{
  "missing_values": "Retained from frozen inputs; missing does not mean negative or zero.",
  "configs": {
    "pairs": {
      "rows": 1152,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "large_string",
          "description": "Original corpus and split identifier."
        },
        "family": {
          "type": "large_string",
          "description": "Source corpus family."
        },
        "item_id": {
          "type": "large_string",
          "description": "Encounter identifier within source_dataset."
        },
        "distractor_type": {
          "type": "large_string",
          "description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
        },
        "clean_transcript": {
          "type": "large_string",
          "description": "Unmodified source transcript (Notes) or saved ASR of clean recording (Audio)."
        },
        "distracted_transcript": {
          "type": "large_string",
          "description": "Transcript with inserted conversation (Notes) or saved ASR of mixed recording (Audio)."
        },
        "reference_note": {
          "type": "large_string",
          "description": "Reference clinical note supplied by original source corpus."
        },
        "distractor_conversation": {
          "type": "large_string",
          "description": "Inserted exchange or human-transcribed donor speech."
        },
        "insert_after_turn": {
          "type": "int64",
          "description": "One-based speaker-turn insertion position."
        },
        "distractor_summary": {
          "type": "large_string",
          "description": "Target content supplied to the contamination judge."
        },
        "distractor_topic": {
          "type": "large_string",
          "description": "Generated aside topic or audio donor-content label."
        },
        "generation_model": {
          "type": "large_string",
          "description": "Identifier of insertion-generation model."
        }
      }
    },
    "insertions": {
      "rows": 1152,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "large_string",
          "description": "Original corpus and split identifier."
        },
        "item_id": {
          "type": "large_string",
          "description": "Encounter identifier within source_dataset."
        },
        "distractor_type": {
          "type": "large_string",
          "description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
        },
        "distractor_topic": {
          "type": "large_string",
          "description": "Generated aside topic or audio donor-content label."
        },
        "distractor_conversation": {
          "type": "large_string",
          "description": "Inserted exchange or human-transcribed donor speech."
        },
        "distractor_summary": {
          "type": "large_string",
          "description": "Target content supplied to the contamination judge."
        },
        "insert_after_turn": {
          "type": "int64",
          "description": "One-based speaker-turn insertion position."
        },
        "assigned_content": {
          "type": "large_string",
          "description": "Clinical content assigned to the insertion generator."
        },
        "style": {
          "type": "large_string",
          "description": "Assigned insertion style."
        },
        "attempts": {
          "type": "int64",
          "description": "Number of insertion generation attempts."
        },
        "body_system": {
          "type": "large_string",
          "description": "Organ system label for the underlying encounter."
        },
        "generation_model": {
          "type": "large_string",
          "description": "Identifier of insertion-generation model."
        },
        "timestamp": {
          "type": "large_string",
          "description": "Saved generation timestamp."
        }
      }
    },
    "organ_systems": {
      "rows": 577,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "large_string",
          "description": "Original corpus and split identifier."
        },
        "item_id": {
          "type": "large_string",
          "description": "Encounter identifier within source_dataset."
        },
        "body_system": {
          "type": "large_string",
          "description": "Organ system label for the underlying encounter."
        },
        "rationale": {
          "type": "large_string",
          "description": "Model-generated explanation of organ-system label."
        },
        "labeller": {
          "type": "large_string",
          "description": "Model used to label the encounter."
        }
      }
    },
    "notes": {
      "rows": 18432,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "large_string",
          "description": "Original corpus and split identifier."
        },
        "item_id": {
          "type": "large_string",
          "description": "Encounter identifier within source_dataset."
        },
        "family": {
          "type": "large_string",
          "description": "Source corpus family."
        },
        "model": {
          "type": "large_string",
          "description": "Note-generation model identifier used in saved experiment."
        },
        "distractor_type": {
          "type": "large_string",
          "description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
        },
        "condition": {
          "type": "large_string",
          "description": "clean or distracted."
        },
        "note": {
          "type": "large_string",
          "description": "Generated clinical note; empty saved records are retained."
        },
        "note_chars": {
          "type": "int64",
          "description": "Character count of saved note."
        },
        "transcript_chars": {
          "type": "int64",
          "description": "Character count of note-generation input."
        }
      }
    },
    "judgments": {
      "rows": 18432,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "large_string",
          "description": "Original corpus and split identifier."
        },
        "item_id": {
          "type": "large_string",
          "description": "Encounter identifier within source_dataset."
        },
        "family": {
          "type": "large_string",
          "description": "Source corpus family."
        },
        "model": {
          "type": "large_string",
          "description": "Note-generation model identifier used in saved experiment."
        },
        "distractor_type": {
          "type": "large_string",
          "description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
        },
        "condition": {
          "type": "large_string",
          "description": "clean or distracted."
        },
        "contamination_v3": {
          "type": "int8",
          "description": "Primary paired-judge binary target contamination flag."
        },
        "severity_v3": {
          "type": "int8",
          "description": "Primary paired-judge severity 0–3 (absent to clinically consequential incorporation)."
        },
        "judge_reasoning_v3": {
          "type": "large_string",
          "description": "Model-generated primary paired-judge explanation."
        },
        "judge_protocol": {
          "type": "large_string",
          "description": "Scoring protocol identifier."
        },
        "clinical_correctness": {
          "type": "int8",
          "description": "Automated quality score, 1–5, higher is better."
        },
        "completeness": {
          "type": "int8",
          "description": "Automated completeness score, 1–5, higher is better."
        },
        "succinctness": {
          "type": "int8",
          "description": "Automated concision score, 1–5, higher is better."
        },
        "hallucination": {
          "type": "int8",
          "description": "Automated binary flag for findings unsupported by the transcript."
        },
        "overall_quality": {
          "type": "int8",
          "description": "Automated overall quality score, 1–5, higher is better."
        },
        "contamination_v2_gpt": {
          "type": "int8",
          "description": "Legacy single-note field retained from frozen schema; not the matched control or primary endpoint."
        },
        "severity_v2": {
          "type": "int8",
          "description": "Legacy severity field retained from frozen schema; not the primary endpoint."
        }
      }
    },
    "attribution": {
      "rows": 3921,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "large_string",
          "description": "Original corpus and split identifier."
        },
        "item_id": {
          "type": "large_string",
          "description": "Encounter identifier within source_dataset."
        },
        "family": {
          "type": "large_string",
          "description": "Source corpus family."
        },
        "model": {
          "type": "large_string",
          "description": "Note-generation model identifier used in saved experiment."
        },
        "distractor_type": {
          "type": "large_string",
          "description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
        },
        "condition": {
          "type": "large_string",
          "description": "clean or distracted."
        },
        "attribution": {
          "type": "large_string",
          "description": "Automated classification of patient versus third-party attribution."
        },
        "used_for_patient": {
          "type": "double",
          "description": "Binary automated flag for use of the target in patient care."
        },
        "judge_reasoning": {
          "type": "large_string",
          "description": "Model-generated scoring explanation."
        },
        "parse_error": {
          "type": "bool",
          "description": "True if automated response could not be parsed; do not convert missing values to zero."
        },
        "judge_model": {
          "type": "large_string",
          "description": "Judge model identifier."
        },
        "judge_protocol": {
          "type": "large_string",
          "description": "Scoring protocol identifier."
        }
      }
    },
    "single_note_judgments": {
      "rows": 18432,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "large_string",
          "description": "Original corpus and split identifier."
        },
        "item_id": {
          "type": "large_string",
          "description": "Encounter identifier within source_dataset."
        },
        "model": {
          "type": "large_string",
          "description": "Note-generation model identifier used in saved experiment."
        },
        "distractor_type": {
          "type": "large_string",
          "description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
        },
        "condition": {
          "type": "large_string",
          "description": "clean or distracted."
        },
        "contamination": {
          "type": "int64",
          "description": "Binary target-contamination flag."
        },
        "severity": {
          "type": "int64",
          "description": "Target-contamination severity, 0–3."
        },
        "judge_reasoning": {
          "type": "large_string",
          "description": "Model-generated scoring explanation."
        },
        "parse_error": {
          "type": "bool",
          "description": "True if automated response could not be parsed; do not convert missing values to zero."
        },
        "parse_repair": {
          "type": "large_string",
          "description": "Recorded formatting repair applied while parsing."
        },
        "judge_model": {
          "type": "large_string",
          "description": "Judge model identifier."
        },
        "judge_provider": {
          "type": "large_string",
          "description": "Judge model provider."
        },
        "judge_protocol": {
          "type": "large_string",
          "description": "Scoring protocol identifier."
        }
      }
    },
    "failure_modes": {
      "rows": 9216,
      "split": "test",
      "columns": {
        "source_dataset": {
          "type": "string",
          "description": "Original corpus and split identifier."
        },
        "item_id": {
          "type": "string",
          "description": "Encounter identifier within source_dataset."
        },
        "model": {
          "type": "string",
          "description": "Note-generation model identifier used in saved experiment."
        },
        "distractor_type": {
          "type": "string",
          "description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
        },
        "model_short": {
          "type": "string",
          "description": "Display label for the note model."
        },
        "outcome": {
          "type": "string",
          "description": "Frozen distracted-note outcome classification."
        },
        "failure_mode": {
          "type": "string",
          "description": "Frozen attribution/clinical-use failure classification."
        },
        "severity_v3": {
          "type": "int64",
          "description": "Primary paired-judge severity 0–3 (absent to clinically consequential incorporation)."
        },
        "aside_class": {
          "type": "string",
          "description": "Assigned aside category."
        },
        "body_system": {
          "type": "string",
          "description": "Organ system label for the underlying encounter."
        },
        "topic": {
          "type": "string",
          "description": "Aside topic."
        },
        "in_assessment_or_plan": {
          "type": "bool",
          "description": "Target appears in assessment or plan, from frozen location annotations."
        },
        "action_in_plan": {
          "type": "bool",
          "description": "Plan contains a target-related clinical action, from frozen annotations."
        },
        "sections": {
          "type": "string",
          "description": "Note sections containing target content."
        }
      }
    }
  }
}