{"id":"W4255494904","doi":"10.1121/1.5031018.6","title":"10.1121/1.5031018.6","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":[0.0002300128,0.0006454841,0.0006026176,0.0002878055,0.0002672828,0.002720644,0.003004441,0.0004390807,0.0008687142],"category_scores_gemma":[0.0002051963,0.00057491,0.0002819857,0.0007152429,0.0002127181,0.001976984,0.001307551,0.0004667251,0.01188194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009718296,"about_ca_system_score_gemma":0.0002132556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003991988,"about_ca_topic_score_gemma":0.00002135894,"domain_scores_codex":[0.996573,0.00004024873,0.000556002,0.001215051,0.0008232213,0.0007925092],"domain_scores_gemma":[0.9972603,0.0001384362,0.0004055783,0.001774713,0.0001584023,0.0002625124],"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":[0.00000850937,0.00009380989,0.000001131295,0.0001036258,0.00003869076,0.00009596414,0.00002605668,3.77964e-7,0.000001973874,0.00005656704,0.9729375,0.02663577],"study_design_scores_gemma":[0.0002549834,0.0002440811,0.00001123141,0.0001759538,0.00002108219,0.00008859781,0.000003858705,0.0001467172,0.00002708192,0.0007390035,0.9975524,0.0007349961],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000173789,0.0002801573,0.009374193,0.0001189522,0.001218242,0.0002325233,0.9712385,0.0003964978,0.01712356],"genre_scores_gemma":[0.000250724,0.00001808828,0.001055476,0.001385844,0.001339209,0.00003528963,0.9905117,0.00004334916,0.005360345],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02590078,"threshold_uncertainty_score":0.9996702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136881002721798,"score_gpt":0.2396103888032876,"score_spread":0.2282415787760697,"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."}}