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"missing_values": "Retained from frozen inputs; missing does not mean negative or zero.",
"configs": {
"pairs": {
"rows": 456,
"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."
},
"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."
},
"donor": {
"type": "int64",
"description": "Seeded donor ordinal for a foreground consultation."
},
"donor_id": {
"type": "large_string",
"description": "Identifier of the background consultation."
},
"level_db": {
"type": "double",
"description": "Background RMS relative to foreground RMS, in dB."
},
"onset": {
"type": "double",
"description": "Overlay onset in foreground audio, seconds."
},
"segment_start": {
"type": "double",
"description": "Donor segment start, seconds in original donor recording."
},
"segment_end": {
"type": "double",
"description": "Donor segment end, seconds in original donor recording."
},
"segment_seconds": {
"type": "double",
"description": "Duration of donor segment, seconds."
},
"segment_score": {
"type": "int64",
"description": "Medical-keyword score used for donor-window selection."
},
"wav": {
"type": "large_string",
"description": "Historical relative WAV path; use recordings configuration for embedded audio."
},
"human_transcript": {
"type": "large_string",
"description": "Original human reference transcript for foreground consultation."
}
}
},
"transcripts": {
"rows": 513,
"split": "test",
"columns": {
"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."
},
"condition": {
"type": "large_string",
"description": "clean or distracted."
},
"wav": {
"type": "large_string",
"description": "Historical relative WAV path; use recordings configuration for embedded audio."
},
"text": {
"type": "large_string",
"description": "Saved ASR transcript."
},
"segments": {
"type": "large_string",
"description": "Serialized ASR chunk texts and time spans."
},
"asr_model": {
"type": "large_string",
"description": "ASR model identifier."
},
"timestamps": {
"type": "large_string",
"description": "Serialized chunk timestamp values."
},
"seconds": {
"type": "double",
"description": "Historical ASR processing time in seconds, not audio duration."
}
}
},
"notes": {
"rows": 3648,
"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."
},
"condition": {
"type": "large_string",
"description": "clean or distracted."
},
"transcript": {
"type": "large_string",
"description": "Input transcript supplied to note generator."
},
"reference_note": {
"type": "large_string",
"description": "Reference clinical note supplied by original source corpus."
},
"distractor_summary": {
"type": "large_string",
"description": "Target content supplied to the contamination judge."
},
"note": {
"type": "large_string",
"description": "Generated clinical note; empty saved records are retained."
},
"model": {
"type": "large_string",
"description": "Note-generation model identifier used in saved experiment."
}
}
},
"judgments": {
"rows": 3648,
"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": {
"type": "double",
"description": "Binary target-contamination flag."
},
"severity": {
"type": "double",
"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."
},
"judge_model": {
"type": "large_string",
"description": "Judge model identifier."
},
"judge_protocol": {
"type": "large_string",
"description": "Scoring protocol identifier."
},
"clinical_correctness": {
"type": "double",
"description": "Automated quality score, 1–5, higher is better."
},
"completeness": {
"type": "double",
"description": "Automated completeness score, 1–5, higher is better."
},
"succinctness": {
"type": "double",
"description": "Automated concision score, 1–5, higher is better."
},
"hallucination": {
"type": "double",
"description": "Automated binary flag for findings unsupported by the transcript."
},
"overall_quality": {
"type": "double",
"description": "Automated overall quality score, 1–5, higher is better."
}
}
},
"leakage": {
"rows": 456,
"split": "test",
"columns": {
"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."
},
"donor": {
"type": "int64",
"description": "Seeded donor ordinal for a foreground consultation."
},
"donor_id": {
"type": "large_string",
"description": "Identifier of the background consultation."
},
"level_db": {
"type": "double",
"description": "Background RMS relative to foreground RMS, in dB."
},
"onset": {
"type": "double",
"description": "Overlay onset in foreground audio, seconds."
},
"segment_start": {
"type": "double",
"description": "Donor segment start, seconds in original donor recording."
},
"segment_end": {
"type": "double",
"description": "Donor segment end, seconds in original donor recording."
},
"segment_seconds": {
"type": "double",
"description": "Duration of donor segment, seconds."
},
"segment_score": {
"type": "int64",
"description": "Medical-keyword score used for donor-window selection."
},
"n_content": {
"type": "int64",
"description": "Number of donor segment content words considered."
},
"n_leaked": {
"type": "int64",
"description": "Number of donor content words in mixed ASR but absent from clean ASR."
},
"leakage": {
"type": "double",
"description": "Fraction n_leaked/n_content."
},
"overlap_clean": {
"type": "double",
"description": "Fraction of donor content words already present in clean ASR."
},
"leaked_words": {
"type": "large_string",
"description": "Donor content words newly present in mixed ASR."
},
"wer_vs_clean": {
"type": "double",
"description": "Mixed-ASR word error rate relative to clean-ASR transcript."
},
"wer_clean_vs_human": {
"type": "double",
"description": "Clean-ASR word error rate relative to human reference."
}
}
},
"recordings": {
"rows": 513,
"split": "test",
"columns": {
"recording_id": {
"type": "string",
"description": "Unique recording key: item_id plus distractor_type."
},
"item_id": {
"type": "string",
"description": "Encounter identifier within source_dataset."
},
"distractor_type": {
"type": "string",
"description": "Perturbation family or audio donor/level identifier; clean for unmodified audio."
},
"condition": {
"type": "string",
"description": "clean or distracted."
},
"donor_id": {
"type": "string",
"description": "Identifier of the background consultation."
},
"level_db": {
"type": "double",
"description": "Background RMS relative to foreground RMS, in dB."
},
"onset": {
"type": "double",
"description": "Overlay onset in foreground audio, seconds."
},
"segment_start": {
"type": "double",
"description": "Donor segment start, seconds in original donor recording."
},
"segment_end": {
"type": "double",
"description": "Donor segment end, seconds in original donor recording."
},
"duration_seconds": {
"type": "double",
"description": "Waveform duration in seconds."
},
"sampling_rate": {
"type": "int32",
"description": "Samples per second; 16000."
},
"pcm_sha256": {
"type": "string",
"description": "SHA256 of little-endian signed 16-bit reconstructed PCM samples."
},
"audio": {
"type": "struct<bytes: binary, path: string>",
"description": "Embedded FLAC bytes and filename with Hugging Face Audio metadata."
}
}
}
}
}
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