{
  "timestamp_utc": "2026-09-30T02:23:43.612283+00:00",
  "sources": {
    "README.md": "58f2f78b94aa783cc30b4b5e79ab003cc83f8a09b66c195d963bca6ee691e44c",
    "apl_sources.py": "61f4c35b464315d19bbffce534cfc037a0111725da0eb07ee53a41db81767fcb",
    "context.py": "a166da3d964b49ca28a013d3a3cf33df1607c76d4a6a53a8ca8e2d955aa92cb0",
    "finalize.py": "40e0f8a9e075db42ac63163de8362d808b9110f371e49f6d4088267eecf2eb4c",
    "pn_observer.py": "0663c1f723f614ebcdb666b46eaa49288aec26125d6aceb18cd237330a705968",
    "prepare.py": "9fbef01968c54637c9063bb1e2d7f45fdc97b886b3c25815a6146fc5f219164c",
    "publication.py": "7a79c499098b74163e7bb16adabcbc54e7069dafa204d0b99c7f4c03d79f8016",
    "run_audit.py": "a5a7861e8ced4739cd1a1f4314b8af7cff71168499f1ae6ed94ac71b481bcdd8",
    "structure_metrics.py": "669e91a862efea5f243a054c883bc1a183e96b0bcf02fac5f6972c4a8272a4cb",
    "test_apl_sources.py": "a7ac0f38877e0244a4bcc574264c72617ec487544a19dbc705e0c1e26c4be78d",
    "test_harness.py": "c2e66adb18d25264d21bfce16462ef698a3ba09d178908bef1068881d46ac1e7",
    "test_pn_observer.py": "fff3cb9da17d9d638e53a609a265d6531ed82824d10e06fcb3863edfeab7a267",
    "test_publication.py": "a3516aefa6a753f8fca6c5a4c2109ae4d65ce0ab0278e4795d537148f04495ff",
    "test_structure_metrics.py": "de1298a4a92facad97339e9a4cdb60ea2b7c30adc1a6f05274bd3be034239717",
    "test_validate_events.py": "d687ceb171c7dad77dfe2eb53524091a7ee02ddc04d9bbafb3b4a59b0d1eb66c",
    "validate_events.py": "32a33988c5c9619b2ff3553c2d508924a404ade17b526541a2ff113a61df68e4"
  },
  "inputs": [
    {
      "path": "experiments/phase4ah-kc-drive-audit/manifest.json",
      "sha256": "fb30ca09068d5d8dc1dc9972e00f674c5dc3aa49c7117d630607b5d9067927db"
    },
    {
      "path": "experiments/phase4ah-kc-drive-audit/checksums.sha256",
      "sha256": "567a3eb5758763c86146235835984a352dd860bf69489d1cf2b048d93fba05c9"
    },
    {
      "path": "models/male-cns-lif/kc-drive-audit-v1/manifest.json",
      "sha256": "fa89cb0a6e2dc77462057b672ffbcfabbe74b893d8f70e4b41d8b5202de15530"
    },
    {
      "path": "models/male-cns-lif/kc-drive-audit-v1/checksums.sha256",
      "sha256": "ed082e56978ac841c1fd92810eb611b63d6c2238d0406c92c281bb5023bc9f70"
    },
    {
      "path": "models/male-cns-lif/kc-drive-audit-v1/replay_core.py",
      "sha256": "aa3187cb23712c09ab603e27e60424294db92de8086d60a95cf7a629d0521fdc"
    },
    {
      "path": "models/male-cns-lif/kc-drive-audit-v1/populations.py",
      "sha256": "8504b38219bac952b07a67e7dc37a520f0a68238cb0b0dd56f4b4b7948c5233b"
    },
    {
      "path": "experiments/phase4ag-author-apl-r2/manifest.json",
      "sha256": "21593297c40bee58516d4d7fd16e9d8d20ffdea5f8e0652fa5dd4e903b4d96ee"
    },
    {
      "path": "experiments/phase4ag-author-apl-r2/checksums.sha256",
      "sha256": "dcc61d2251cb1b425196f180e3ec3816d1fa5eb91aefda7940f9d821aae82d19"
    },
    {
      "path": "models/male-cns-lif/author-apl-lab-v2/manifest.json",
      "sha256": "208c00961b9d895d14ac050bc23fbc18db49d0df1fcf5c20d4c3eaa8e15ff7ff"
    },
    {
      "path": "models/male-cns-lif/author-apl-lab-v2/checksums.sha256",
      "sha256": "effb9ef2cbd08ec973a01c2b9b2a9047fc0a42732601a734e733d56b93029ff1"
    },
    {
      "path": "datasets/male-cns-v1.0/processed/neuron-only/neuron-index/unified-metadata.parquet",
      "sha256": "922af16846a065c3dbfca7c11146b83ca217fcbd7392edc745f444a04eae8c63"
    },
    {
      "path": "experiments/phase4af-author-fullgraph/oracle/no_input/coefficients.json",
      "sha256": "bde79d95ef0097b827d5faf4d5658eafc009d8e9fcef1fddd645c953823e3f64"
    },
    {
      "path": "datasets/male-cns-v1.0/processed/neuron-only/gpu/csr-weights.int64.npy",
      "sha256": "c7ae101451dd19c47fd976bcfacbc52d67193a68ffa04c5b4d2e911cfd52d99b"
    }
  ],
  "parent_commit": "9437bbff79de448cff0bb438bc53a1fbab482938",
  "scope": "Post-outcome conditional PN/APL source audit; no new whole-brain simulation or learning",
  "previous_KC_outcome_known": true,
  "before_PN_contribution_accounting": true,
  "new_GPU_use": false,
  "max_threads": 4,
  "output_budget_bytes": 2147483648,
  "per_trial_first_arrival_row_limit": 1000000,
  "truncation_allowed": false,
  "exact_gate": "All PN events/counts/final v,g,ref; fixture pre/post histories; 12 deterministic accounting repeats",
  "APL_gate": "Source float64 mathematical accounting within prior standard-float32 analytic roundoff bounds; not CUDA bitwise replay",
  "hypotheses": {
    "temporal_precedence": "For every trial and every KC first spike, non-design PN spike must be strictly earlier; equals/after/absent retained",
    "direct_PN_KC_arrival": "PN source tick+18 strictly less than KC first spike; same tick arrival is after threshold",
    "APL_source_precedence": "Group h write at tick s affects only KC threshold ticks greater than s",
    "APL_share": "Cumulative group shares are descriptive float64; no accumulated source-share error bound or robust-majority claim",
    "odor_structure": "Fixed 686-dimensional counts; secondary fixed outside-six-odor-design-union subset; no activity-fraction or learning gate"
  },
  "attribution_limits": [
    "Accepted edge antecedents are not proof of single-neuron necessity or intervention causality",
    "Non-design means not in static design targets; does not mean noise or biologically incorrect",
    "Source-V lanes are non-feedback accounting with initial/constant/request/reset/roundoff lanes",
    "APL input matrix is legacy nonnegative raw-contact-normalized engineering mapping, not signed LIF current",
    "No CAL per-tick target state was independently saved; final states and events only",
    "Fixtures generated h for 4064 KC but applied only to fixture KC"
  ],
  "python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]",
  "numpy": "2.2.6",
  "pandas": "2.3.3",
  "machine": "x86_64",
  "longdouble_mantissa_bits": 63
}