Scaffold Reverso MVP pipeline structure

Set up the project skeleton per PLAN.md §4:
- src/ package: identifiers, disease, drugs, scoring, provenance
  with pydantic schemas and confidence-tier logic (working);
  data-pull/compute functions stubbed per their build week
- 5 starter notebooks (01-05) with PLAN-referenced steps
- tests/test_scoring.py: tier-assignment tests pass; scoring
  reference test xfail until Week 3
- docs/: recovery_test_report, data_sources, known_limitations skeletons
- pyproject.toml (requires-python >=3.11,<3.14), .gitignore, README
- data/ tree preserved via .gitkeep; raw/processed/results gitignored

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/provenance.py

@@ -0,0 +1,72 @@
+"""Provenance and confidence-tier tracking.
+
+The confidence tier is the most commercially important design decision in the pipeline
+(PLAN.md §3). *Every* persisted artifact — signatures and drug profiles alike — must carry
+a tier and the provenance needed to justify it.
+
+    Tier A — measured data, peer-reviewed source, n>10 per group, recent
+    Tier B — measured but small-n, older, or single-source
+    Tier C — inferred / extrapolated / hypothesis-only
+"""
+
+from __future__ import annotations
+
+from datetime import date
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class ConfidenceTier(str, Enum):
+    """Confidence tier for a persisted artifact. See module docstring."""
+
+    A = "A"
+    B = "B"
+    C = "C"
+
+
+class Provenance(BaseModel):
+    """Where a record came from and when. Attached to every persisted artifact."""
+
+    source: str = Field(..., description="Human-readable source name, e.g. 'GEO', 'ChEMBL'.")
+    source_id: str | None = Field(
+        None, description="Accession / identifier within the source, e.g. 'GSE53441'."
+    )
+    source_url: str | None = None
+    source_version: str | None = Field(
+        None, description="Dataset/release version where the source is versioned."
+    )
+    download_date: date | None = Field(
+        None, description="Date the underlying data was downloaded (reproducibility)."
+    )
+    license: str | None = None
+    notes: str | None = None
+
+
+def assign_tier(
+    *,
+    is_measured: bool,
+    n_per_group: int | None,
+    peer_reviewed: bool,
+    single_source: bool,
+) -> ConfidenceTier:
+    """Assign a confidence tier from the evidence characteristics.
+
+    This encodes the tier rules from PLAN.md §3 so tier assignment is consistent and
+    auditable rather than ad-hoc per notebook.
+
+    Args:
+        is_measured: True if the value is directly measured (vs inferred/extrapolated).
+        n_per_group: Sample size per group, if applicable (None when not meaningful).
+        peer_reviewed: Whether the source is peer-reviewed.
+        single_source: Whether the evidence rests on a single source.
+
+    Returns:
+        The assigned ``ConfidenceTier``.
+    """
+    if not is_measured:
+        return ConfidenceTier.C
+    if peer_reviewed and (n_per_group is not None and n_per_group > 10) and not single_source:
+        return ConfidenceTier.A
+    # Measured, but small-n / older / single-source falls to Tier B.
+    return ConfidenceTier.B