{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006458656,0.00009569307,0.0001142991,0.0000361725,0.0003112384,0.0002421379,0.0001104362,0.00002397832,0.1237343],"category_scores_gemma":[0.0000333917,0.00008557505,0.00005348788,0.00001761467,0.00007260466,0.0002767045,0.00003461589,0.0001017677,0.001500614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002589328,"about_ca_system_score_gemma":0.000004923364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004503437,"about_ca_topic_score_gemma":0.001632603,"domain_scores_codex":[0.9993078,0.00003017337,0.0001530965,0.000161643,0.0001653546,0.0001820088],"domain_scores_gemma":[0.999635,0.00006393536,0.00006088757,0.0001400755,0.00004308073,0.00005699872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003211847,0.0007954114,0.01798233,0.0000383156,0.0003316473,0.0001526082,0.04527723,0.000004312364,0.0273559,0.1959609,0.6254349,0.08663432],"study_design_scores_gemma":[0.0003270102,0.00003804881,0.001992239,0.00002882987,0.00002558672,7.762305e-7,0.0008250302,0.0002596281,0.001321539,0.02209209,0.9728757,0.0002135977],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5252017,0.00005578126,0.000002077251,0.0003095774,0.0003659178,0.00004897115,0.00002842022,0.00003930125,0.4739482],"genre_scores_gemma":[0.9516279,0.00004578959,0.00006992555,0.0002407159,0.0007381429,0.000006812442,0.000007893914,0.00001169931,0.04725114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4266971,"threshold_uncertainty_score":0.9992768,"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."}}