{"id":"W3186202858","doi":"10.1109/access.2021.3096776","title":"Person Identification From Audio Aesthetic","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Music and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Science and Engineering Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Categorization; Identification (biology); Computer science; Set (abstract data type); Preference; Artificial intelligence; Speech recognition; Human–computer interaction; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008817071,0.00007425874,0.00008964279,0.00003277767,0.000128591,0.0008781262,0.0008271007,0.00003710181,0.00006684944],"category_scores_gemma":[0.00001543888,0.00007309666,0.00003829635,0.0003256157,0.00002018491,0.00105189,0.00008967314,0.00007486395,0.0001401341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002203462,"about_ca_system_score_gemma":0.00008728824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005102204,"about_ca_topic_score_gemma":0.00001100698,"domain_scores_codex":[0.9991456,0.00003610309,0.00012243,0.0003537822,0.0001981977,0.0001438367],"domain_scores_gemma":[0.9993058,0.00002091529,0.00008257917,0.0004496538,0.00009331031,0.00004778229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000386912,0.0001504854,0.003985032,0.0000470637,0.00002518785,0.000192419,0.004014341,0.0002340135,0.06248716,0.004098239,0.03930551,0.8854567],"study_design_scores_gemma":[0.0008824514,0.00002207582,0.05893856,0.000227827,0.00004304209,0.0001079156,0.000213143,0.05370993,0.8254731,0.03347027,0.02597735,0.000934291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3016228,0.0004249409,0.6909267,0.003599896,0.001013036,0.00003511419,0.000001551678,0.0001149199,0.002261009],"genre_scores_gemma":[0.9941019,0.00001358177,0.00357413,0.001404134,0.0001341937,0.000006993461,0.000004742755,0.000006058029,0.0007542677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8845224,"threshold_uncertainty_score":0.8467792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05110715317271566,"score_gpt":0.2948192681841568,"score_spread":0.2437121150114411,"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."}}