# Batch 01 Review

## Review Metadata

- Batch ID: `2026-05-17-batch-01`
- Case ID: `case-public-sector-ai-policy-audit-001`
- Arm ID: `full_ofone`
- Run ID: `2026-05-17-batch-01__case-public-sector-ai-policy-audit-001__full_ofone__agentic_coding__r3`
- Reviewer: local Codex unblinded review
- Review date: 2026-05-18
- Blinding status: `unblinded`

## Pre-Score Compliance Gate

| Check | Result | Notes |
| --- | --- | --- |
| Case fidelity | `pass` | Artifact identity and artifact-level benchmark trace bind to `case-public-sector-ai-policy-audit-001`, repeat 3, the full-OfOne prompt, and the expected input-bundle hash. |
| Required outputs present | `pass` | Raw Markdown output, Audit artifact JSON, validator JSON, rendering, patch report, and review file are present. |
| Independence from other arms/examples | `pass` | The artifact is case-native for the public-sector AI policy audit repeat-3 slot and does not reuse another case artifact or another benchmark arm output. |
| No-superiority compliance | `pass` | The output does not claim empirical superiority. |
| Auto-reject before aggregate scoring | `no` | No pre-score gate row failed. |

## Semantic Fidelity

- Case binding: Pass; the response addresses public-sector AI deployment, rights, legitimacy, model evidence, gates, review logs, stakeholder exposure, and operational constraints.
- Copied-example risk: Low; the response is scoped to the public-sector AI policy audit case and repeat-3 run metadata.
- Evidence provenance adequacy: Scenario-level only; acceptable for this benchmark slice and explicit about missing external policy, model, rights, appeal, and monitoring records.
- Artifact/source identity: Artifact, run ID, case ID, prompt hash, input-bundle hash, validator, rendering, patch report, and review file align with the repeat-3 run record.

## Scores

| Metric | Score | Rationale |
| --- | ---: | --- |
| decision_quality | 4 | Correctly blocks live deployment and returns the policy for model, rights, appeal, stakeholder, and review evidence. |
| evidence_grounding_precision | 4 | Uses stable content hashes and separates case evidence from claims, gates, actors, and review-log objects. |
| uncertainty_calibration | 4 | Open unknowns block release and rendering confidence without overstating deployability. |
| trace_completeness | 5 | Links evidence, claims, unknowns, criteria, actors, lenses, gates, review logs, triggers, and rendering. |
| auditability | 5 | Includes case-native artifact, benchmark trace, validator JSON, Audit rendering, patch report, and review file. |
| update_quality | 4 | Trigger activation reaches claims, gates, review logs, options, council result, tradeoff surface, and rendering. |
| cost | 2 | Full artifact and machine-output burden is materially higher than text arms. |
| inter_run_stability | NA | Deferred until aggregate scoring. |

## Required Notes

- Strongest decision-relevant contribution: It makes model evidence gaps, rights and legitimacy claims, stakeholder exposure, gates, review-log objects, and patch closure inspectable as decision state.
- Most important unsupported claim or missing evidence: The benchmark dossier lacks real external policy, model validation, subgroup, appeal-access, stakeholder consultation, review-log, and operational-monitoring artifacts.
- Hidden variable or unknown that changed the review: Symbolic review, automation bias, and inaccessible appeal paths can undermine a nominal human-in-the-loop policy.
- Gate, safety, or release concern: Live release remains blocked until public-policy, rights, model-evidence, and review-log gates close with evidence.
- Patch/update behavior, if applicable: Trigger `T1` propagates human-review pressure through gates, claims, council result, options, review logs, tradeoff surface, and rendering; `T2` and `T3` remain available for scoped rerun or conflict patching.
- Failure mode observed: No case-binding failure; remaining limitation is that the benchmark dossier remains abstract and lacks real external policy, model, subgroup, appeal, or monitoring artifacts.

## Adjudication

- Accept run for aggregate scoring: `yes`
- If no, reason: N/A
- Reviewer confidence: `medium`