{
  "schema_version": 1,
  "status": "passed",
  "protocol_id": "five-photo-geometry-v1",
  "audited_at_utc": "2026-10-10T21:06:58.659934+00:00",
  "reference_source_commit": "feead1da4d77bb0dc674fcf0f5f38979fac9a90c",
  "audit_script_sha256": "a5a1f7a4846e458127c0a151e5b4647a9eefa432289768f18f78d79653f95002",
  "runtime": {
    "python": "3.12.14",
    "numpy": "2.3.5"
  },
  "independence": "No primary extractor or solver code imported. Scalar fsum cosine geometry from saved float32 features; separate NumPy MSE/correlation replay of all 120 bijections.",
  "checks_performed": [
    "published three-file source lock",
    "frozen source bytes",
    "frozen feature helper bytes",
    "protocol identity, labels and tie tolerance",
    "complete original manifest identity and labels",
    "complete feature metadata",
    "feature provenance hashes",
    "feature and preflight rows match original manifest",
    "preflight and final input hash records agree",
    "recorded model-verification flags",
    "model and source provenance matches protocol",
    "recorded frozen inference settings",
    "extraction artifact hashes",
    "exact feature archive fields",
    "feature row order and unique identities",
    "feature dimensions, dtype and finite values",
    "nonzero unit feature vectors",
    "declared independent shuffle seeds and index mappings",
    "answer-free solver input schema",
    "solver received identical blind matrices",
    "blind matrix provenance",
    "opaque shuffled IDs",
    "all 50 matrix entries replayed from raw features",
    "human mapping replayed from original manifest and both shuffles",
    "all 120 bijections present exactly once in declared order",
    "every saved assignment mapping",
    "all 120 MSE costs independently recomputed",
    "all 120 distance correlations independently recomputed",
    "every tolerance-based optimum flag",
    "selected optimum set and ties",
    "minimum objective",
    "gap to next distinct cost",
    "selected-result provenance hashes",
    "selected-result frozen source hashes",
    "completed solver output checksums",
    "evaluation provenance hashes",
    "evaluated human labels and pairing",
    "human-assignment rank interval and optimum status",
    "evaluated ties and correctness range",
    "human-assignment objective",
    "evaluated objective gap",
    "mean correct count",
    "mean correct fraction",
    "uniform random row-match reference",
    "uniform random expected correct count",
    "each evaluated optimum and its correctness",
    "token report covers all five labels",
    "fixed tokenization and reported coverage accounting",
    "aggregate token and OOV accounting",
    "reported token-coverage fraction",
    "all audited input bytes unchanged during replay"
  ],
  "input_sha256_by_role": {
    "features": "d9924e021e2b4233493930454ca7d32be2cde55617d1ed8aaca83bf11b528515",
    "feature_metadata": "52329157770a80d6f751e7af3bae90da985ddd528940524f334ee85146b554a0",
    "extraction_preflight": "569fa0680948dcefe1e94defe32668de258af2e26d23c3462eb0aa0e8e8667ac",
    "extracted_blind_matrices": "4614d2b1ab377d6ca252322deb7c31589dad657f8f310eb4e2fce6335cc9df24",
    "ground_truth": "09f81d617a232360d5ebe490c265faa70763e908e1572a6b275c1f9b5cffada5",
    "solver_blind_matrices": "4614d2b1ab377d6ca252322deb7c31589dad657f8f310eb4e2fce6335cc9df24",
    "assignments": "7d7d14947dcff9320d6c685e35bb1a736bcc9e1875f72d31b0d04d4689f2461e",
    "selected": "a1e438fbe79ce42bae93e92e6139e825e7ac368c56abcfe7e5221ad3e9f19b11",
    "selection_checksums": "c0e8eb1501230a1a8f53117cc05a4773d8774585e753517d0766183e841a79dc",
    "evaluation": "6561389d21da6a9450205cf9fc158b291ea17af8ca1253c2ec129b13606cfe01",
    "private_manifest": "252eab59d902fcb3a772a73782d46156d6f53c9bf67bc3f79010b52e95287ece",
    "release_lock": "28bf4082cc710aeced85b2e59911b9e8c44b8728469592c0d9423b44f3daf333",
    "frozen_helper": "3e8445f22a8f255dcf47af89ac0b8c45461ba98046549702abc7b09b6b59a624",
    "source_protocol.json": "fcc1c4252ae268e7dcf645419eaea07e1196c09e939a7066e55cf084afddf603",
    "source_extract_five.py": "37665c8dbb0d2479bfc162854ad0508d6e1b89a6dc8ee51f75bb4d095c14a823",
    "source_solve.py": "6819921cecd461dc841b1ea89dddb18fab53fc0c36c07b631d7f755346ce5dfc"
  },
  "replay_tolerance_absolute": 2e-14,
  "tie_tolerance_absolute": 1e-12,
  "maximum_distance_matrix_absolute_error": 3.3306690738754696e-16,
  "maximum_cost_absolute_error": 5.551115123125783e-17,
  "maximum_correlation_absolute_error": 1.1102230246251565e-15,
  "permutations_replayed": 120,
  "distance_entries_replayed": 50,
  "minimum_cost": 0.03781860020010573,
  "optimal_assignment_count": 1,
  "optimal_assignments": [
    {
      "text_indices": [
        0,
        4,
        1,
        3,
        2
      ],
      "correct_out_of_5": 1,
      "cost": 0.03781860020010573,
      "distance_correlation": 0.8782134342420859,
      "matches": [
        {
          "image_id": "I00",
          "human_label": "Basketball",
          "assigned_label": "Slide",
          "correct": false
        },
        {
          "image_id": "I01",
          "human_label": "Slide",
          "assigned_label": "Basketball",
          "correct": false
        },
        {
          "image_id": "I02",
          "human_label": "Water fountain",
          "assigned_label": "Water fountain",
          "correct": true
        },
        {
          "image_id": "I03",
          "human_label": "Trashcan",
          "assigned_label": "Tree",
          "correct": false
        },
        {
          "image_id": "I04",
          "human_label": "Tree",
          "assigned_label": "Trashcan",
          "correct": false
        }
      ]
    }
  ],
  "mean_correct_out_of_5": 1.0,
  "correct_out_of_5_range": [
    1,
    1
  ],
  "mean_correct_fraction": 0.2,
  "human_assignment_cost": 0.07347346776688787,
  "human_assignment_rank_interval": [
    94,
    94
  ],
  "gap_to_next_distinct_cost": 0.00028064526353802077,
  "uniform_random_expected_correct_out_of_5": 1.0,
  "tokens": [
    {
      "label": "Slide",
      "tokens": [
        "slide"
      ],
      "reported_oov_tokens": []
    },
    {
      "label": "Trashcan",
      "tokens": [
        "trashcan"
      ],
      "reported_oov_tokens": []
    },
    {
      "label": "Tree",
      "tokens": [
        "tree"
      ],
      "reported_oov_tokens": []
    },
    {
      "label": "Basketball",
      "tokens": [
        "basketball"
      ],
      "reported_oov_tokens": []
    },
    {
      "label": "Water fountain",
      "tokens": [
        "water",
        "fountain"
      ],
      "reported_oov_tokens": []
    }
  ],
  "not_performed": [
    "No encoder inference replay or independent GloVe lookup",
    "No inspection or rehash of photograph bytes",
    "No independent authentication of human labels or capture chronology",
    "No audit of process-level access to the answer file"
  ],
  "scope": "Numerical and recorded-provenance consistency for five selected items only. Exploratory transductive comparison, not Rosetta, a p-value, a confidence interval, or held-out generalization.",
  "privacy": "No photographs, raw feature vectors, private paths or capture metadata are included."
}