{"id":"W2921546228","doi":"10.3389/fnins.2019.00199","title":"The Music-In-Noise Task (MINT): A Tool for Dissecting Complex Auditory Perception","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; Concordia University; McGill University; Centre for Interdisciplinary Research in Music Media and Technology; Centre for Research on Brain Language and Music; International Laboratory for Brain, Music and Sound Research","funders":"Canadian Institutes of Health Research; China Scholarship Council; International Development Research Centre; Centre for Research on Brain, Language and Music","keywords":"Noise (video); Perception; Task (project management); Silence; Cognitive psychology; Computer science; Speech recognition; Cognition; Rhythm; Psychology; Artificial intelligence; Neuroscience; Acoustics","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":[0.0008273195,0.0001264084,0.0001512527,0.0001467188,0.0003599878,0.0001337104,0.0004585386,0.00005060266,0.000004854167],"category_scores_gemma":[0.00299586,0.00009691939,0.00006506488,0.0005558777,0.0003139563,0.0003482932,0.00007652383,0.000209739,0.00001188766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001368498,"about_ca_system_score_gemma":0.00006488709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001984919,"about_ca_topic_score_gemma":0.000008275264,"domain_scores_codex":[0.9980801,0.0001807692,0.000311109,0.0006437359,0.0003157084,0.0004685751],"domain_scores_gemma":[0.9989736,0.0005281013,0.00008969391,0.0003278466,0.00003222825,0.00004854868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003978326,0.00005018569,0.03911547,0.00002789217,1.204004e-7,0.000002104385,0.000807251,0.0007142411,0.9467715,0.0002379005,0.001357475,0.01087605],"study_design_scores_gemma":[0.0004365165,0.0001638704,0.9124551,0.00003586706,0.000001333967,0.00000373048,0.0005832257,0.07571049,0.001004667,0.0009479276,0.008478294,0.0001790092],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863397,0.00000677473,0.004011586,0.0003905015,0.008105204,0.0008795722,0.000005383594,0.00003818992,0.0002230747],"genre_scores_gemma":[0.9976121,0.000015691,0.001143981,0.0005326332,0.0001089146,0.0000841631,5.161266e-7,0.00001464611,0.000487329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9457669,"threshold_uncertainty_score":0.3952258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02794011747640385,"score_gpt":0.2760522250345679,"score_spread":0.248112107558164,"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."}}