{
  "schema_version": 1,
  "artifact": "embedding_dict.json",
  "distribution": "external_runtime_asset_not_tracked_by_git",
  "embedding_model": "embedding-3",
  "semantic_similarity_threshold": 0.85,
  "semantic_similarity_operation": "raw_dot_product_historical_implementation_on_approximately_unit_norm_vectors",
  "sha256": "df3c3dfd625f8205c278d9d7b22641cfbf351c886559abb1894c663bb657d067",
  "size_bytes": 548127946,
  "entry_count": 19644,
  "embedding_dimension": 2048,
  "non_finite_vector_count": 0,
  "inconsistent_dimension_count": 0,
  "blank_key_count": 1,
  "vector_l2_norm": {
    "minimum": 0.9999337429879811,
    "mean": 0.999999939836742,
    "median": 0.9999999995442519,
    "maximum": 1.000054469389027,
    "maximum_absolute_deviation_from_one": 0.0000662570120189
  },
  "frozen_at": "2026-08-22",
  "recommended_file_mode": "0444",
  "runtime_environment": {
    "ORIENTER_EMBEDDING_CACHE": "/absolute/path/to/embedding_dict.json",
    "ORIENTER_EMBEDDING_OFFLINE": "1",
    "ORIENTER_EMBEDDING_READONLY": "1"
  },
  "notes": [
    "Verify the SHA-256 before evaluation.",
    "Offline mode fails before an embedding API call when a required category is absent.",
    "The paper describes cosine similarity, while the historical evaluator uses a raw dot product. The cached vectors are approximately unit norm, so the operations are numerically close but not formally identical.",
    "The frozen artifact is unchanged from the historical evaluator cache; the single blank key is retained for byte identity and is not used by the validated evaluation paths."
  ]
}