{"id":"W2773275010","doi":"","title":"A Biological Approach to Speech Spectrography","year":2017,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Speech recognition","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000279429,0.0001445593,0.0001751479,0.000143759,0.0004397113,0.0009816226,0.001715712,0.00008091662,0.0000221302],"category_scores_gemma":[0.0002704998,0.0001145934,0.0001102535,0.0002030981,0.00009095492,0.0004934133,0.0003888012,0.000112571,0.0003005839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001709175,"about_ca_system_score_gemma":0.00001184514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000858452,"about_ca_topic_score_gemma":4.877564e-7,"domain_scores_codex":[0.9988064,0.000003678446,0.0001395699,0.0005065538,0.0002095539,0.0003342733],"domain_scores_gemma":[0.9992223,0.00001702971,0.00008764865,0.0003484743,0.0001306685,0.0001938391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002830797,0.0004632606,0.02407198,0.0000365038,0.00005677033,0.00002146862,0.0008053299,5.573654e-8,0.01757205,0.4910557,0.02045486,0.4454337],"study_design_scores_gemma":[0.001823571,0.001109057,0.4053643,0.0001853857,0.00004342453,0.0009012392,0.001248162,0.007202188,0.289106,0.08099107,0.2091469,0.002878707],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3405985,0.0000271636,0.02762792,0.00214203,0.000191457,0.000330104,0.00000175918,0.0004821719,0.6285989],"genre_scores_gemma":[0.6933966,0.00001199019,0.3056531,0.0005486424,0.0001512146,0.00004137688,4.487841e-7,0.000006874041,0.0001898578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.628409,"threshold_uncertainty_score":0.9465809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0544217407407934,"score_gpt":0.2710896909148267,"score_spread":0.2166679501740333,"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."}}