{"id":"W2521372618","doi":"10.1007/978-3-319-46282-0_19","title":"Information Rate for Fast Time-Domain Instrument Classification","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Pattern recognition (psychology); Entropy (arrow of time); Artificial intelligence; Feature (linguistics); Frequency domain; Feature vector; Feature extraction; Frame (networking); Time domain; Domain (mathematical analysis); Computer vision; Mathematics","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"],"consensus_categories":[],"category_scores_codex":[0.001027629,0.0003514211,0.0003169022,0.000555353,0.0003151603,0.0007346149,0.001861769,0.0002206667,0.00001855828],"category_scores_gemma":[0.0000643791,0.0002761472,0.00009109044,0.0002891516,0.0003385493,0.001988313,0.0005166858,0.0002469205,0.0001501971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003183623,"about_ca_system_score_gemma":0.0005450337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001064479,"about_ca_topic_score_gemma":0.000002117396,"domain_scores_codex":[0.9977675,0.00002139628,0.0005205461,0.0006666511,0.0005464288,0.0004774903],"domain_scores_gemma":[0.9980952,0.0002569187,0.0004686553,0.000785367,0.0002815856,0.0001122933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004834434,0.000005857343,0.000002988229,0.0000369982,0.000003962657,9.834089e-7,0.0005254543,0.0004442816,0.0004871261,0.04088811,0.00009630738,0.9575031],"study_design_scores_gemma":[0.0006979316,0.0001716571,0.000100419,0.0007622311,0.000006927426,0.00002324136,4.110909e-7,0.4861613,0.002771504,0.4691766,0.0392928,0.0008350192],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003700211,0.00004025644,0.9870503,0.003608703,0.001020541,0.0004990667,0.00001006912,0.0001273289,0.007606733],"genre_scores_gemma":[0.05328204,0.00002788266,0.9356926,0.008066691,0.0009783577,0.0000733351,0.00003275254,0.00004223915,0.001804114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9566681,"threshold_uncertainty_score":0.9999691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01783601617201227,"score_gpt":0.2320858803550677,"score_spread":0.2142498641830555,"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."}}