# Codex response to retrieval review round 2

All round-2 findings were accepted. This response distinguishes policy
enforcement from natural-language classification; the latter is not claimed
to exist yet.

## Changes

- Removed `HumanClinicalScopeGuard` and its animal keyword list. Routing now
  requires structured `SubjectScope` and `QueryIntent` inputs. Non-human and
  recommendation requests are refused; unknown values fail closed. The API or
  classifier that supplies these fields remains future work.
- Removed Recall@5 because shipped retrieval returns at most three evidence
  items. Reports contain Recall@1 and Recall@3 only.
- Added `resolved_drug_id` and `drug_resolution_status` to results. Evaluation
  now reports drug-resolution accuracy and status counts.
- Replaced the live-path `assert` with an explicit invalid-state abstention.
- Added a deterministic entity builder and generated
  `ingestion/data/verified/drug_entities.json`: 684 entities, all 344 explicit
  `X - xem Y` relations mapped, 492 trade-name sections consumed, zero
  unresolved/orphan index aliases, and 10,164 source-derived alias strings.
- Parenthesised headings are split into valid aliases. `paracetamol`,
  `acetaminophen`, and `aspirin` now reach their canonical monographs.
- Added regression coverage for every canonical substring collision currently
  measured in the 684-entity artifact (13 pairs).
- Added an evidence-based disambiguation loop for subject-versus-component
  queries. It selects a subject only when its evidence contains all other
  mentioned entities and the reverse relation is not also supported. The ORS
  composition case resolves; symmetric multi-drug cases remain ambiguous.

## Measured diagnostic

Against `scratch/rag-table-pilot/out/all` (whole-corpus prose plus the complete
identified structured-block inventory; not every source page contains a
structured block):

- 10 manual cases: 9 positive, 1 policy-enforcement negative
- Recall@1: 1.0
- Recall@3: 1.0
- drug-resolution accuracy: 1.0 (9/9 in-scope human cases)
- negative policy enforcement: 1.0 (1/1)
- expert release gate: 0 cases, metrics `null`

These ten cases are a diagnostic, not clinical-production evidence.

## Commands reproduced

```text
python -m pytest -q                           # ingestion: 206 passed
python -m pytest tests -q                     # ai-service: 14 passed
python -m ruff check rag tests                # passed
python -m ingestion.entities.catalog ...      # 684 / 344 / 492 / 0 unresolved
python -m rag.run_eval ...                    # R@1 1.0, R@3 1.0, resolver 1.0
```

No cloud call was made and no AWS cost was incurred.