{"id":"W4233976102","doi":"10.1121/1.5031018.11","title":"10.1121/1.5031018.11","year":2018,"lang":"en","type":"dataset","venue":"Default Digital Object Group","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Acoustics; Kullback–Leibler divergence; Entropy (arrow of time); Bandwidth (computing); Mathematics; Random noise; Inverse; Speech recognition; Computer science; Physics; Statistics; Telecommunications","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002481471,0.000694119,0.0006490158,0.0003076943,0.000286949,0.00279295,0.003146416,0.0004696258,0.0008565659],"category_scores_gemma":[0.0002130282,0.0006200799,0.0003034742,0.0007609823,0.0002303276,0.002085721,0.001383211,0.0004304658,0.009425011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001042707,"about_ca_system_score_gemma":0.0002279508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006917558,"about_ca_topic_score_gemma":0.00005754829,"domain_scores_codex":[0.9963362,0.00004438996,0.0005983216,0.001294006,0.000874249,0.0008528579],"domain_scores_gemma":[0.9970997,0.0001482735,0.0004387774,0.001859832,0.0001693579,0.0002840389],"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":[0.000007456521,0.0001039434,0.000002930931,0.0001144276,0.00003456855,0.0001070107,0.00002927646,4.396702e-7,0.000001985623,0.00007471733,0.9807788,0.01874442],"study_design_scores_gemma":[0.0002799811,0.0002189474,0.00001680843,0.0001930321,0.00002376574,0.0000950762,0.000004788883,0.0001541298,0.00002991798,0.0006356933,0.9975564,0.0007913831],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000379115,0.0002497703,0.009633872,0.000139636,0.001346498,0.0002603322,0.9761932,0.0004306634,0.01170813],"genre_scores_gemma":[0.0003657736,0.00001606001,0.0011165,0.001438107,0.001425425,0.00003921557,0.9900374,0.00004933075,0.005512193],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01795303,"threshold_uncertainty_score":0.999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01193047692232822,"score_gpt":0.2402809440675311,"score_spread":0.2283504671452029,"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."}}