogisticPredictor,
+    ManufacturingStressXGBoostPredictor,
+)
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+SPEC_PATH = Path(__file__).resolve().parent / "specs" / "manufacturing_stress_smoke.yaml"
+STORE_DIR = REPO_ROOT / "data" / "predictions"
+# Bump when prompt or predictor semantics change without changing predictor IDs.
+CACHE_VERSION = "v3"
+
+
+def backtest_cache_id(spec: BacktestSpec) -> str:
+    """Return a versioned cache key tied to the complete backtest spec."""
+    serialized = json.dumps(spec.model_dump(mode="json"), sort_keys=True, separators=(",", ":"))
+    fingerprint = hashlib.sha256(serialized.encode()).hexdigest()[:10]
+    return f"manufacturing_stress_smoke_llmp_{CACHE_VERSION}_{fingerprint}"
+
+
+def _cache_rejection_reason(cached: BacktestResult, spec: BacktestSpec) -> str | None:
+    """Explain why a cached result is unsafe to reuse, if applicable."""
+    if cached.spec != spec:
+        return "the cached backtest specification differs"
+    if cached.skipped_origins:
+        return f"the cached result skipped {cached.skipped_origins} origin(s)"
+    return None
+
+
+def _parse_args() -> argparse.Namespace:
+    """Parse explicit controls for the paid backtest."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--force-refresh",
+        action="store_true",
+        help="Re-run predictors and overwrite cached results.",
+    )
+    return parser.parse_args()
+
+
+def run_or_load_backtest(
+    predictor: Predictor,
+    spec: BacktestSpec,
+    service: DataService,
+    *,
+    force_refresh: bool,
+    spec_id: str | None = None,
+    store_dir: Path = STORE_DIR,
+) -> BacktestResult:
+    """Load a compatible complete result or run it and cache only if complete."""
+    predictor_id = predictor.predictor_id
+    resolved_spec_id = spec_id or backtest_cache_id(spec)
+    if not force_refresh:
+        cached = load_backtest_result(resolved_spec_id, predictor_id, store_dir=store_dir)
+        if cached is not None:
+            rejection_reason = _cache_rejection_reason(cached, spec)
+            if rejection_reason is None:
+                path = store_dir / resolved_spec_id / f"{predictor_id}.yaml"
+                print(f"{predictor_id}: loaded {path}")
+                return cached
+            print(f"{predictor_id}: ignoring cache because {rejection_reason}")
+
+    result = backtest(
+        predictor=predictor,
+        spec=spec,
+        data_service=service,
+        max_retries=1,
+        retry_delay=1.0,
+    )
+    if result.skipped_origins:
+        print(f"{predictor_id}: not cached because the run skipped {result.skipped_origins} origin(s)")
+        return result
+
+    path = save_backtest_result(result, spec_id=resolved_spec_id, store_dir=store_dir)
+    print(f"{predictor_id}: saved {path}")
+    return result
+
+
+def scored_origin_keys(result: BacktestResult) -> set[tuple[datetime, datetime]]:
+    """Return the exact forecast-origin/date pairs scored in a result."""
+    return {(prediction.as_of, prediction.forecast_date) for prediction in result.predictions}
+
+
+def validate_comparable_results(results: list[BacktestResult]) -> None:
+    """Require all model comparisons to use identical scored origins."""
+    if len(results) < 2:
+        return
+
+    reference = results[0]
+    reference_keys = scored_origin_keys(reference)
+    for result in results[1:]:
+        result_keys = scored_origin_keys(result)
+        if result_keys != reference_keys:
+            missing = len(reference_keys - result_keys)
+            extra = len(result_keys - reference_keys)
+            raise ValueError(
+                f"Cannot compare {result.predictor_id} with {reference.predictor_id}: "
+                f"scored origins differ (missing={missing}, extra={extra})."
+            )
+
+
+def main() -> None:
+    """Run all predictors under the identical binary Brier-score harness."""
+    args = _parse_args()
+    with SPEC_PATH.open() as file:
+        spec = BacktestSpec.model_validate(yaml.safe_load(file))
+
+    service = build_manufacturing_stress_service()
+    predictors = [
+        HistoricalFrequencyPredictor(),
+        ManufacturingStressLogisticPredictor(),
+        ManufacturingStressXGBoostPredictor(),
+        build_manufacturing_stress_agent_predictor(anonymize_dates=True),
+    ]
+    results: list[BacktestResult] = []
+    for predictor in predictors:
+        result = run_or_load_backtest(predictor, spec, service, force_refresh=args.force_refresh)
+        results.append(result)
+        print(
+            f"{result.predictor_id}: {result.mean_score:.4f} mean {result.metric}; "
+            f"scored={len(result.scores)} skipped={result.skipped_origins}"
+        )
+    validate_comparable_results(results)
+    print("All predictors were scored on identical forecast origins.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/implementations/manufacturing_stress_forecasting/run_agent_prediction.py b/implementations/manufacturing_stress_forecasting/run_agent_prediction.py
new file mode 100644
index 000000000..2c2f668f7
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/run_agent_prediction.py
@@ -0,0 +1,58 @@
+"""Run one current, quantitative-only manufacturing-stress agent forecast."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pandas as pd
+import yaml
+from aieng.forecasting.evaluation import BacktestSpec
+from aieng.forecasting.methods import HistoricalFrequencyPredictor
+from manufacturing_stress_forecasting.analyst_agent import build_manufacturing_stress_agent_predictor
+from manufacturing_stress_forecasting.data import (
+    IPMAN_SERIES_ID,
+    build_manufacturing_stress_service,
+)
+from manufacturing_stress_forecasting.predictors import (
+    ManufacturingStressLogisticPredictor,
+    ManufacturingStressXGBoostPredictor,
+)
+
+
+SPEC_PATH = Path(__file__).resolve().parent / "specs" / "manufacturing_stress_smoke.yaml"
+
+
+def main() -> None:
+    """Forecast from the most recent cached IPMAN release date."""
+    with SPEC_PATH.open() as file:
+        task = BacktestSpec.model_validate(yaml.safe_load(file)).task
+
+    service = build_manufacturing_stress_service()
+    full_ipman = service.get_series(IPMAN_SERIES_ID, as_of=pd.Timestamp("2100-01-01").to_pydatetime())
+    as_of = pd.Timestamp(full_ipman["released_at"].max())
+    context = service.context(as_of=as_of.to_pydatetime())
+
+    predictors = [
+        HistoricalFrequencyPredictor(),
+        ManufacturingStressLogisticPredictor(),
+        ManufacturingStressXGBoostPredictor(),
+        build_manufacturing_stress_agent_predictor(),
+    ]
+    print(f"Forecast origin: {as_of.date()}")
+    print(
+        f"Latest visible IPMAN reference month: {pd.Timestamp(context.get_series(IPMAN_SERIES_ID)['timestamp'].max()).date()}"
+    )
+    for predictor in predictors:
+        prediction = predictor.predict(task, context)[0]
+        output = {
+            "predictor_id": prediction.predictor_id,
+            "forecast_date": str(pd.Timestamp(prediction.forecast_date).date()),
+            "stress_probability": prediction.payload.probability,
+            "metadata": prediction.metadata,
+        }
+        print(json.dumps(output, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/implementations/manufacturing_stress_forecasting/run_parameter_smoke.py b/implementations/manufacturing_stress_forecasting/run_parameter_smoke.py
new file mode 100644
index 000000000..06570d65c
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/run_parameter_smoke.py
@@ -0,0 +1,478 @@
+"""Run a controlled deterministic parameter sweep for manufacturing stress."""
+
+from __future__ import annotations
+
+import argparse
+from collections.abc import Callable
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+
+import pandas as pd
+import yaml
+from aieng.forecasting.data import DataService
+from aieng.forecasting.evaluation import BacktestSpec, backtest
+from aieng.forecasting.evaluation.backtest import BacktestResult
+from aieng.forecasting.evaluation.predictor import Predictor
+from aieng.forecasting.methods import HistoricalFrequencyPredictor
+from manufacturing_stress_forecasting.data import build_manufacturing_stress_service
+from manufacturing_stress_forecasting.predictors import (
+    ManufacturingStressLogisticPredictor,
+    ManufacturingStressXGBoostPredictor,
+)
+from manufacturing_stress_forecasting.targets import DEFAULT_STRESS_THRESHOLD_PCT
+
+
+SPEC_PATH = Path(__file__).resolve().parent / "specs" / "manufacturing_stress_smoke.yaml"
+BASELINE_NAME = "historical_frequency"
+STAGE_WINDOWS = {
+    "tune": (datetime(2000, 1, 1), datetime(2017, 12, 1)),
+    "confirm": (datetime(2018, 1, 1), datetime(2024, 12, 1)),
+}
+TUNING_FOLDS = (
+    ("2000-2005", datetime(2000, 1, 1), datetime(2005, 12, 1)),
+    ("2006-2011", datetime(2006, 1, 1), datetime(2011, 12, 1)),
+    ("2012-2017", datetime(2012, 1, 1), datetime(2017, 12, 1)),
+)
+
+
+@dataclass(frozen=True)
+class Candidate:
+    """Named predictor factory used by the controlled sweep."""
+
+    name: str
+    factory: Callable[[], Predictor]
+
+
+CANDIDATES = (
+    Candidate(
+        "logistic_c_0_001",
+        lambda: ManufacturingStressLogisticPredictor(regularization_c=0.001),
+    ),
+    Candidate(
+        "logistic_c_0_003",
+        lambda: ManufacturingStressLogisticPredictor(regularization_c=0.003),
+    ),
+    Candidate(
+        "logistic_c_0_01",
+        lambda: ManufacturingStressLogisticPredictor(regularization_c=0.01),
+    ),
+    Candidate(
+        "logistic_c_0_03",
+        lambda: ManufacturingStressLogisticPredictor(regularization_c=0.03),
+    ),
+    Candidate(
+        "logistic_c_0_1",
+        lambda: ManufacturingStressLogisticPredictor(regularization_c=0.1),
+    ),
+    Candidate(
+        "logistic_c_1_0",
+        lambda: ManufacturingStressLogisticPredictor(regularization_c=1.0),
+    ),
+    Candidate(
+        "xgb_25_depth1_lr0_05",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=25,
+            max_depth=1,
+            learning_rate=0.05,
+        ),
+    ),
+    Candidate(
+        "xgb_50_depth1_lr0_05",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=50,
+            max_depth=1,
+            learning_rate=0.05,
+        ),
+    ),
+    Candidate(
+        "xgb_50_depth2_lr0_03",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=50,
+            max_depth=2,
+            learning_rate=0.03,
+        ),
+    ),
+    Candidate(
+        "xgb_100_depth2_lr0_05",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=100,
+            max_depth=2,
+            learning_rate=0.05,
+        ),
+    ),
+    Candidate(
+        "xgb_25_depth1_lr0_03",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=25,
+            max_depth=1,
+            learning_rate=0.03,
+        ),
+    ),
+    Candidate(
+        "xgb_50_depth1_lr0_03",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=50,
+            max_depth=1,
+            learning_rate=0.03,
+        ),
+    ),
+    Candidate(
+        "xgb_100_depth2_lr0_02",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=100,
+            max_depth=2,
+            learning_rate=0.02,
+        ),
+    ),
+    Candidate(
+        "xgb_50_depth2_lr0_03_minchild3_l2_5",
+        lambda: ManufacturingStressXGBoostPredictor(
+            n_estimators=50,
+            max_depth=2,
+            learning_rate=0.03,
+            min_child_weight=3.0,
+            reg_lambda=5.0,
+        ),
+    ),
+)
+
+
+def _parse_args() -> argparse.Namespace:
+    """Parse the sweep stage and forecast-origin stride."""
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--stage",
+        choices=tuple(STAGE_WINDOWS),
+        default="tune",
+        help="Tune all candidates or confirm exactly one previously selected candidate.",
+    )
+    parser.add_argument(
+        "--candidate",
+        choices=tuple(candidate.name for candidate in CANDIDATES),
+        help="Candidate to evaluate during the confirm stage.",
+    )
+    parser.add_argument(
+        "--stride",
+        type=int,
+        choices=(1, 3),
+        default=3,
+        help="Months between forecast origins. This does not change the forecast horizon.",
+    )
+    parser.add_argument(
+        "--stress-threshold-pct",
+        type=float,
+        default=DEFAULT_STRESS_THRESHOLD_PCT,
+        help=(
+            "Trailing 3-month IPMAN percentage-change threshold that defines the stress "
+            f"label (must be negative; default {DEFAULT_STRESS_THRESHOLD_PCT}). Changing this "
+            "redefines the target, so tune and confirm results at different thresholds are not "
+            "comparable to each other."
+        ),
+    )
+    args = parser.parse_args()
+    if args.stress_threshold_pct >= 0:
+        parser.error(f"--stress-threshold-pct must be negative; got {args.stress_threshold_pct}.")
+    if args.stage == "confirm" and args.candidate is None:
+        parser.error("--candidate is required when --stage confirm is selected.")
+    if args.stage == "tune" and args.candidate is not None:
+        parser.error("--candidate is only valid when --stage confirm is selected.")
+    return args
+
+
+def build_experiment_spec(base_spec: BacktestSpec, *, stage: str, stride: int) -> BacktestSpec:
+    """Return the fixed tuning or confirmation window without changing the task."""
+    if stage not in STAGE_WINDOWS:
+        raise ValueError(f"Unknown stage {stage!r}; expected one of {tuple(STAGE_WINDOWS)}.")
+    if stride not in (1, 3):
+        raise ValueError(f"stride must be 1 or 3; got {stride}.")
+    start, end = STAGE_WINDOWS[stage]
+    return base_spec.model_copy(update={"start": start, "end": end, "stride": stride})
+
+
+def predictors_for_stage(stage: str, candidate_name: str | None = None) -> list[tuple[str, Predictor]]:
+    """Build the baseline plus all tuning candidates or one confirmation candidate."""
+    if stage == "tune":
+        selected = CANDIDATES
+    elif stage == "confirm":
+        if candidate_name is None:
+            raise ValueError("candidate_name is required for the confirm stage.")
+        matches = tuple(candidate for candidate in CANDIDATES if candidate.name == candidate_name)
+        if not matches:
+            raise ValueError(f"Unknown candidate {candidate_name!r}.")
+        selected = matches
+    else:
+        raise ValueError(f"Unknown stage {stage!r}; expected one of {tuple(STAGE_WINDOWS)}.")
+
+    return [
+        (BASELINE_NAME, HistoricalFrequencyPredictor()),
+        *((candidate.name, candidate.factory()) for candidate in selected),
+    ]
+
+
+def _scored_origin_keys(result: BacktestResult) -> set[tuple[datetime, datetime]]:
+    """Return the exact origin and forecast-date pairs scored by one result."""
+    return {(prediction.as_of, prediction.forecast_date) for prediction in result.predictions}
+
+
+def validate_comparable_results(results: list[tuple[str, BacktestResult]]) -> None:
+    """Reject comparisons that were not scored on identical dates."""
+    if len(results) < 2:
+        return
+    reference_name, reference = results[0]
+    reference_keys = _scored_origin_keys(reference)
+    for name, result in results[1:]:
+        keys = _scored_origin_keys(result)
+        if keys != reference_keys:
+            missing = len(reference_keys - keys)
+            extra = len(keys - reference_keys)
+            raise ValueError(
+                f"Cannot compare {name} with {reference_name}: "
+                f"scored origins differ (missing={missing}, extra={extra})."
+            )
+
+
+def run_candidates(
+    named_predictors: list[tuple[str, Predictor]],
+    *,
+    spec: BacktestSpec,
+    service: DataService,
+) -> list[tuple[str, BacktestResult]]:
+    """Backtest each named predictor without caches or LLM calls."""
+    results: list[tuple[str, BacktestResult]] = []
+    for name, predictor in named_predictors:
+        print(f"Running {name}...")
+        result = backtest(predictor=predictor, spec=spec, data_service=service)
+        results.append((name, result))
+    validate_comparable_results(results)
+    return results
+
+
+def build_tuning_fold_specs(base_spec: BacktestSpec, *, stride: int) -> dict[str, BacktestSpec]:
+    """Build chronological validation specs that exactly cover the tuning window."""
+    tuning_spec = build_experiment_spec(base_spec, stage="tune", stride=stride)
+    return {name: tuning_spec.model_copy(update={"start": start, "end": end}) for name, start, end in TUNING_FOLDS}
+
+
+def run_tuning_folds(
+    *,
+    base_spec: BacktestSpec,
+    stride: int,
+    service: DataService,
+) -> dict[str, list[tuple[str, BacktestResult]]]:
+    """Run all tuning candidates independently on each chronological fold."""
+    fold_results = {}
+    for fold_name, spec in build_tuning_fold_specs(base_spec, stride=stride).items():
+        print(f"Tuning fold {fold_name}...")
+        fold_results[fold_name] = run_candidates(predictors_for_stage("tune"), spec=spec, service=service)
+    return fold_results
+
+
+def tuning_summary_tables(
+    fold_results: dict[str, list[tuple[str, BacktestResult]]],
+    *,
+    service: DataService,
+) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    """Summarize fold and pooled skill plus each scored probability/outcome pair."""
+    if not fold_results:
+        raise ValueError("fold_results must contain at least one tuning fold.")
+
+    first_results = next(iter(fold_results.values()))
+    if not first_results:
+        raise ValueError("Each tuning fold must include the historical-frequency baseline.")
+    task = first_results[0][1].spec.task
+    target = service.get_series(task.target_series_id, as_of=datetime.now(tz=timezone.utc).replace(tzinfo=None))
+    outcomes_by_date = {
+        pd.Timestamp(timestamp): float(value)
+        for timestamp, value in zip(target["timestamp"], target["value"], strict=True)
+    }
+
+    fold_rows = []
+    origin_rows = []
+    pooled_scores: dict[str, list[float]] = {}
+    pooled_skipped: dict[str, int] = {}
+    pooled_events: dict[str, int] = {}
+    expected_names = {name for name, _result in first_results}
+
+    for fold_name, results in fold_results.items():
+        if {name for name, _result in results} != expected_names:
+            raise ValueError(f"Tuning fold {fold_name} has a different candidate set.")
+        validate_comparable_results(results)
+        result_by_name = dict(results)
+        if BASELINE_NAME not in result_by_name:
+            raise ValueError(f"Every tuning fold must include {BASELINE_NAME}.")
+        baseline_brier = result_by_name[BASELINE_NAME].mean_score
+
+        for name, result in results:
+            if result.metric != "brier":
+                raise ValueError(f"Tuning candidate {name} must use the binary Brier metric.")
+            scored_outcomes = []
+            for prediction, score in zip(result.predictions, result.scores, strict=True):
+                forecast_date = pd.Timestamp(prediction.forecast_date)
+                if forecast_date not in outcomes_by_date:
+                    raise ValueError(f"No resolved target value for forecast date {forecast_date.date()}.")
+                outcome = outcomes_by_date[forecast_date]
+                scored_outcomes.append(outcome)
+                origin_rows.append(
+                    {
+                        "candidate": name,
+                        "fold": fold_name,
+                        "as_of": prediction.as_of,
+                        "forecast_date": prediction.forecast_date,
+                        "probability": float(prediction.payload.probability),
+                        "outcome": outcome,
+                        "brier": score,
+                    }
+                )
+
+            fold_rows.append(
+                {
+                    "candidate": name,
+                    "fold": fold_name,
+                    "mean_brier": result.mean_score,
+                    "delta_vs_baseline": result.mean_score - baseline_brier,
+                    "brier_skill": float("nan") if baseline_brier == 0 else 1.0 - result.mean_score / baseline_brier,
+                    "scored": len(result.scores),
+                    "skipped": result.skipped_origins,
+                    "stress_events": int(sum(scored_outcomes)),
+                }
+            )
+            pooled_scores.setdefault(name, []).extend(result.scores)
+            pooled_skipped[name] = pooled_skipped.get(name, 0) + result.skipped_origins
+            pooled_events[name] = pooled_events.get(name, 0) + int(sum(scored_outcomes))
+
+    fold_table = pd.DataFrame(fold_rows)
+    pooled_baseline = sum(pooled_scores[BASELINE_NAME]) / len(pooled_scores[BASELINE_NAME])
+    pooled_rows = []
+    for name, scores in pooled_scores.items():
+        mean_brier = sum(scores) / len(scores)
+        candidate_fold_rows = [row for row in fold_rows if row["candidate"] == name]
+        folds_beating_baseline = sum(row["delta_vs_baseline"] < 0 for row in candidate_fold_rows)
+        event_folds = sum(row["stress_events"] > 0 for row in candidate_fold_rows)
+        event_folds_beating_baseline = sum(
+            row["stress_events"] > 0 and row["delta_vs_baseline"] < 0 for row in candidate_fold_rows
+        )
+        pooled_rows.append(
+            {
+                "candidate": name,
+                "mean_brier": mean_brier,
+                "delta_vs_baseline": mean_brier - pooled_baseline,
+                "brier_skill": float("nan") if pooled_baseline == 0 else 1.0 - mean_brier / pooled_baseline,
+                "scored": len(scores),
+                "skipped": pooled_skipped[name],
+                "stress_events": pooled_events[name],
+                "folds_beating_baseline": folds_beating_baseline,
+                "event_folds": event_folds,
+                "event_folds_beating_baseline": event_folds_beating_baseline,
+            }
+        )
+
+    pooled_table = pd.DataFrame(pooled_rows).sort_values(["mean_brier", "candidate"]).reset_index(drop=True)
+    origin_table = pd.DataFrame(origin_rows).sort_values(["fold", "candidate", "as_of"]).reset_index(drop=True)
+    return fold_table, pooled_table, origin_table
+
+
+def select_tuning_candidate(pooled_table: pd.DataFrame) -> pd.Series | None:
+    """Select the best candidate only when pooled and fold-level skill agree."""
+    eligible = pooled_table[
+        (pooled_table["candidate"] != BASELINE_NAME)
+        & (pooled_table["delta_vs_baseline"] < 0)
+        & (pooled_table["folds_beating_baseline"] >= 2)
+        & (pooled_table["event_folds_beating_baseline"] >= 1)
+    ]
+    if eligible.empty:
+        return None
+    return eligible.sort_values(["mean_brier", "candidate"]).iloc[0]
+
+
+def comparison_table(results: list[tuple[str, BacktestResult]]) -> pd.DataFrame:
+    """Summarize Brier score and skill relative to historical frequency."""
+    result_by_name = dict(results)
+    if BASELINE_NAME not in result_by_name:
+        raise ValueError(f"Results must include {BASELINE_NAME}.")
+    if any(result.metric != "brier" for _, result in results):
+        raise ValueError("The parameter sweep requires binary Brier-score results.")
+
+    baseline_brier = result_by_name[BASELINE_NAME].mean_score
+    rows = []
+    for name, result in results:
+        skill = float("nan") if baseline_brier == 0 else 1.0 - result.mean_score / baseline_brier
+        rows.append(
+            {
+                "candidate": name,
+                "mean_brier": result.mean_score,
+                "delta_vs_baseline": result.mean_score - baseline_brier,
+                "brier_skill": skill,
+                "scored": len(result.scores),
+                "skipped": result.skipped_origins,
+            }
+        )
+    return pd.DataFrame(rows).sort_values(["mean_brier", "candidate"]).reset_index(drop=True)
+
+
+def _print_table(table: pd.DataFrame) -> None:
+    """Print stable, readable score precision."""
+    print(
+        table.to_string(
+            index=False,
+            formatters={
+                "mean_brier": "{:.4f}".format,
+                "delta_vs_baseline": "{:+.4f}".format,
+                "brier_skill": "{:+.1%}".format,
+            },
+        )
+    )
+
+
+def main() -> None:
+    """Run the requested stage and report comparable deterministic scores."""
+    args = _parse_args()
+    with SPEC_PATH.open() as file:
+        base_spec = BacktestSpec.model_validate(yaml.safe_load(file))
+
+    service = build_manufacturing_stress_service(stress_threshold_pct=args.stress_threshold_pct)
+
+    if args.stage == "tune":
+        spec = build_experiment_spec(base_spec, stage="tune", stride=args.stride)
+        print(
+            f"Stage=tune; origins={spec.start.date()} to {spec.end.date()}; "
+            f"stride={spec.stride}; horizon={spec.task.horizons[0]} month(s); "
+            f"stress_threshold_pct={args.stress_threshold_pct}"
+        )
+        fold_results = run_tuning_folds(base_spec=base_spec, stride=args.stride, service=service)
+        fold_table, table, _origin_table = tuning_summary_tables(fold_results, service=service)
+        print("\nFold-level Brier scores:")
+        _print_table(fold_table)
+        print("\nPooled tuning Brier scores:")
+        _print_table(table)
+
+        best_model = select_tuning_candidate(table)
+        if best_model is None:
+            print("\nNo candidate beat historical frequency overall and in at least two of three folds.")
+        else:
+            candidate_name = str(best_model["candidate"])
+            print(f"\nStable tuning candidate: {candidate_name}")
+            print("Confirm only this selected candidate with:")
+            print(
+                "uv run --directory implementations python -m "
+                "manufacturing_stress_forecasting.run_parameter_smoke "
+                f"--stage confirm --candidate {candidate_name} --stride {args.stride}"
+            )
+    else:
+        spec = build_experiment_spec(base_spec, stage="confirm", stride=args.stride)
+        named_predictors = predictors_for_stage(args.stage, args.candidate)
+        print(
+            f"Stage=confirm; origins={spec.start.date()} to {spec.end.date()}; "
+            f"stride={spec.stride}; horizon={spec.task.horizons[0]} month(s); "
+            f"stress_threshold_pct={args.stress_threshold_pct}"
+        )
+        results = run_candidates(named_predictors, spec=spec, service=service)
+        table = comparison_table(results)
+        print()
+        _print_table(table)
+        print("\nHistorical diagnostic completed without evaluating the other candidates.")
+        print("The 2018–2024 window has already been inspected; do not tune against it.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/implementations/manufacturing_stress_forecasting/run_smoke.py b/implementations/manufacturing_stress_forecasting/run_smoke.py
new file mode 100644
index 000000000..3105e92d9
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/run_smoke.py
@@ -0,0 +1,35 @@
+"""Run the two-predictor manufacturing-stress smoke backtest."""
+
+from pathlib import Path
+
+import yaml
+from aieng.forecasting.evaluation import BacktestSpec, backtest
+from aieng.forecasting.methods import HistoricalFrequencyPredictor
+from manufacturing_stress_forecasting.data import build_manufacturing_stress_service
+from manufacturing_stress_forecasting.predictors import (
+    ManufacturingStressLogisticPredictor,
+    ManufacturingStressXGBoostPredictor,
+)
+
+
+SPEC_PATH = Path(__file__).resolve().parent / "specs" / "manufacturing_stress_smoke.yaml"
+
+
+def main() -> None:
+    """Load data, run both baselines, and print their mean Brier scores."""
+    with SPEC_PATH.open() as file:
+        spec = BacktestSpec.model_validate(yaml.safe_load(file))
+
+    service = build_manufacturing_stress_service()
+    predictors = [
+        HistoricalFrequencyPredictor(),
+        ManufacturingStressLogisticPredictor(),
+        ManufacturingStressXGBoostPredictor(),
+    ]
+    for predictor in predictors:
+        result = backtest(predictor=predictor, spec=spec, data_service=service)
+        print(f"{predictor.predictor_id}: {result.mean_score:.4f} mean {result.metric}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/implementations/manufacturing_stress_forecasting/smoke_hybrid_agent.py b/implementations/manufacturing_stress_forecasting/smoke_hybrid_agent.py
new file mode 100644
index 000000000..8a4c47794
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/smoke_hybrid_agent.py
@@ -0,0 +1,69 @@
+"""Run the opt-in hybrid manufacturing-stress smoke test."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import pandas as pd
+import yaml
+from aieng.forecasting.evaluation import BacktestSpec
+from dotenv import load_dotenv
+from manufacturing_stress_forecasting.data import build_manufacturing_stress_service
+from manufacturing_stress_forecasting.hybrid_agent import (
+    HybridAgentPredictor,
+    build_hybrid_agent_predictor,
+    build_numerical_anchor,
+    validate_agent_adjustment,
+)
+
+
+SPEC_PATH = Path(__file__).resolve().parent / "specs" / "manufacturing_stress_smoke.yaml"
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--as-of", default="2017-01-01", help="Monthly forecast origin for the smoke test.")
+    parser.add_argument("--run-agent", action="store_true", help="Make one real LLM call through the ADK runner.")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = _parse_args()
+    repo_root = Path(__file__).resolve().parents[2]
+    load_dotenv(repo_root / ".env", override=False)
+    with SPEC_PATH.open() as file:
+        spec = BacktestSpec.model_validate(yaml.safe_load(file))
+
+    service = build_manufacturing_stress_service(
+        cache_dir=repo_root / "data" / "fred",
+        yahoo_cache_dir=repo_root / "data" / "yfinance",
+    )
+    as_of = pd.Timestamp(args.as_of).to_pydatetime()
+    context = service.context(as_of=as_of)
+    anchor = build_numerical_anchor(spec.task, context)
+    dry_run_validation = validate_agent_adjustment(anchor.probability, anchor.probability)
+
+    print(f"Origin: {as_of.date()}")
+    print(f"Target: {spec.task.target_series_id}; horizon: {spec.task.horizons[0]} month(s)")
+    print("Features: 15")
+    print(f"Logistic ({anchor.as_dict()['anchor_method']} input): {anchor.logistic_probability:.6f}")
+    print(f"XGBoost: {anchor.xgboost_probability:.6f}")
+    print(f"Anchor: {anchor.probability:.6f}")
+    print(f"Dry-run validation: {dry_run_validation['validation_status']}")
+
+    if not args.run_agent:
+        print("Agent call skipped. Use --run-agent to invoke the existing ADK/Vector path.")
+        return
+
+    predictor = HybridAgentPredictor(build_hybrid_agent_predictor(anonymize_dates=False))
+    prediction = predictor.predict(spec.task, context)[0]
+    print(f"Adjusted forecast probability: {prediction.payload.probability:.6f}")
+    print(f"Anchor probability: {prediction.metadata['anchor_probability']:.6f}")
+    print(f"Agent proposal: {prediction.metadata['agent_probability']:.6f}")
+    print(f"Agent adjustment: {prediction.metadata['agent_adjustment']:.6f}")
+    print(f"Validation: {prediction.metadata['validation_status']}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/implementations/manufacturing_stress_forecasting/specs/manufacturing_stress_protected_eval.yaml b/implementations/manufacturing_stress_forecasting/specs/manufacturing_stress_protected_eval.yaml
new file mode 100644
index 000000000..acaf3b671
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/specs/manufacturing_stress_protected_eval.yaml
@@ -0,0 +1,18 @@
+description: >-
+  Limited protected evaluation window for the manufacturing stress forecasting
+  task. Each predictor evaluation consumes one of five tracked runs.
+spec_id: manufacturing_stress_protected_eval_v1
+
+task:
+  task_id: manufacturing_stress_3m
+  target_series_id: manufacturing_stress
+  horizons: [3]
+  frequency: MS
+  payload_type: binary
+  description: Probability that manufacturing will be under stress three months ahead.
+
+start: "2023-01-01"
+end: "2024-12-01"
+stride: 3
+warmup: 60
+max_runs: 5
diff --git a/implementations/manufacturing_stress_forecasting/specs/manufacturing_stress_smoke.yaml b/implementations/manufacturing_stress_forecasting/specs/manufacturing_stress_smoke.yaml
new file mode 100644
index 000000000..39cc9e5cc
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/specs/manufacturing_stress_smoke.yaml
@@ -0,0 +1,21 @@
+# Initial Track 1 experiment: one binary target, one three-month horizon.
+
+description: >-
+  Development backtest for forecasting whether U.S. manufacturing will be
+  under IPMAN-defined stress three months after each forecast origin.
+
+task:
+  task_id: manufacturing_stress_3m
+  target_series_id: manufacturing_stress
+  horizons: [3]
+  frequency: MS
+  payload_type: binary
+  description: >-
+    Probability that U.S. manufacturing will be under stress three months
+    ahead. A resolved month is stressed when IPMAN has declined by at least
+    2 percent over its preceding three months.
+
+start: "2018-01-01"
+end: "2024-12-01"
+stride: 3
+warmup: 60
diff --git a/implementations/manufacturing_stress_forecasting/targets.py b/implementations/manufacturing_stress_forecasting/targets.py
new file mode 100644
index 000000000..d2db91aa7
--- /dev/null
+++ b/implementations/manufacturing_stress_forecasting/targets.py
@@ -0,0 +1,42 @@
+"""Deterministic manufacturing-stress target construction."""
+
+from __future__ import annotations
+
+import pandas as pd
+from aieng.forecasting.data.features import canonical_three_col
+
+
+DEFAULT_LOOKBACK_MONTHS = 3
+DEFAULT_STRESS_THRESHOLD_PCT = -2.0
+
+
+def derive_manufacturing_stress_labels(
+    ipman: pd.DataFrame,
+    *,
+    lookback_months: int = DEFAULT_LOOKBACK_MONTHS,
+    threshold_pct: float = DEFAULT_STRESS_THRESHOLD_PCT,
+) -> pd.DataFrame:
+    """Create a monthly 0/1 target from trailing IPMAN deterioration.
+
+    A month is labelled stressed when IPMAN has fallen by at least
+    ``abs(threshold_pct)`` percent over the preceding ``lookback_months``.
+    The label inherits the current IPMAN observation's ``released_at`` date,
+    because it cannot be known before that observation is published.
+    """
+    if lookback_months < 1:
+        raise ValueError(f"lookback_months must be positive; got {lookback_months}")
+    if threshold_pct >= 0:
+        raise ValueError(f"threshold_pct must be negative; got {threshold_pct}")
+
+    out = ipman.copy().sort_values("timestamp").reset_index(drop=True)
+    deterioration = out["value"].pct_change(periods=lookback_months, fill_method=None) * 100.0
+    out["value"] = (deterioration <= threshold_pct).astype(float)
+    out.loc[deterioration.isna(), "value"] = float("nan")
+    return canonical_three_col(out)
+
+
+__all__ = [
+    "DEFAULT_LOOKBACK_MONTHS",
+    "DEFAULT_STRESS_THRESHOLD_PCT",
+    "derive_manufacturing_stress_labels",
+]
diff --git a/implementations/pyproject.toml b/implementations/pyproject.toml
index 7467a9c70..0b14070c1 100644
--- a/implementations/pyproject.toml
+++ b/implementations/pyproject.toml
@@ -10,6 +10,7 @@ requires-python = ">=3.12"
 dependencies = [
     "aieng-forecasting[numerical,llm,agentic,documents]",
     "beautifulsoup4>=4.12,<5",  # BoC press-release HTML extraction (boc_rate_decisions.press_releases)
+    "xgboost>=2.1,<4",
 ]
 
 [tool.setuptools.packages.find]
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diff --git a/implementations/tests/manufacturing_stress_forecasting/test_agent_backtest.py b/implementations/tests/manufacturing_stress_forecasting/test_agent_backtest.py
new file mode 100644
index 000000000..5792afcb0
--- /dev/null
+++ b/implementations/tests/manufacturing_stress_forecasting/test_agent_backtest.py
@@ -0,0 +1,122 @@
+"""Tests for manufacturing-stress backtest caching and comparison guards."""
+
+from datetime import datetime
+from pathlib import Path
+
+import pytest
+from aieng.forecasting.data import DataService
+from aieng.forecasting.evaluation import BacktestSpec, ForecastingTask
+from aieng.forecasting.evaluation.backtest import BacktestResult
+from aieng.forecasting.evaluation.prediction import BinaryForecast, Prediction
+from aieng.forecasting.methods import HistoricalFrequencyPredictor
+from manufacturing_stress_forecasting import run_agent_backtest
+
+
+def _spec(*, stride: int = 3) -> BacktestSpec:
+    return BacktestSpec(
+        task=ForecastingTask(
+            task_id="manufacturing_stress_3m",
+            target_series_id="manufacturing_stress",
+            horizons=[3],
+            frequency="MS",
+            payload_type="binary",
+            description="Manufacturing stress three months ahead",
+        ),
+        start=datetime(2020, 1, 1),
+        end=datetime(2020, 7, 1),
+        stride=stride,
+    )
+
+
+def _result(
+    spec: BacktestSpec,
+    *,
+    predictor_id: str = "historical_frequency",
+    as_of: datetime = datetime(2020, 1, 1),
+    skipped_origins: int = 0,
+) -> BacktestResult:
+    prediction = Prediction(
+        predictor_id=predictor_id,
+        task_id=spec.task.task_id,
+        issued_at=datetime(2026, 1, 1),
+        as_of=as_of,
+        forecast_date=datetime(as_of.year, as_of.month + 3, 1),
+        payload=BinaryForecast(probability=0.1),
+    )
+    return BacktestResult(
+        spec=spec,
+        predictor_id=predictor_id,
+        predictions=[prediction],
+        scores=[0.01],
+        metric="brier",
+        mean_score=0.01,
+        ran_at=datetime(2026, 1, 1),
+        skipped_origins=skipped_origins,
+    )
+
+
+def test_cache_id_changes_with_backtest_spec() -> None:
+    assert run_agent_backtest.backtest_cache_id(_spec(stride=1)) != run_agent_backtest.backtest_cache_id(
+        _spec(stride=3)
+    )
+
+
+def test_incomplete_result_is_not_cached(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    spec = _spec()
+    incomplete = _result(spec, skipped_origins=1)
+    saved: list[BacktestResult] = []
+
+    monkeypatch.setattr(run_agent_backtest, "load_backtest_result", lambda *args, **kwargs: None)
+    monkeypatch.setattr(run_agent_backtest, "backtest", lambda **kwargs: incomplete)
+    monkeypatch.setattr(
+        run_agent_backtest,
+        "save_backtest_result",
+        lambda result, **kwargs: saved.append(result) or tmp_path / "unexpected.yaml",
+    )
+
+    result = run_agent_backtest.run_or_load_backtest(
+        HistoricalFrequencyPredictor(),
+        spec,
+        DataService(),
+        force_refresh=False,
+        store_dir=tmp_path,
+    )
+
+    assert result is incomplete
+    assert saved == []
+
+
+def test_comparison_rejects_different_scored_origins() -> None:
+    spec = _spec()
+    first = _result(spec, predictor_id="first", as_of=datetime(2020, 1, 1))
+    second = _result(spec, predictor_id="second", as_of=datetime(2020, 2, 1))
+
+    with pytest.raises(ValueError, match="scored origins differ"):
+        run_agent_backtest.validate_comparable_results([first, second])
+
+
+def test_incompatible_cached_spec_is_recomputed(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    requested_spec = _spec(stride=1)
+    stale = _result(_spec(stride=3))
+    fresh = _result(requested_spec)
+    saved: list[BacktestResult] = []
+
+    monkeypatch.setattr(run_agent_backtest, "load_backtest_result", lambda *args, **kwargs: stale)
+    monkeypatch.setattr(run_agent_backtest, "backtest", lambda **kwargs: fresh)
+    monkeypatch.setattr(
+        run_agent_backtest,
+        "save_backtest_result",
+        lambda result, **kwargs: saved.append(result) or tmp_path / "fresh.yaml",
+    )
+
+    result = run_agent_backtest.run_or_load_backtest(
+        HistoricalFrequencyPredictor(),
+        requested_spec,
+        DataService(),
+        force_refresh=False,
+        spec_id="deliberately-shared-id",
+        store_dir=tmp_path,
+    )
+
+    assert result is fresh
+    assert saved == [fresh]
diff --git a/implementations/tests/manufacturing_stress_forecasting/test_agent_prompt.py b/implementations/tests/manufacturing_stress_forecasting/test_agent_prompt.py
new file mode 100644
index 000000000..91c6fb662
--- /dev/null
+++ b/implementations/tests/manufacturing_stress_forecasting/test_agent_prompt.py
@@ -0,0 +1,160 @@
+"""Tests for the manufacturing-stress agent payload."""
+
+import json
+from datetime import datetime
+
+import pandas as pd
+from aieng.forecasting.data import DataService, SeriesMetadata
+from aieng.forecasting.data.features import StaticFrameAdapter
+from aieng.forecasting.evaluation import ForecastingTask
+from manufacturing_stress_forecasting.analyst_agent import (
+    ManufacturingStressPromptBuilder,
+    build_manufacturing_stress_agent_config,
+)
+from manufacturing_stress_forecasting.analyst_agent.json_runner import normalize_json_object
+from manufacturing_stress_forecasting.data import IPMAN_SERIES_ID, STRESS_SERIES_ID
+from manufacturing_stress_forecasting.features import FEATURE_SERIES_IDS
+
+
+def _frame(values: list[float], *, future_value: float | None = None) -> pd.DataFrame:
+    dates = pd.date_range("2020-01-01", periods=len(values), freq="MS")
+    frame = pd.DataFrame({"timestamp": dates, "value": values, "released_at": dates})
+    if future_value is not None:
+        frame = pd.concat(
+            [
+                frame,
+                pd.DataFrame(
+                    {
+                        "timestamp": [pd.Timestamp("2020-07-01")],
+                        "value": [future_value],
+                        "released_at": [pd.Timestamp("2020-08-01")],
+                    }
+                ),
+            ],
+            ignore_index=True,
+        )
+    return frame
+
+
+def test_prompt_uses_only_cutoff_visible_evidence() -> None:
+    service = DataService()
+    metadata = lambda series_id: SeriesMetadata(  # noqa: E731
+        series_id=series_id,
+        description=series_id,
+        source="test",
+        units="test",
+        frequency="MS",
+    )
+    service.register(
+        IPMAN_SERIES_ID,
+        StaticFrameAdapter(_frame([100, 101, 102, 103, 104, 105], future_value=999)),
+        metadata(IPMAN_SERIES_ID),
+    )
+    service.register(
+        STRESS_SERIES_ID, StaticFrameAdapter(_frame([0, 0, 1, 0, 0, 0], future_value=1)), metadata(STRESS_SERIES_ID)
+    )
+    for index, series_id in enumerate(FEATURE_SERIES_IDS):
+        service.register(
+            series_id, StaticFrameAdapter(_frame([float(index)] * 6, future_value=999)), metadata(series_id)
+        )
+
+    task = ForecastingTask(
+        task_id="manufacturing_stress_3m",
+        target_series_id=STRESS_SERIES_ID,
+        horizons=[3],
+        frequency="MS",
+        payload_type="binary",
+        description="Will manufacturing be stressed three months ahead?",
+    )
+    prompt = ManufacturingStressPromptBuilder()(task=task, context=service.context(datetime(2020, 6, 1)))
+    payload = json.loads(prompt)
+
+    assert payload["as_of"] == "2020-06-01"
+    assert payload["forecast_date"] == "2020-09-01"
+    assert payload["recent_ipman"][-1]["value"] == 105.0
+    assert len(payload["recent_ipman"]) <= 12
+    assert len(payload["current_ipman_signals_pct"]) == 3
+    assert len(payload["current_macro_signals"]) == 12
+    assert 999.0 not in payload["current_ipman_signals_pct"].values()
+    assert 999.0 not in payload["current_macro_signals"].values()
+    assert "\n" not in prompt
+
+
+def test_agent_config_limits_output_tokens() -> None:
+    config = build_manufacturing_stress_agent_config()
+
+    assert config.max_output_tokens == 384
+    assert config.temperature == 0.0
+    assert config.seed == 42
+
+
+def test_python_dict_agent_output_is_normalized_to_json() -> None:
+    raw_output = (
+        "{'probability': 0.08, 'reasoning': 'Signals are mixed.', "
+        "'direction_bias': 'neutral', 'key_signals': ['IPMAN'], 'confidence': 'medium'}"
+    )
+
+    normalized = normalize_json_object(raw_output)
+
+    assert json.loads(normalized) == {
+        "probability": 0.08,
+        "reasoning": "Signals are mixed.",
+        "direction_bias": "neutral",
+        "key_signals": ["IPMAN"],
+        "confidence": "medium",
+    }
+
+
+def test_nonliteral_agent_output_is_not_executed_or_rewritten() -> None:
+    raw_output = "{'probability': __import__('os').system('false')}"
+
+    assert normalize_json_object(raw_output) == raw_output
+
+
+def test_backtest_prompt_anonymizes_calendar_dates() -> None:
+    service = DataService()
+    metadata = lambda series_id: SeriesMetadata(  # noqa: E731
+        series_id=series_id,
+        description=series_id,
+        source="test",
+        units="test",
+        frequency="MS",
+    )
+    service.register(
+        IPMAN_SERIES_ID,
+        StaticFrameAdapter(_frame([100, 101, 102, 103, 104, 105])),
+        metadata(IPMAN_SERIES_ID),
+    )
+    service.register(
+        STRESS_SERIES_ID,
+        StaticFrameAdapter(_frame([0, 0, 1, 0, 0, 0])),
+        metadata(STRESS_SERIES_ID),
+    )
+    for index, series_id in enumerate(FEATURE_SERIES_IDS):
+        service.register(
+            series_id,
+            StaticFrameAdapter(_frame([float(index)] * 6)),
+            metadata(series_id),
+        )
+
+    task = ForecastingTask(
+        task_id="manufacturing_stress_3m",
+        target_series_id=STRESS_SERIES_ID,
+        horizons=[3],
+        frequency="MS",
+        payload_type="binary",
+        description="Will manufacturing be stressed three months ahead?",
+    )
+    prompt = ManufacturingStressPromptBuilder(anonymize_dates=True)(
+        task=task,
+        context=service.context(datetime(2020, 6, 1)),
+    )
+    payload = json.loads(prompt)
+
+    assert "as_of" not in payload
+    assert "forecast_date" not in payload
+    assert payload["timing"] == {"calendar_dates_anonymized": True}
+    assert payload["recent_ipman"][-1] == {"months_before_origin": 0, "value": 105.0}
+    assert "reference_month" not in payload["recent_ipman"][-1]
+    assert "released_at" not in payload["recent_ipman"][-1]
+    assert "2020-" not in prompt
diff --git a/implementations/tests/manufacturing_stress_forecasting/test_parameter_smoke.py b/implementations/tests/manufacturing_stress_forecasting/test_parameter_smoke.py
new file mode 100644
index 000000000..acd21b0cb
--- /dev/null
+++ b/implementations/tests/manufacturing_stress_forecasting/test_parameter_smoke.py
@@ -0,0 +1,265 @@
+"""Tests for the controlled manufacturing-stress parameter sweep."""
+
+from datetime import datetime
+
+import pandas as pd
+import pytest
+from aieng.forecasting.evaluation import BacktestSpec, ForecastingTask
+from aieng.forecasting.evaluation.backtest import BacktestResult
+from aieng.forecasting.evaluation.prediction import BinaryForecast, Prediction
+from manufacturing_stress_forecasting.run_parameter_smoke import (
+    BASELINE_NAME,
+    CANDIDATES,
+    TUNING_FOLDS,
+    build_experiment_spec,
+    build_tuning_fold_specs,
+    comparison_table,
+    predictors_for_stage,
+    select_tuning_candidate,
+    tuning_summary_tables,
+)
+
+
+def _base_spec() -> BacktestSpec:
+    return BacktestSpec(
+        task=ForecastingTask(
+            task_id="manufacturing_stress_3m",
+            target_series_id="manufacturing_stress",
+            horizons=[3],
+            frequency="MS",
+            payload_type="binary",
+            description="Manufacturing stress three months ahead",
+        ),
+        start=datetime(2018, 1, 1),
+        end=datetime(2024, 12, 1),
+        stride=3,
+        warmup=60,
+    )
+
+
+def _result(predictor_id: str, score: float) -> BacktestResult:
+    prediction = Prediction(
+        predictor_id=predictor_id,
+        task_id=_base_spec().task.task_id,
+        issued_at=datetime(2026, 1, 1),
+        as_of=datetime(2020, 1, 1),
+        forecast_date=datetime(2020, 4, 1),
+        payload=BinaryForecast(probability=0.1),
+    )
+    return BacktestResult(
+        spec=_base_spec(),
+        predictor_id=predictor_id,
+        predictions=[prediction],
+        scores=[score],
+        metric="brier",
+        mean_score=score,
+        ran_at=datetime(2026, 1, 1),
+    )
+
+
+def _fold_result(
+    predictor_id: str,
+    spec: BacktestSpec,
+    forecasts: list[tuple[datetime, datetime, float, float]],
+) -> BacktestResult:
+    predictions = []
+    scores = []
+    for as_of, forecast_date, probability, outcome in forecasts:
+        predictions.append(
+            Prediction(
+                predictor_id=predictor_id,
+                task_id=spec.task.task_id,
+                issued_at=datetime(2026, 1, 1),
+                as_of=as_of,
+                forecast_date=forecast_date,
+                payload=BinaryForecast(probability=probability),
+            )
+        )
+        scores.append((probability - outcome) ** 2)
+    return BacktestResult(
+        spec=spec,
+        predictor_id=predictor_id,
+        predictions=predictions,
+        scores=scores,
+        metric="brier",
+        mean_score=sum(scores) / len(scores),
+        ran_at=datetime(2026, 1, 1),
+    )
+
+
+class _TargetService:
+    def __init__(self, target: pd.DataFrame) -> None:
+        self._target = target
+
+    def get_series(self, series_id: str, *, as_of: datetime) -> pd.DataFrame:
+        assert series_id == "manufacturing_stress"
+        return self._target
+
+
+def test_stage_windows_preserve_the_forecasting_task() -> None:
+    base = _base_spec()
+
+    tuning = build_experiment_spec(base, stage="tune", stride=1)
+    confirmation = build_experiment_spec(base, stage="confirm", stride=3)
+
+    assert tuning.start == datetime(2000, 1, 1)
+    assert tuning.end == datetime(2017, 12, 1)
+    assert tuning.stride == 1
+    assert confirmation.start == datetime(2018, 1, 1)
+    assert confirmation.end == datetime(2024, 12, 1)
+    assert confirmation.stride == 3
+    assert tuning.task == base.task
+    assert confirmation.task == base.task
+
+
+def test_tuning_stage_builds_unique_named_candidates_after_baseline() -> None:
+    names = [name for name, _predictor in predictors_for_stage("tune")]
+
+    assert names[0] == BASELINE_NAME
+    assert len(names) == len(CANDIDATES) + 1
+    assert len(names) == len(set(names))
+
+
+def test_confirmation_stage_builds_only_the_selected_candidate() -> None:
+    selected = CANDIDATES[0].name
+
+    names = [name for name, _predictor in predictors_for_stage("confirm", selected)]
+
+    assert names == [BASELINE_NAME, selected]
+
+
+def test_confirmation_stage_requires_a_candidate() -> None:
+    with pytest.raises(ValueError, match="candidate_name is required"):
+        predictors_for_stage("confirm")
+
+
+def test_comparison_table_reports_skill_against_historical_frequency() -> None:
+    table = comparison_table(
+        [
+            (BASELINE_NAME, _result(BASELINE_NAME, 0.04)),
+            ("candidate", _result("candidate", 0.03)),
+        ]
+    )
+
+    candidate = table.set_index("candidate").loc["candidate"]
+    assert candidate["delta_vs_baseline"] == pytest.approx(-0.01)
+    assert candidate["brier_skill"] == pytest.approx(0.25)
+
+
+def test_tuning_fold_specs_cover_tuning_window_without_changing_task() -> None:
+    base = _base_spec()
+    folds = build_tuning_fold_specs(base, stride=3)
+
+    assert list(folds) == [name for name, _start, _end in TUNING_FOLDS]
+    assert [(spec.start, spec.end) for spec in folds.values()] == [(start, end) for _name, start, end in TUNING_FOLDS]
+    assert all(spec.task == base.task and spec.stride == 3 and spec.warmup == base.warmup for spec in folds.values())
+    assert [origin for spec in folds.values() for origin in spec.origins()] == build_experiment_spec(
+        base, stage="tune", stride=3
+    ).origins()
+
+
+def test_tuning_summary_reports_fold_events_and_rejects_unstable_winner() -> None:
+    specs = build_tuning_fold_specs(_base_spec(), stride=3)
+    first_forecasts = [
+        (datetime(2000, 1, 1), datetime(2000, 4, 1), 0.8, 1.0),
+        (datetime(2000, 4, 1), datetime(2000, 7, 1), 0.2, 0.0),
+    ]
+    second_forecasts = [
+        (datetime(2006, 1, 1), datetime(2006, 4, 1), 0.2, 0.0),
+        (datetime(2006, 4, 1), datetime(2006, 7, 1), 0.2, 0.0),
+    ]
+    first_candidate = [
+        (origin, forecast, 0.9 if outcome else 0.4, outcome)
+        for origin, forecast, _probability, outcome in first_forecasts
+    ]
+    second_candidate = [
+        (origin, forecast, 0.1, outcome) for origin, forecast, _probability, outcome in second_forecasts
+    ]
+    fold_results = {
+        "2000-2005": [
+            (BASELINE_NAME, _fold_result(BASELINE_NAME, specs["2000-2005"], first_forecasts)),
+            ("candidate", _fold_result("candidate", specs["2000-2005"], first_candidate)),
+        ],
+        "2006-2011": [
+            (BASELINE_NAME, _fold_result(BASELINE_NAME, specs["2006-2011"], second_forecasts)),
+            ("candidate", _fold_result("candidate", specs["2006-2011"], second_candidate)),
+        ],
+    }
+    target = pd.DataFrame(
+        {
+            "timestamp": [datetime(2000, 4, 1), datetime(2000, 7, 1), datetime(2006, 4, 1), datetime(2006, 7, 1)],
+            "value": [1.0, 0.0, 0.0, 0.0],
+        }
+    )
+
+    fold_table, pooled_table, origin_table = tuning_summary_tables(fold_results, service=_TargetService(target))
+
+    candidate_folds = fold_table[fold_table["candidate"] == "candidate"].set_index("fold")
+    assert candidate_folds.loc["2000-2005", "stress_events"] == 1
+    assert candidate_folds.loc["2006-2011", "stress_events"] == 0
+    candidate_pooled = pooled_table.set_index("candidate").loc["candidate"]
+    assert candidate_pooled["folds_beating_baseline"] == 1
+    assert candidate_pooled["event_folds"] == 1
+    assert candidate_pooled["event_folds_beating_baseline"] == 0
+    assert candidate_pooled["delta_vs_baseline"] > 0
+    assert select_tuning_candidate(pooled_table) is None
+    assert len(origin_table) == 8
+
+
+def test_tuning_summary_rejects_different_scored_origins() -> None:
+    spec = build_tuning_fold_specs(_base_spec(), stride=3)["2000-2005"]
+    baseline = [
+        (datetime(2000, 1, 1), datetime(2000, 4, 1), 0.8, 1.0),
+        (datetime(2000, 4, 1), datetime(2000, 7, 1), 0.2, 0.0),
+    ]
+    candidate = baseline[:1]
+    fold_results = {
+        "2000-2005": [
+            (BASELINE_NAME, _fold_result(BASELINE_NAME, spec, baseline)),
+            ("candidate", _fold_result("candidate", spec, candidate)),
+        ]
+    }
+    target = pd.DataFrame({"timestamp": [datetime(2000, 4, 1), datetime(2000, 7, 1)], "value": [1.0, 0.0]})
+
+    with pytest.raises(ValueError, match="scored origins differ"):
+        tuning_summary_tables(fold_results, service=_TargetService(target))
+
+
+def test_select_tuning_candidate_requires_pooled_and_fold_skill() -> None:
+    table = pd.DataFrame(
+        [
+            {
+                "candidate": BASELINE_NAME,
+                "mean_brier": 0.04,
+                "delta_vs_baseline": 0.0,
+                "folds_beating_baseline": 0,
+                "event_folds_beating_baseline": 0,
+            },
+            {
+                "candidate": "stable",
+                "mean_brier": 0.03,
+                "delta_vs_baseline": -0.01,
+                "folds_beating_baseline": 2,
+                "event_folds_beating_baseline": 1,
+            },
+            {
+                "candidate": "unstable",
+                "mean_brier": 0.02,
+                "delta_vs_baseline": -0.02,
+                "folds_beating_baseline": 1,
+                "event_folds_beating_baseline": 1,
+            },
+            {
+                "candidate": "eventless_only",
+                "mean_brier": 0.025,
+                "delta_vs_baseline": -0.015,
+                "folds_beating_baseline": 2,
+                "event_folds_beating_baseline": 0,
+            },
+        ]
+    )
+
+    winner = select_tuning_candidate(table)
+
+    assert winner is not None
+    assert winner["candidate"] == "stable"
diff --git a/implementations/tests/manufacturing_stress_forecasting/test_targets_and_features.py b/implementations/tests/manufacturing_stress_forecasting/test_targets_and_features.py
new file mode 100644
index 000000000..1b1d38428
--- /dev/null
+++ b/implementations/tests/manufacturing_stress_forecasting/test_targets_and_features.py
@@ -0,0 +1,107 @@
+"""Focused tests for label construction and historical feature cutoffs."""
+
+import pandas as pd
+import pytest
+from manufacturing_stress_forecasting.features import (
+    CPI_YOY_SERIES_ID,
+    FEATURE_SERIES_IDS,
+    FED_FUNDS_SERIES_ID,
+    SPY_RETURN_3M_SERIES_ID,
+    SPY_RETURN_12M_SERIES_ID,
+    XLI_RETURN_3M_SERIES_ID,
+    XLI_RETURN_12M_SERIES_ID,
+    YIELD_CURVE_SERIES_ID,
+    build_feature_snapshot,
+    build_macro_feature_frames,
+)
+from manufacturing_stress_forecasting.targets import derive_manufacturing_stress_labels
+
+
+def test_stress_label_uses_trailing_three_month_decline() -> None:
+    dates = pd.date_range("2024-01-01", periods=5, freq="MS")
+    ipman = pd.DataFrame(
+        {
+            "timestamp": dates,
+            "value": [100.0, 100.0, 100.0, 100.0, 97.9],
+            "released_at": dates + pd.offsets.MonthBegin(1),
+        }
+    )
+
+    labels = derive_manufacturing_stress_labels(ipman)
+
+    assert labels["value"].tolist() == [0.0, 1.0]
+    assert labels["timestamp"].tolist() == [pd.Timestamp("2024-04-01"), pd.Timestamp("2024-05-01")]
+    assert labels["released_at"].tolist() == [pd.Timestamp("2024-05-01"), pd.Timestamp("2024-06-01")]
+
+
+def test_feature_snapshot_ignores_values_released_after_origin() -> None:
+    origin = pd.Timestamp("2024-03-01")
+    frames: dict[str, pd.DataFrame] = {}
+    for index, series_id in enumerate(FEATURE_SERIES_IDS):
+        frames[series_id] = pd.DataFrame(
+            {
+                "timestamp": [pd.Timestamp("2024-01-01"), pd.Timestamp("2024-02-01")],
+                "value": [float(index), 999.0],
+                "released_at": [pd.Timestamp("2024-02-01"), pd.Timestamp("2024-04-01")],
+            }
+        )
+
+    snapshot = build_feature_snapshot(origin, frames)
+
+    assert snapshot is not None
+    for index, series_id in enumerate(FEATURE_SERIES_IDS):
+        assert snapshot[series_id] == pytest.approx(float(index))
+
+
+def test_macro_features_use_month_end_values_with_next_business_day_release() -> None:
+    timestamps = pd.to_datetime(["2024-01-30", "2024-01-31", "2024-02-28", "2024-02-29"])
+
+    def frame(values: list[float]) -> pd.DataFrame:
+        return pd.DataFrame(
+            {
+                "timestamp": timestamps,
+                "value": values,
+                "released_at": timestamps,
+            }
+        )
+
+    features = build_macro_feature_frames(
+        frame([5.30, 5.31, 5.32, 5.33]),
+        frame([4.00, 4.10, 4.20, 4.30]),
+        frame([4.40, 4.50, 4.55, 4.60]),
+    )
+
+    fed = features[FED_FUNDS_SERIES_ID]
+    spread = features[YIELD_CURVE_SERIES_ID]
+
+    assert fed["timestamp"].tolist() == [pd.Timestamp("2024-01-01"), pd.Timestamp("2024-02-01")]
+    assert fed["value"].tolist() == pytest.approx([5.31, 5.33])
+    assert fed["released_at"].tolist() == [pd.Timestamp("2024-02-01"), pd.Timestamp("2024-03-01")]
+    assert spread["value"].tolist() == pytest.approx([-0.40, -0.30])
+    assert spread["released_at"].tolist() == [pd.Timestamp("2024-02-01"), pd.Timestamp("2024-03-01")]
+
+
+def test_expanded_macro_features_include_cpi_yoy_and_monthly_market_return() -> None:
+    timestamps = pd.date_range("2023-01-01", periods=13, freq="MS")
+
+    def frame(values: list[float]) -> pd.DataFrame:
+        return pd.DataFrame({"timestamp": timestamps, "value": values, "released_at": timestamps})
+
+    features = build_macro_feature_frames(
+        frame([5.0] * 13),
+        frame([4.0] * 13),
+        frame([3.0] * 13),
+        frame([100.0] + [100.0] * 11 + [110.0]),
+        frame([4.0] * 13),
+        frame([200.0] * 13),
+        frame([15.0] * 13),
+        frame([300.0] * 13),
+        frame([100.0 + 5.0 * index for index in range(13)]),
+        frame([100.0] * 13),
+    )
+
+    assert features[CPI_YOY_SERIES_ID]["value"].iloc[-1] == pytest.approx(10.0)
+    assert features[SPY_RETURN_3M_SERIES_ID]["value"].iloc[-1] == pytest.approx(100.0 * 15.0 / 145.0)
+    assert features[SPY_RETURN_12M_SERIES_ID]["value"].iloc[-1] == pytest.approx(60.0)
+    assert features[XLI_RETURN_3M_SERIES_ID]["value"].iloc[-1] == pytest.approx(0.0)
+    assert features[XLI_RETURN_12M_SERIES_ID]["value"].iloc[-1] == pytest.approx(0.0)
diff --git a/planning-docs/manufacturing-adaptive-agent-tracing-plan.md b/planning-docs/manufacturing-adaptive-agent-tracing-plan.md
new file mode 100644
index 000000000..266f84c79
--- /dev/null
+++ b/planning-docs/manufacturing-adaptive-agent-tracing-plan.md
@@ -0,0 +1,180 @@
+# Manufacturing Adaptive Agent Tracing Plan
+
+## Decision
+
+Add observability to the manufacturing adaptive agent in two complementary
+layers:
+
+1. Use the repository's existing Langfuse and Google ADK instrumentation as the
+   canonical execution trace.
+2. Persist a small, redacted local audit of adaptive strategy mutations and
+   state transitions, because durable strategy evolution should not depend only
+   on remote trace retention.
+
+This plan is documentation-only until the manufacturing adaptive-agent surface
+has been confirmed. The first implementation step must locate and verify its
+prediction, resolution, self-review, strategy-state, and mutation-tool paths.
+
+## Existing Foundation
+
+Langfuse is already supported by the shared library:
+
+- `aieng.forecasting.langfuse_tracing.init_langfuse_tracing()` initializes the
+  Langfuse client and Google ADK OpenTelemetry instrumentation when
+  `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` are present.
+- `AdkTextRunner` can propagate user/session IDs, trace names, tags, metadata,
+  and agent versions for each turn.
+- `langfuse_generation()` nests inner LiteLLM calls that ADK does not observe
+  automatically.
+- `stamp_forecast_on_trace()` attaches structured forecasts to a trace.
+- Trace evaluation helpers can fetch traces and push scores back to Langfuse.
+- Energy/Oil and BoC provide reference implementations for traced agent runs,
+  rationale capture, and trace-based evaluation.
+
+The Energy/Oil adaptive agent also provides the state-management pattern to
+reuse: typed mutation tools, `AdaptiveSkillState`, `AdaptiveSkillStore`, YAML
+state, rendered strategy markdown, and reversible `.history/` backups.
+
+## Observability Boundary
+
+The trace should expose observable execution, not hidden chain-of-thought. It
+may record:
+
+- invocation type and task metadata;
+- tool calls and sanitized inputs/outputs;
+- skills or strategy versions loaded;
+- feature or evidence summaries used by the agent;
+- final forecast probabilities and forecast dates;
+- concise model-provided rationale;
+- strategy mutations and evaluation scores.
+
+It should not commit or expose credentials, full prompts by default, raw search
+results, large market-data payloads, or claims that a rationale is a complete
+record of internal reasoning.
+
+## Phases
+
+### Phase 1: Confirm the manufacturing surface
+
+1. Inventory the manufacturing adaptive-agent directory, runner, predictor,
+   strategy state, mutation tools, notebooks, reports, and tests.
+2. Identify the boundaries for prediction, resolution, self-review, and direct
+   user-question messages.
+3. Confirm whether the agent already uses `AdaptiveSkillState`,
+   `AdaptiveSkillStore`, typed mutation tools, and `AdkTextRunner`.
+4. Define a stable trace identity containing the run ID, invocation type,
+   forecast origin, task ID, strategy variant/version, and agent revision.
+
+### Phase 2: Reuse Langfuse
+
+1. Add explicit, opt-in Langfuse configuration to the manufacturing runner or
+   predictor. Preserve no-op behavior when credentials or the optional package
+   are absent.
+2. Propagate bounded ASCII metadata and tags such as:
+   `manufacturing-stress`, `adaptive-agent`, `prediction`, `resolution`, and
+   `self-review`.
+3. Stamp each structured forecast onto its trace with probability, forecast
+   date, rationale, key signals, and strategy version.
+4. Preserve the trace ID or URL in prediction metadata and print or flush it in
+   short-lived scripts and notebooks.
+
+### Phase 3: Audit adaptation
+
+1. Instrument the typed mutation boundary for observations, hypotheses,
+   hypothesis outcomes, calibration promotion, and approach updates.
+2. Write one sanitized JSONL event per mutation containing UTC timestamp, run
+   ID, invocation type, trace ID, tool name, strategy path/version, result, and
+   state-before/state-after SHA-256 hashes.
+3. Keep `skill_state.yaml` and `AdaptiveSkillStore` `.history/` backups as the
+   authoritative reversible state history.
+4. Use Langfuse links in local events rather than duplicating full transcripts.
+5. Prefer runtime-only or gitignored raw traces. Commit only redacted,
+   curated summaries unless the repository explicitly decides otherwise.
+
+### Phase 4: Produce insight reports
+
+Build a report utility or notebook that can reconstruct:
+
+- adaptation timeline;
+- hypothesis lifecycle and confirmation/refutation counts;
+- calibration-correction lineage;
+- strategy versions over time;
+- forecast rationale by strategy version, regime, and horizon;
+- before/after performance by strategy version;
+- tool and skill usage counts;
+- links to the corresponding Langfuse traces.
+
+The reports must distinguish observed events and state transitions from
+model-generated explanations.
+
+All plots produced by this phase must be executive-presentation level. They
+should use consistent titles, subtitles or annotations where needed, readable
+axis labels and legends, restrained color choices, clear event markers, and
+accessible contrast. Charts must remain legible when exported for a slide or
+briefing document, avoid exposing raw trace payloads or internal identifiers,
+and include the reporting period, units, sample size, and relevant strategy
+version or forecast horizon whenever those details affect interpretation.
+Exported figures should have deterministic filenames, publication-quality
+resolution, and layout checked at the intended presentation size.
+
+### Phase 5: Tests and documentation
+
+1. Test tracing-disabled and credentials-absent behavior.
+2. Test Langfuse metadata propagation and forecast stamping with fakes; do not
+   require a live Langfuse service in unit tests.
+3. Test mutation event schemas, redaction, state-hash changes, and one-event-
+   per-mutation behavior.
+4. Run a deterministic local smoke path with tracing disabled.
+5. Run an opt-in traced smoke path only when credentials and the required
+   extras are available.
+6. Update the manufacturing README and agent guides with setup, trace naming,
+   viewing workflow, artifact locations, and limitations.
+
+## Recommended Data Contract
+
+Every trace or local event should use a stable subset of:
+
+```json
+{
+  "run_id": "...",
+  "trace_id": "...",
+  "timestamp": "2026-10-04T12:00:00Z",
+  "invocation_type": "prediction",
+  "task_id": "manufacturing_stress_forecast",
+  "as_of": "2026-10-04",
+  "strategy_version": "...",
+  "event": "forecast_or_strategy_mutation",
+  "tool": "...",
+  "result": "...",
+  "state_before_sha256": "...",
+  "state_after_sha256": "..."
+}
+```
+
+The exact schema should be finalized after Phase 1, once the manufacturing
+agent's actual state and tool contracts are known.
+
+## Verification Criteria
+
+- With tracing disabled, forecasts and adaptation behavior are unchanged.
+- With tracing enabled, each intended invocation produces one identifiable
+  trace with stable tags and metadata.
+- Each trace contains the structured forecast observation.
+- Each strategy mutation has one sanitized local audit event and a matching
+  state-history backup.
+- Reports can connect a forecast to its strategy version, rationale, mutation
+  history, trace ID, and eventual score.
+- Every generated plot meets the executive-presentation standard: readable at
+   slide size, appropriately annotated, accessible, consistently styled, and
+   exported at presentation quality.
+- No protected evaluation period, model parameter, or adaptation policy is
+  changed merely to add observability.
+
+## Review Outcome
+
+This plan is approved for implementation planning. The important sequencing
+constraint is to verify the manufacturing adaptive-agent implementation first.
+If it does not yet have typed persistent strategy state and mutation tools, that
+state-management layer should be established or aligned with the Energy/Oil
+pattern before adding tracing. Otherwise, tracing would capture model calls but
+not the durable adaptation that the user wants to inspect.
\ No newline at end of file
diff --git a/planning-docs/manufacturing-stress-adaptive-agent-plan.md b/planning-docs/manufacturing-stress-adaptive-agent-plan.md
new file mode 100644
index 000000000..8ca1f72b0
--- /dev/null
+++ b/planning-docs/manufacturing-stress-adaptive-agent-plan.md
@@ -0,0 +1,120 @@
+# Manufacturing Stress Adaptive Agent Plan
+
+## Review verdict
+
+This remains a good next agentic improvement. It should be developed after the deterministic modeling and regime-diagnostic work, while its project-wide evaluation is tracked and implemented separately.
+
+The manufacturing problem is a good fit for a bounded adaptive analyst because the existing numerical predictors already provide a strong, inspectable anchor. The agent can add structured interpretation and a limited adjustment while preserving a clear numerical baseline. A first version should avoid unrestricted web search: macro releases, revisions, and cutoff handling create more leakage and reproducibility risk than value at this stage.
+
+## Goals
+
+- Provide an auditable analyst layer over the existing logistic, XGBoost, and hybrid predictions.
+- Let the analyst learn reusable calibration observations from resolved forecasts without changing the underlying models.
+- Add the adaptive analyst as an explicitly named predictor in `manufacturing_stress_workbench_2` and compare it with the fixed deterministic roster.
+- Keep every forecast cutoff-aware, reproducible, and separable from the protected evaluation contract.
+
+## Non-goals and invariants
+
+- The agent does not modify model parameters, feature definitions, regime thresholds, or the protected `EvalSpec`.
+- The agent does not select the hybrid weight from protected evaluation results.
+- The agent does not silently learn from outcomes during a frozen protected evaluation.
+- The agent does not execute arbitrary code or use unrestricted network search in the initial version.
+- The deterministic 50/50 hybrid remains an independent anchor and is never replaced by the agent-adjusted path.
+
+## Proposed architecture
+
+### 1. Typed deterministic forecast tool
+
+Expose one implementation-local tool that returns:
+
+- Logistic, XGBoost, and 50/50 anchor probabilities.
+- The selected origin date and three-month target date.
+- Feature snapshots and the three regime labels available at the origin.
+- Model disagreement and any relevant cutoff or release-lag metadata.
+
+The tool should compute from the existing predictor and data-service paths rather than duplicate model logic. Its output is the agent's evidence packet, not a new model-training interface.
+
+### 2. Bounded analyst adjustment
+
+The analyst returns a typed result containing:
+
+- Final stress probability.
+- Direction relative to the numerical anchor.
+- A concise rationale.
+- Supporting and countervailing evidence.
+- A bounded adjustment from the anchor.
+
+The adjustment limit must be explicit and tested. The result should preserve the anchor probability, adjusted probability, and adjustment amount so later evaluation can distinguish numerical performance from analyst movement.
+
+### 3. Resolution message
+
+After the three-month outcome is resolved, provide the agent with:
+
+- Original forecast probability and numerical anchor.
+- Resolved binary label and Brier contribution.
+- Origin and target dates.
+- The original feature and regime snapshot.
+
+Resolution is an input to governed learning, not permission to rewrite the historical forecast or protected evaluation record.
+
+### 4. Governed strategy memory
+
+Use typed, scoped memory tools modeled on the existing adaptive-agent infrastructure:
+
+- `record_observation`
+- `open_hypothesis`
+- `record_hypothesis_outcome`
+- `graduate_hypothesis`
+- A bounded approach or calibration narrative, if needed after the first version
+
+A calibration rule may be promoted only after at least three confirming outcomes from distinct forecast origins. Confirmation must not come entirely from one regime or one contiguous episode. Every mutation should be auditable and versioned, and the strategy state should be snapshot-able before training and evaluation.
+
+The initial implementation should prefer observations and hypotheses over automatically changing numeric corrections. Any promoted correction must remain a bounded instruction to the analyst, not a model-parameter update.
+
+## Staged implementation
+
+### Phase 0: deterministic modeling baseline
+
+Complete the existing deterministic modeling and diagnostic work. Preserve the four fixed predictors, their common development/backtest origins, regime-conditional diagnostics, and offline hybrid-weight analysis as the baseline that the agent will join.
+
+**Gate:** deterministic results, origin alignment, and documentation are complete.
+
+### Phase 1: deterministic manufacturing forecast tool
+
+Build the typed evidence packet around existing predictors and cutoff-safe feature data. Add unit tests for target-date calculation, missing or unresolved targets, probability bounds, and preservation of the anchor values.
+
+**Gate:** the tool produces identical evidence for identical inputs and cannot mutate evaluation configuration.
+
+### Phase 2: bounded analyst agent
+
+Add the typed analyst output and bounded anchor adjustment. Register it in `manufacturing_stress_workbench_2` as a named model/predictor alongside historical frequency, logistic, XGBoost, and the deterministic hybrid anchor. Run the notebook comparison on development-period examples and compare its forecasts with the fixed roster and unadjusted anchor. Keep network access and arbitrary code execution disabled.
+
+**Gate:** malformed or out-of-bound agent outputs are rejected; the fallback behavior is deterministic; the agent path is opt-in.
+
+### Phase 3: resolution and curriculum
+
+Add resolution messages and a small, reproducible curriculum built from development-period forecast records, regime tables, and error summaries. Do not replay every historical origin as if it were a live training environment. Prefer structured case studies and explicit before/after state snapshots.
+
+**Gate:** the training material is cutoff-safe, can be regenerated from local artifacts, and does not include protected outcomes.
+
+### Phase 4: governed adaptive state
+
+Add strategy memory and mutation tools with scope guards, audit history, snapshot/restore, and the three-confirmation promotion rule. The notebook should be able to show the named agent before and after its development-period curriculum, while keeping the deterministic roster and anchor available for comparison.
+
+**Gate:** no state mutation can alter predictor parameters or already-recorded forecasts; promotion tests cover mixed and single-regime evidence.
+
+### Phase 5: optional external context
+
+Only after the local-data path is stable, consider a restricted external-context tool. It must enforce an explicit `as_of` cutoff, record retrieved evidence, and have a reproducible cached mode. This is optional and should not be required for the first manufacturing agent result.
+
+## Acceptance criteria
+
+- The typed tool is cutoff-aware and reuses the existing manufacturing service and predictors.
+- `manufacturing_stress_workbench_2` includes the adaptive analyst as a named comparison row beside the existing deterministic predictors.
+- The notebook comparison reports the agent's forecast, anchor probability, bounded adjustment, and fallback status for each comparable development origin.
+- The agent output is schema-validated, probability-bounded, and adjustment-bounded.
+- The fallback path is deterministic and does not require an LLM call.
+- Strategy mutations are typed, auditable, snapshot-able, and protected from parameter or `EvalSpec` changes.
+- Calibration promotion requires three confirming outcomes from distinct origins and is not supported by one regime alone.
+- Training artifacts are separated from any future project-wide evaluation data.
+- The agent remains opt-in until its implementation and governance gates pass.
diff --git a/planning-docs/manufacturing-stress-initial-expansion-plan.md b/planning-docs/manufacturing-stress-initial-expansion-plan.md
new file mode 100644
index 000000000..b812d583f
--- /dev/null
+++ b/planning-docs/manufacturing-stress-initial-expansion-plan.md
@@ -0,0 +1,569 @@
+# Manufacturing Stress Initial Expansion Plan
+
+## Purpose
+
+Expand the manufacturing-stress forecasting workflow from a quantitative-only analyst into a controlled hybrid forecasting system. The statistical models provide a transparent numerical anchor. The LLM receives that anchor and cutoff-safe evidence, explains the situation, and proposes an adjustment that Python bounds before using it as the hybrid forecast probability.
+
+The expansion must be auditable, reproducible, and suitable for prospective evaluation. The adjusted forecast, numerical anchor, and agent proposal must remain separately recorded so the agent's incremental value can be evaluated.
+
+## Compatibility requirement
+
+This expansion is additive. It must preserve every existing manufacturing-stress capability unless a deliberate, separately documented breaking change is approved.
+
+The following existing behavior must continue to work unchanged:
+
+- The current parameter-sweep notebook and its `tune` and `confirm` stages.
+- The full 15-feature statistical predictors.
+- The historical-frequency baseline.
+- The existing logistic and XGBoost predictor APIs and predictor IDs.
+- The current `BacktestSpec` files and historical backtest runners.
+- Existing cached prediction and backtest artifacts.
+- The quantitative-only manufacturing-stress agent.
+- `run_agent_prediction.py` and `run_agent_backtest.py`.
+- Existing smoke tests, notebooks, imports, and command-line interfaces.
+
+Implementation rules:
+
+1. Add new modules and entry points rather than replacing existing ones.
+2. Preserve public function signatures and existing defaults.
+3. Keep the current quantitative-only agent available as a separate predictor.
+4. Do not change the meaning of `manufacturing_stress`, existing specs, or existing predictor IDs.
+5. Do not invalidate existing caches. Version new hybrid artifacts separately.
+6. Keep hybrid configuration opt-in; existing workflows must not activate it automatically.
+7. If a shared helper must change, add backward-compatible behavior and regression tests before using it from the hybrid path.
+8. Do not change existing notebook outputs or execution semantics merely to support the hybrid experiment.
+
+The hybrid system should have distinct names, configuration, cache namespaces, and predictor IDs. A user must be able to run the old and new workflows side by side and compare their outputs.
+
+## Frozen initial scope
+
+### Target
+
+Use the existing target from `specs/manufacturing_stress_smoke.yaml`:
+
+- Target series: `manufacturing_stress`
+- Event: trailing three-month IPMAN percentage change is less than or equal to `-2%`
+- Target values: binary `0` or `1`
+- Forecast horizon: three months ahead
+- Frequency: monthly (`MS`)
+
+The 2018-2024 period is a historical diagnostic only. It is not an untouched confirmation set.
+
+### Features
+
+Use the full 15-variable feature list from `features.py`:
+
+IPMAN features:
+
+- `ipman_change_1m_pct`
+- `ipman_change_3m_pct`
+- `ipman_change_6m_pct`
+
+Macro and market features:
+
+- `FEDFUNDS`
+- `YC_SPREAD`
+- `CPIAUCSL`
+- `CPI_YOY`
+- `UNRATE`
+- `ICSA`
+- `VIXCLS`
+- `CREDIT_SPREAD`
+- `SPY_RETURN_3M`
+- `SPY_RETURN_12M`
+- `XLI_RETURN_3M`
+- `XLI_RETURN_12M`
+
+The feature set is fixed for this expansion. Do not silently substitute the earlier five-variable or eight-variable shortlist.
+
+### Statistical candidates
+
+Use these two fixed candidates:
+
+- `logistic_c_0_001`
+- `xgb_50_depth2_lr0_03`
+
+They use the full 15-variable feature list. The expansion does not re-select regularization or XGBoost parameters.
+
+## Target architecture
+
+```text
+cutoff-safe data context
+        |
+        +--> historical-frequency baseline
+        +--> logistic candidate
+        +--> XGBoost candidate
+                         |
+                  numerical anchor
+                         |
+             structured agent payload
+                         |
+          LLM explanation and adjustment proposal
+                         |
+              deterministic Python validation
+                         |
+      bounded agent-adjusted forecast probability
+                         |
+          persisted forecast and later outcome
+```
+
+During the initial phase:
+
+- The numerical anchor is the starting probability supplied to the agent.
+- The agent's proposal is bounded by Python to the configured adjustment range.
+- The bounded adjusted probability is the hybrid agent's forecast; the anchor, proposal, and applied adjustment remain in metadata for analysis.
+
+## Phase 0: Establish the frozen configuration
+
+Create a single configuration for the hybrid experiment. It should explicitly identify:
+
+```yaml
+target_series_id: manufacturing_stress
+target_threshold_pct: -2.0
+lookback_months: 3
+horizon_months: 3
+frequency: MS
+feature_set: full_15
+candidates:
+  - logistic_c_0_001
+  - xgb_50_depth2_lr0_03
+mode: shadow
+max_agent_adjustment: 0.03
+```
+
+The configuration should be referenced by the smoke test, historical runner, and prospective runner. Avoid duplicating target or candidate definitions in notebooks and scripts.
+
+Record versions for:
+
+- Configuration
+- Feature definition
+- Prompt
+- Agent model
+- Statistical predictor implementation
+
+## Phase 1: Build the numerical anchor
+
+### Inputs
+
+For every forecast origin, run:
+
+1. Historical frequency predictor
+2. Logistic predictor using all 15 features
+3. XGBoost predictor using all 15 features
+
+The two candidates must be scored on the same forecast origins and use the same `ForecastContext` cutoff.
+
+### Anchor definition
+
+Start with a transparent fixed average of the two selected model probabilities:
+
+```python
+anchor_probability = 0.5 * logistic_probability + 0.5 * xgboost_probability
+```
+
+Retain the historical-frequency probability for comparison, but do not include it in the first ensemble unless the experiment explicitly defines and versions a different formula.
+
+The anchor record should contain:
+
+```json
+{
+  "historical_frequency_probability": 0.06,
+  "logistic_probability": 0.08,
+  "xgboost_probability": 0.11,
+  "anchor_probability": 0.095,
+  "anchor_method": "mean_logistic_xgboost",
+  "model_version": "anchor_v1"
+}
+```
+
+Do not optimize ensemble weights against the 2018-2024 diagnostic period.
+
+### Calibration follow-up
+
+Calibration is a later step, after the first transparent anchor works. Use chronological data only, for example:
+
+```text
+model development: 2000-2013
+calibration:       2014-2017
+historical review: 2018-2024
+prospective test:  future resolved observations
+```
+
+Any calibration method must be fitted only before the period being evaluated.
+
+## Phase 2: Extend the cutoff-safe agent payload
+
+Reuse `ManufacturingStressPromptBuilder` and the existing `ForecastContext` boundary. Do not give the agent raw unrestricted repository data.
+
+The payload should contain:
+
+```json
+{
+  "task": {
+    "task_id": "manufacturing_stress_3m",
+    "horizon_months": 3
+  },
+  "as_of": "YYYY-MM-DD",
+  "forecast_date": "YYYY-MM-DD",
+  "target_definition": {
+    "event": "manufacturing stress",
+    "threshold_pct": -2.0,
+    "lookback_months": 3
+  },
+  "current_signals": {},
+  "recent_ipman": [],
+  "historical_stress": {},
+  "statistical_anchor": {
+    "historical_frequency_probability": 0.0,
+    "logistic_probability": 0.0,
+    "xgboost_probability": 0.0,
+    "anchor_probability": 0.0
+  },
+  "evidence": []
+}
+```
+
+Every observation must preserve:
+
+- `reference_month`
+- `released_at`
+- `value`
+- `source`
+- `available_at_cutoff`
+
+An observation is available only when:
+
+```python
+released_at <= as_of
+```
+
+The system must distinguish the reference month from the release date. A value from an old reference month is still unavailable if it was released after the forecast origin.
+
+## Phase 3: Define the agent output contract
+
+Add a structured output model for the hybrid analyst. The agent should return fields equivalent to:
+
+```json
+{
+  "anchor_probability": 0.095,
+  "adjustment": -0.01,
+  "proposed_probability": 0.085,
+  "direction_bias": "down",
+  "supporting_evidence": [
+    {
+      "signal": "VIXCLS",
+      "direction": "stress_increasing",
+      "reason": "..."
+    }
+  ],
+  "countervailing_evidence": [
+    {
+      "signal": "XLI_RETURN_12M",
+      "direction": "stress_decreasing",
+      "reason": "..."
+    }
+  ],
+  "uncertainties": [
+    "..."
+  ],
+  "rationale": "..."
+}
+```
+
+Prompt rules:
+
+1. Begin from the supplied anchor.
+2. Estimate `P(stress=1)`, not confidence in the explanation.
+3. Use only the supplied payload.
+4. Do not use remembered events or post-cutoff information.
+5. Do not double-count correlated IPMAN signals.
+6. Explain both supporting and countervailing evidence.
+7. Propose a small adjustment only.
+8. Identify missing, preliminary, or uncertain evidence.
+9. Keep the rationale concise and specific.
+
+The agent's declared `proposed_probability` is not authoritative. Python recalculates the validated result.
+
+## Phase 4: Deterministic output validation
+
+Implement validation outside the LLM. Suggested initial limits:
+
+```python
+MAX_AGENT_ADJUSTMENT = 0.03
+MIN_PROBABILITY = 0.01
+MAX_PROBABILITY = 0.99
+```
+
+Validation must check:
+
+- JSON is parseable.
+- Required fields are present.
+- `adjustment` is numeric and finite.
+- `direction_bias` is one of `up`, `down`, or `neutral`.
+- Anchor probability is in `[0, 1]`.
+- Evidence dates are not later than `as_of`.
+- Target and horizon match the frozen configuration.
+- The adjustment is within the permitted range.
+- The adjusted forecast probability is calculated by Python.
+
+Authoritative calculation:
+
+```python
+validated_adjustment = max(
+    -MAX_AGENT_ADJUSTMENT,
+    min(agent_adjustment, MAX_AGENT_ADJUSTMENT),
+)
+adjusted_probability = min(
+    MAX_PROBABILITY,
+    max(MIN_PROBABILITY, anchor_probability + validated_adjustment),
+)
+```
+
+If the output is malformed or violates the contract, record a validation failure rather than silently falling back to the anchor forecast.
+
+## Phase 5: Dedicated smoke test
+
+Create a small hybrid smoke test separate from the full parameter sweep. Its purpose is technical validation, not model selection.
+
+Use one or two fixed historical origins. Prefer one origin followed by a stress event and one origin not followed by a stress event, if available.
+
+The smoke test must verify:
+
+1. The data service builds successfully.
+2. The frozen target configuration loads.
+3. The full 15-feature snapshot is available.
+4. Both statistical candidates produce probabilities.
+5. The numerical anchor is calculated.
+6. The agent receives a payload containing the anchor.
+7. The agent returns parseable structured output.
+8. The output validator accepts or rejects it deterministically.
+9. The adjustment limit is enforced.
+10. No evidence has `released_at > as_of`.
+11. The forecast date is exactly three months after the origin under the task frequency.
+12. A complete forecast artifact can be written and read back.
+
+Use a cached or deterministic agent response for routine smoke testing where possible. Do not require a paid LLM call for every local test.
+
+The smoke test should print:
+
+- Origin date
+- Forecast date
+- Active target and threshold
+- Number of features
+- Logistic probability
+- XGBoost probability
+- Anchor probability
+- Agent adjustment
+- Adjusted forecast probability
+- Validation status
+- A rationale of at most 40 words
+- Main supporting and countervailing evidence
+- Overall direction (`up`, `down`, or `neutral`)
+
+## Phase 6: Historical evaluation
+
+Build a hybrid backtest runner based on `run_agent_backtest.py` and the existing shared backtest harness.
+
+Compare:
+
+- Historical frequency
+- `logistic_c_0_001`
+- `xgb_50_depth2_lr0_03`
+- Numerical anchor
+- Existing quantitative-only agent
+- Hybrid agent adjusted probability
+
+All comparisons must use identical:
+
+- Target definition
+- Feature list
+- Forecast origins
+- Forecast horizon
+- Cutoff rules
+- Resolved outcomes
+
+Keep the 2000-2017 tuning folds and 2018-2024 historical diagnostic clearly labelled. Do not call 2018-2024 a clean confirmation period.
+
+Report at least:
+
+- Mean Brier score
+- Brier score by chronological fold
+- Number of scored origins
+- Number of stress events
+- Mean predicted probability
+- Calibration by probability bucket
+- False positives
+- False negatives
+- Agent adjustment mean, median, and range
+- Number of validation failures
+- Number of forecasts where the agent moved toward or away from stress
+
+The historical backtest answers whether the shadow adjustment would have helped historically. It does not authorize activating the adjustment.
+
+## Phase 7: Structured evidence first
+
+The first evidence implementation should use repository data already available through the data service:
+
+- IPMAN momentum
+- Federal funds rate
+- Yield-curve spread
+- CPI and CPI growth
+- Unemployment
+- Initial claims
+- VIX
+- Credit spread
+- Broad-market returns
+- Industrial-sector returns
+
+Represent each evidence item as a structured record:
+
+```json
+{
+  "source": "VIXCLS",
+  "reference_date": "YYYY-MM-DD",
+  "released_at": "YYYY-MM-DD",
+  "value": 21.4,
+  "relevance": "market_stress",
+  "available_at_cutoff": true
+}
+```
+
+Only after this path is tested should official reports or bounded web retrieval be added. If web retrieval is added later, preserve the query, source, publication date, retrieval date, cutoff date, and verification status.
+
+## Phase 8: Forecast artifact storage
+
+Persist one immutable forecast record per forecast origin. Store enough information to reproduce and audit the forecast:
+
+```json
+{
+  "forecast_id": "...",
+  "created_at": "...",
+  "as_of": "...",
+  "forecast_date": "...",
+  "target_series_id": "manufacturing_stress",
+  "target_threshold_pct": -2.0,
+  "feature_set": "full_15",
+  "features": {},
+  "historical_frequency_probability": 0.0,
+  "logistic_probability": 0.0,
+  "xgboost_probability": 0.0,
+  "anchor_probability": 0.0,
+  "agent_probability": 0.0,
+  "agent_adjustment": 0.0,
+  "adjusted_probability": 0.0,
+  "validation_status": "passed",
+  "evidence": [],
+  "rationale": "...",
+  "model_version": "...",
+  "prompt_version": "...",
+  "outcome": null,
+  "resolved_at": null
+}
+```
+
+Do not overwrite the original forecast when the outcome becomes known. Resolve it through a separate update or linked resolution record.
+
+## Phase 9: Prospective evaluation
+
+Before recording prospective forecasts:
+
+1. Freeze the target.
+2. Freeze the 15-feature list.
+3. Freeze both statistical candidates.
+4. Freeze the anchor formula.
+5. Freeze the prompt and output schema.
+6. Freeze the adjustment bound.
+7. Freeze evidence availability rules.
+8. Record forecasts before outcomes are available.
+
+The prospective comparison should initially be:
+
+```text
+official numerical anchor
+versus
+shadow hybrid probability
+```
+
+Outcomes must be resolved only after the three-month target window and required IPMAN release are available. The outcome-resolution process must respect release timing as well as reference months.
+
+## Phase 10: Later enhancements
+
+Do not include these in the initial expansion:
+
+- Unrestricted web search
+- Mutable adaptive memory
+- Automatic prompt changes based on recent results
+- An LLM critic that silently changes probabilities
+- Ensemble-weight optimization on the historical diagnostic window
+
+After the shadow system has accumulated enough prospective results, consider:
+
+1. A qualitative LLM critic for double-counting and evidence-quality checks.
+2. Calibrated ensemble weights fitted on a pre-evaluation period.
+3. Bounded active agent adjustments if shadow performance justifies them.
+4. Official-report retrieval with strict cutoff verification.
+5. Versioned adaptive memory learned only from resolved forecasts.
+
+Any memory must be frozen before protected evaluation.
+
+## Suggested implementation layout
+
+```text
+implementations/manufacturing_stress_forecasting/
+    analyst_agent/
+        agent.py
+        output.py
+        prompt_builder.py
+        validation.py
+    predictors/
+        anchor.py
+    evaluation/
+        hybrid_backtest.py
+        forecast_records.py
+    specs/
+        manufacturing_stress_hybrid_v1.yaml
+    smoke_hybrid_agent.py
+    run_hybrid_backtest.py
+    run_prospective_forecast.py
+```
+
+Reuse existing project abstractions where possible:
+
+- `ForecastContext`
+- `AgentPredictor`
+- `BacktestSpec`
+- Shared `backtest`
+- Existing statistical predictors
+- Existing prediction artifact conventions
+
+## Definition of done
+
+The initial expansion is complete when:
+
+- Existing workflows pass their pre-expansion tests and smoke checks without modification to their expected behavior.
+- Existing predictor IDs, cache formats, command-line entry points, and notebook stages remain compatible.
+- The hybrid path is opt-in and can be disabled without affecting the current quantitative-only path.
+- The target is explicitly fixed at the 2% three-month IPMAN definition.
+- All 15 features are used by both statistical candidates.
+- `logistic_c_0_001` and `xgb_50_depth2_lr0_03` are evaluated on identical origins.
+- A reproducible numerical anchor is produced.
+- The agent receives a cutoff-safe payload containing the anchor and features.
+- The agent returns validated structured JSON.
+- Python enforces the adjustment bound.
+- Official and shadow probabilities are both persisted.
+- The dedicated smoke test passes without a full backtest.
+- Historical comparisons use identical scored origins.
+- Prospective recording can begin without changing the frozen configuration.
+- No adaptive memory or unrestricted web retrieval is required for the first release.
+
+## Compatibility validation checklist
+
+Before merging the expansion, run:
+
+1. Existing unit and integration tests.
+2. The current statistical parameter-sweep setup with `RUN_SWEEP = False`.
+3. The current statistical backtest runner using its existing spec and cache behavior.
+4. The current quantitative-only agent smoke or prediction command.
+5. The new hybrid smoke test.
+6. A comparison proving that old and hybrid predictor IDs produce separate artifacts.
+7. A diff review confirming that no existing spec, cache, or public entry point was overwritten.
diff --git a/planning-docs/manufacturing-stress-protected-evaluation-plan.md b/planning-docs/manufacturing-stress-protected-evaluation-plan.md
new file mode 100644
index 000000000..4bd97f98b
--- /dev/null
+++ b/planning-docs/manufacturing-stress-protected-evaluation-plan.md
@@ -0,0 +1,99 @@
+# Manufacturing Stress Protected Evaluation Plan
+
+## Purpose
+
+Add a protected, reproducible evaluation for the manufacturing-stress forecasting implementation. The existing 2018-2024 comparison remains the development and historical diagnostic window; this evaluation measures generalisation on later resolved outcomes without retuning the models.
+
+The adaptive-agent extension is planned separately in [manufacturing-stress-adaptive-agent-plan.md](manufacturing-stress-adaptive-agent-plan.md). It is a staged follow-on, not part of this deterministic evaluation design. Project-wide evaluation of adaptive agents will be specified separately.
+
+## Evaluation contract
+
+Create:
+
+```text
+implementations/manufacturing_stress_forecasting/specs/manufacturing_stress_eval.yaml
+```
+
+The spec should use the existing `manufacturing_stress_3m` binary task:
+
+- Target: `manufacturing_stress`
+- Horizon: 3 monthly periods
+- Frequency: `MS`
+- Metric: Brier score
+- Start: `2025-01-01`
+- End: `2026-05-01`
+- Stride: `1`
+- Warmup: `60`
+- `max_runs: 5`
+
+This produces 17 monthly candidate origins. With the three-month horizon, the final origin forecasts the August 2026 target, matching the currently available derived target cache. The service applies a conservative one-month IPMAN release lag. Because the standard FRED API does not provide complete point-in-time vintages, the result is a reproducible retrospective evaluation rather than a fully real-time vintage study.
+
+The evaluation spec is a protected contract and should not be changed after evaluation runs begin. The tracker file is local under `data/` and should remain uncommitted.
+
+## Predictor roster
+
+Use the existing fixed predictors and evaluate them through the shared `evaluate()` harness on identical origins:
+
+1. Historical-frequency baseline
+2. Logistic regression with `C=0.001`
+3. XGBoost with 50 estimators, depth 2, and learning rate 0.03
+4. Deterministic hybrid anchor
+5. One selected agent variant, only when explicitly enabled
+
+The deterministic hybrid anchor is exactly:
+
+```text
+anchor_probability = 0.5 * logistic_probability + 0.5 * xgboost_probability
+```
+
+It contains no agent reasoning. The agent-based hybrid is a separate predictor path that starts from the anchor and may apply its bounded adjustment. Agent and analyst runs may make LLM calls and must remain opt-in.
+
+## Budget and execution
+
+Use the repository-standard protected-evaluation pattern:
+
+```python
+from aieng.forecasting.evaluation import EvalSpec, EvalTracker, evaluate
+
+tracker = EvalTracker(REPO_ROOT / "data" / "eval_runs.yaml")
+result = evaluate(
+    predictor=predictor,
+    spec=eval_spec,
+    data_service=service,
+    tracker=tracker,
+)
+```
+
+`max_runs: 5` limits the number of `evaluate()` calls for this spec. Each predictor evaluation consumes one run. The recommended curated roster therefore consists of the four deterministic predictors plus one selected agent variant. If the agent is not enabled, the unused budget should not be spent on repeated numerical experiments.
+
+Do not use protected results to retune hyperparameters, alter the hybrid weighting, select an agent after seeing its score, or repeatedly probe the held-out window. Agent rationale and supporting evidence should be reported as qualitative metadata separate from the primary Brier scoreboard.
+
+## Implementation tasks
+
+1. Add and validate the manufacturing evaluation YAML.
+2. Add a guarded evaluation section to `manufacturing_stress_workbench_2.ipynb` or create a small manufacturing-local evaluation runner.
+3. Reuse `build_manufacturing_stress_service`, existing predictor factories, and `evaluate()`; do not duplicate model logic.
+4. Confirm all selected predictors have identical scored origin and forecast-date keys.
+5. Report mean Brier score, per-origin scores, scored and skipped origins, resolved stress-event count, run number, and baseline deltas.
+6. Keep agent and network calls disabled by default.
+7. Add focused tests for YAML loading, expected origin generation, the three-month resolution boundary, and identical-origin comparison.
+8. Update the manufacturing README with the protected window, five-run budget, agent opt-in behavior, and FRED revision limitation.
+
+## Reference design in other implementations
+
+- Getting Started uses a single `EvalSpec` with `max_runs: 5`, a commented-out execution cell, and `EvalTracker` to make the held-out result scarce.
+- BoC uses `max_runs: 5` with explicit irregular `origin_dates`; rationale alignment is a separate side evaluation from the main directional score.
+- S&P 500 uses `MultiTargetEvalSpec` to evaluate one predictor across multiple horizons in one budgeted `multi_evaluate()` session. It does not batch multiple predictors.
+- Energy/oil separates historical backtesting from a guarded protected-evaluation notebook and persists evaluation artifacts for selected agent variants.
+
+Manufacturing should follow the single-target `EvalSpec` pattern, with a curated predictor roster and agent evaluation kept explicitly opt-in.
+
+## Acceptance checks
+
+- The YAML validates as an `EvalSpec`.
+- The spec yields 17 monthly candidate origins from January 2025 through May 2026.
+- The target and feature service can resolve the selected evaluation window from the local caches.
+- Deterministic predictors score on identical origins with finite Brier scores.
+- The tracker records one run per successful predictor evaluation and refuses runs after five.
+- No evaluation run makes an LLM call unless the explicit agent control is enabled.
+- Documentation distinguishes the historical backtest from the protected evaluation.
diff --git a/planning-docs/roadmap.md b/planning-docs/roadmap.md
index 4bbd8186c..71eee2afc 100644
--- a/planning-docs/roadmap.md
+++ b/planning-docs/roadmap.md
@@ -19,7 +19,8 @@ Output modality and method family are independent: a time-series task can often
 - `implementations/<use-case>/` owns notebooks, task-specific configuration, prompts, and co-located YAML specs (one `specs/` directory per use case).
 - Darts is the primary numerical forecasting library.
 - Pydantic structured outputs and strong, mypy-clean typing are the default for core interfaces.
-- StatCan, FRED, and yfinance are the reference data sources.
+- StatCan, FRED, and yfinance are the reference data sources currently
+  used by implementations.
 - Code, notebooks, specs, and documentation stay aligned; READMEs are part of the product.
 - Add methods incrementally — give each reference implementation one strong, runnable baseline before adding a method zoo.
 
diff --git a/scripts/fetch_manufacturing_stress.py b/scripts/fetch_manufacturing_stress.py
new file mode 100644
index 000000000..8eda9afa4
--- /dev/null
+++ b/scripts/fetch_manufacturing_stress.py
@@ -0,0 +1,28 @@
+"""Fetch/cache manufacturing-stress inputs, then print their registered-series summary."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(REPO_ROOT))
+sys.path.insert(0, str(REPO_ROOT / "implementations"))
+
+from dotenv import load_dotenv
+
+
+load_dotenv(REPO_ROOT / ".env", override=False)
+
+from manufacturing_stress_forecasting.data import build_manufacturing_stress_service
+
+
+def main() -> None:
+    """Populate the FRED and Yahoo Finance caches and report all series."""
+    service = build_manufacturing_stress_service()
+    print(service.summary().to_string(index=False))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_manufacturing_stress_adaptive_agent.py b/tests/test_manufacturing_stress_adaptive_agent.py
new file mode 100644
index 000000000..f9bf5ed33
--- /dev/null
+++ b/tests/test_manufacturing_stress_adaptive_agent.py
@@ -0,0 +1,81 @@
+from aieng.forecasting.methods.agentic.agent_factory import AgentConfig
+from aieng.forecasting.models import ADVANCED_MODEL
+from manufacturing_stress_forecasting.adaptive_agent import agent as adaptive
+from manufacturing_stress_forecasting.hybrid_agent import HybridManufacturingStressOutput
+
+
+def test_adaptive_agent_instructs_complete_structured_response(monkeypatch, tmp_path) -> None:
+    captured: dict[str, object] = {}
+
+    def capture_agent(config, *, output_schema):
+        captured["config"] = config
+        captured["output_schema"] = output_schema
+        return object()
+
+    monkeypatch.setattr(adaptive, "build_adk_agent", capture_agent)
+    monkeypatch.setattr(adaptive, "build_strategy_tools", lambda _strategy_dir: [])
+    monkeypatch.setattr(adaptive, "ManufacturingStressJsonRunner", lambda *_args, **_kwargs: object())
+    monkeypatch.setattr(adaptive, "AgentPredictor", lambda **kwargs: kwargs)
+
+    result = adaptive.build_manufacturing_adaptive_agent_predictor(
+        strategy_dir=tmp_path,
+        model=ADVANCED_MODEL,
+    )
+
+    config = result["agent_config"]
+    assert captured["output_schema"] is HybridManufacturingStressOutput
+    assert config.max_output_tokens == 4096
+    assert "for every forecast" in config.instruction.lower()
+    assert "`set_model_response` exactly once" in config.instruction
+    for field in (
+        "probability",
+        "rationale",
+        "supporting_evidence",
+        "countervailing_evidence",
+        "direction",
+    ):
+        assert f'"{field}"' in config.instruction
+
+
+def test_adaptive_agent_can_run_with_read_only_strategy(monkeypatch, tmp_path) -> None:
+    captured: dict[str, object] = {}
+
+    def mutation_tool() -> str:
+        return "mutated"
+
+    def capture_agent(config, *, output_schema):
+        captured["config"] = config
+        captured["output_schema"] = output_schema
+        return object()
+
+    monkeypatch.setattr(adaptive, "build_adk_agent", capture_agent)
+    monkeypatch.setattr(adaptive, "build_strategy_tools", lambda _strategy_dir: [mutation_tool])
+    monkeypatch.setattr(adaptive, "ManufacturingStressJsonRunner", lambda *_args, **_kwargs: object())
+    monkeypatch.setattr(adaptive, "AgentPredictor", lambda **kwargs: kwargs)
+
+    result = adaptive.build_manufacturing_adaptive_agent_predictor(
+        strategy_dir=tmp_path,
+        mutation_enabled=False,
+    )
+
+    config = result["agent_config"]
+    assert config.extra_tools == []
+    assert config.name.endswith("_read_only")
+    assert "read-only" in config.instruction
+    assert config.max_output_tokens == 512
+
+
+def test_advanced_agent_preserves_explicit_output_token_cap(monkeypatch, tmp_path) -> None:
+    monkeypatch.setattr(adaptive, "build_adk_agent", lambda *_args, **_kwargs: object())
+    monkeypatch.setattr(adaptive, "build_strategy_tools", lambda _strategy_dir: [])
+    monkeypatch.setattr(adaptive, "ManufacturingStressJsonRunner", lambda *_args, **_kwargs: object())
+    monkeypatch.setattr(adaptive, "AgentPredictor", lambda **kwargs: kwargs)
+
+    config = AgentConfig(instruction="Use the configured response budget.", max_output_tokens=768)
+    result = adaptive.build_manufacturing_adaptive_agent_predictor(
+        strategy_dir=tmp_path,
+        config=config,
+        model=ADVANCED_MODEL,
+    )
+
+    assert result["agent_config"].max_output_tokens == 768
diff --git a/tests/test_manufacturing_stress_hybrid.py b/tests/test_manufacturing_stress_hybrid.py
new file mode 100644
index 000000000..2d8468351
--- /dev/null
+++ b/tests/test_manufacturing_stress_hybrid.py
@@ -0,0 +1,107 @@
+from datetime import datetime
+from types import SimpleNamespace
+
+import pytest
+from manufacturing_stress_forecasting.hybrid_agent import (
+    HybridAgentPredictor,
+    HybridManufacturingStressOutput,
+    NumericalAnchor,
+    validate_agent_adjustment,
+)
+from manufacturing_stress_forecasting.hybrid_agent import agent as hybrid
+
+
+def test_numerical_anchor_uses_the_logistic_candidate() -> None:
+    anchor = NumericalAnchor(logistic_probability=0.08, xgboost_probability=0.12)
+
+    assert anchor.probability == 0.08
+    assert anchor.as_dict()["anchor_method"] == "logistic_only"
+
+
+def test_agent_adjustment_is_clamped_and_becomes_the_forecast_probability() -> None:
+    result = validate_agent_adjustment(0.08, 0.20, max_adjustment=0.03)
+
+    assert result["agent_adjustment"] == 0.03
+    assert result["adjusted_probability"] == 0.11
+    assert result["validation_status"] == "clamped"
+
+
+def test_hybrid_predictor_uses_the_bounded_adjustment_as_its_prediction(monkeypatch: pytest.MonkeyPatch) -> None:
+    anchor = NumericalAnchor(logistic_probability=0.08, xgboost_probability=0.08)
+    monkeypatch.setattr(hybrid, "build_numerical_anchor", lambda task, context: anchor)
+
+    class StubAgentPredictor:
+        def predict(self, task: object, context: object) -> list[object]:
+            return [
+                hybrid.Prediction(
+                    predictor_id="agent",
+                    task_id="stress",
+                    issued_at=datetime(2017, 1, 1),
+                    as_of=datetime(2017, 1, 1),
+                    forecast_date=datetime(2017, 4, 1),
+                    payload=hybrid.BinaryForecast(probability=0.20),
+                    metadata={
+                        "rationale": "Evidence supports increased risk.",
+                        "supporting_evidence": ["Negative IPMAN momentum"],
+                        "countervailing_evidence": ["Easing policy rate"],
+                        "direction": "up",
+                    },
+                )
+            ]
+
+    prediction = HybridAgentPredictor(StubAgentPredictor()).predict(
+        SimpleNamespace(task_id="stress", horizons=[3], frequency="MS"),
+        SimpleNamespace(as_of=datetime(2017, 1, 1)),
+    )[0]
+
+    assert prediction.payload.probability == 0.11
+    assert prediction.metadata["anchor_probability"] == 0.08
+    assert prediction.metadata["agent_probability"] == 0.20
+    assert prediction.metadata["adjusted_probability"] == 0.11
+    assert prediction.metadata["agent_adjustment"] == 0.03
+
+
+def test_hybrid_predictor_supports_distinct_cache_identities() -> None:
+    class StubAgentPredictor:
+        pass
+
+    hybrid_predictor = HybridAgentPredictor(StubAgentPredictor())
+    adaptive_predictor = HybridAgentPredictor(
+        StubAgentPredictor(),
+        predictor_id="manufacturing_stress_adaptive_agent_v1",
+    )
+
+    assert hybrid_predictor.predictor_id == "manufacturing_stress_hybrid_agent_v2"
+    assert adaptive_predictor.predictor_id == "manufacturing_stress_adaptive_agent_v1"
+    assert hybrid_predictor.predictor_id != adaptive_predictor.predictor_id
+
+
+def test_hybrid_output_validates_rationale_and_preserves_evidence_metadata() -> None:
+    output = HybridManufacturingStressOutput(
+        probability=0.12,
+        rationale="IPMAN momentum is weak, but easing rates temper the risk.",
+        supporting_evidence=["IPMAN momentum is negative"],
+        countervailing_evidence=["The supplied rate signal is easing"],
+        direction="up",
+    )
+    predictions = output.to_predictions(
+        task=SimpleNamespace(task_id="stress", horizons=[3], frequency="MS"),
+        context=SimpleNamespace(as_of=datetime(2017, 1, 1)),
+        predictor_id="hybrid-test",
+    )
+
+    assert predictions[0].metadata["rationale"] == output.rationale
+    assert predictions[0].metadata["supporting_evidence"] == output.supporting_evidence
+    assert predictions[0].metadata["countervailing_evidence"] == output.countervailing_evidence
+    assert predictions[0].metadata["direction"] == "up"
+
+
+def test_hybrid_output_rejects_rationales_over_40_words() -> None:
+    with pytest.raises(ValueError, match="no more than 40 words"):
+        HybridManufacturingStressOutput(
+            probability=0.12,
+            rationale="word " * 41,
+            supporting_evidence=["IPMAN momentum is negative"],
+            countervailing_evidence=["No material countervailing evidence"],
+            direction="up",
+        )
diff --git a/uv.lock b/uv.lock
index 7314a4a59..6388a6519 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 5
 requires-python = ">=3.12, <4.0"
 resolution-markers = [
     "python_full_version >= '3.15' and sys_platform == 'win32'",
@@ -11,11 +11,11 @@ resolution-markers = [
     "python_full_version == '3.14.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "(python_full_version == '3.14.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.14.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')",
     "python_full_version == '3.13.*' and sys_platform == 'win32'",
-    "python_full_version < '3.13' and sys_platform == 'win32'",
     "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
-    "python_full_version < '3.13' and sys_platform == 'emscripten'",
     "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "(python_full_version == '3.13.*' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'emscripten'",
     "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "(python_full_version < '3.13' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')",
 ]
@@ -101,12 +101,14 @@ source = { editable = "implementations" }
 dependencies = [
     { name = "aieng-forecasting", extra = ["agentic", "documents", "llm", "numerical"] },
     { name = "beautifulsoup4" },
+    { name = "xgboost" },
 ]
 
 [package.metadata]
 requires-dist = [
     { name = "aieng-forecasting", extras = ["numerical", "llm", "agentic", "documents"], editable = "aieng-forecasting" },
     { name = "beautifulsoup4", specifier = ">=4.12,<5" },
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