{"id":"W2799367230","doi":"10.1121/1.5035697","title":"Human dissimilarity ratings of musical instrument timbre: A computational meta-analysis","year":2018,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Music and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Timbre; Perception; Computer science; Set (abstract data type); Speech recognition; Waveform; Spectrogram; Correlation; Modulation (music); Pattern recognition (psychology); Mathematics; Artificial intelligence; Musical; Acoustics; Psychology; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015625,0.0001205815,0.0006323582,0.00002737687,0.0003175324,0.00003689033,0.001307043,0.00004194968,0.0001278751],"category_scores_gemma":[0.00009980905,0.0000575531,0.001318118,0.0007812203,0.001150732,0.0001466843,0.0004265521,0.0002947226,9.871608e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002816942,"about_ca_system_score_gemma":0.0001176646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002807317,"about_ca_topic_score_gemma":5.017724e-7,"domain_scores_codex":[0.9980599,0.0002005627,0.0006230141,0.000121098,0.0008255096,0.0001698997],"domain_scores_gemma":[0.9977494,0.0003415396,0.001058381,0.0003550725,0.0004217156,0.00007391911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002559648,0.003795777,0.001144343,0.0004490435,0.2263758,0.00000687295,0.04516869,0.3431453,0.05675267,0.004380054,0.2320744,0.08645106],"study_design_scores_gemma":[0.0003314222,0.0004172426,0.004487739,0.00002108016,0.02982854,0.00002785508,0.000405637,0.9506676,0.001497729,0.0117099,0.0004458342,0.00015949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01471238,0.00007166002,0.9709651,0.01399362,0.00005184492,0.00005290014,0.000004219201,0.000006510637,0.000141707],"genre_scores_gemma":[0.7819795,0.000003467158,0.2150542,0.002846178,0.00008351493,3.744688e-7,1.749526e-7,0.000003479708,0.00002914552],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7672671,"threshold_uncertainty_score":0.423992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04623347576107491,"score_gpt":0.2997786854765854,"score_spread":0.2535452097155105,"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."}}