{"id":"W2496224292","doi":"","title":"Extracting Keyphrases from Spoken Audio Documents","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Utterance; Natural language processing; Speech recognition; Artificial intelligence; Robustness (evolution); Transcription (linguistics); Redundancy (engineering); Word error rate; Linguistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006394193,0.0007430059,0.0005929422,0.002263149,0.000576055,0.001461737,0.0005091119,0.0007431719,0.003783976],"category_scores_gemma":[0.005771988,0.0002976186,0.000400216,0.001234621,0.0005091415,0.001816959,0.0007737657,0.0006677121,0.003808105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004226658,"about_ca_system_score_gemma":0.0007140273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001099734,"about_ca_topic_score_gemma":0.001336424,"domain_scores_codex":[0.999216,0.0001210481,0.0001147225,0.0002111641,0.0002617417,0.00007536738],"domain_scores_gemma":[0.9947745,0.002524044,0.0006640737,0.0006528081,0.001279632,0.0001049511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005232934,0.00005826566,0.002638539,0.001530839,0.00003889363,0.001252198,0.002167421,0.001296227,0.4170219,0.00401497,0.004560648,0.5648967],"study_design_scores_gemma":[0.000111651,0.0004772168,0.02503082,0.0002676541,0.0001818241,0.0038848,0.005838294,0.04540453,0.7947398,0.009662841,0.114191,0.0002096152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3691558,0.002737488,0.5980484,0.0007039353,0.000404828,0.0006184413,0.007768241,0.009599466,0.01096336],"genre_scores_gemma":[0.4339137,0.00151283,0.5482948,0.00009880427,0.0001624099,0.0002244897,0.007747427,0.0006664488,0.007379055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003783976,"threshold_uncertainty_score":0.01265866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04872146604949829,"score_gpt":0.2411007861840895,"score_spread":0.1923793201345912,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}