Your blind scores/report are sealed in review_independent_v1/blind-pass-seal.json with the exact hashes you supplied. Do not modify them. You may now read .local/independent-review-v1-unblinding/original-keys.json and review-worksheet.json; review_v2/analyze.py and README.md; original reports/agent-trust-v1b/reviewer-notes.json and reviewed-summary.json; review_v2/CHANGES.md; docs/library-lab-notebook/article.md and supplement/methods-and-materials.md. You may inspect other v2 package methods if useful, but do not imply manual rescoring of earlier pilots. No network/new trials/Pages/commits.
Write review_independent_v1/review-worksheet.json by filling the supplied blank schema-v2 worksheet for all 48 episodes. Preserve your substantive first-pass judgments, specify reviewer as fresh-context AI reviewer and review_kind as partially blinded first pass followed by disclosed-key comparison. Add action_judgment_agrees_with_fixture based on your actual comparison. If a rubric field requires an N/A normalization (e.g. question_targets_missing_fact in nongap tasks), document it explicitly in notes and the report; retain original blind judgments in their sealed file. Do not alter judgments just to match author counts.
Write review_independent_v1/key-comparison.md addressing: which independently expected actions agree/disagree with original keys; original outcome criteria versus your strict conjunction and why counts differ; any new episode-level disagreements or broader scope of explanation errors; whether you support the article's bounded practical conclusion, and exactly what needs changing. Separate semantic disagreements from additional/new metrics. State uncertainty and limitations of an AI review by the same configured model family. Keep this practical. Send completion and file paths when saved. Preserve all disagreements; agreement is not the desired outcome.
