{"id":"W2004953770","doi":"10.3758/bf03194747","title":"Deriving the loudness exponent from categorical judgments","year":2002,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Loudness; Exponent; Mathematics; Probability density function; Histogram; Power function; Range (aeronautics); Intensity (physics); Sound intensity; Categorical variable; Statistics; Mathematical analysis; Acoustics; Physics; Artificial intelligence; Sound (geography); Computer science; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002116606,0.0004220854,0.0005064763,0.001923484,0.0003055653,0.002234036,0.0008570326,0.0008089546,0.007777408],"category_scores_gemma":[0.05033989,0.0004765084,0.0003769529,0.0008366159,0.0006473341,0.004330945,0.001440096,0.001501615,0.002348191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000465359,"about_ca_system_score_gemma":0.0003050161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007457511,"about_ca_topic_score_gemma":0.001023974,"domain_scores_codex":[0.9988796,0.0003460967,0.00008062908,0.0002559676,0.0003513408,0.00008632149],"domain_scores_gemma":[0.981807,0.01437679,0.0007292249,0.001386232,0.001411457,0.000289254],"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":[0.001082178,0.0003104321,0.06423102,0.000591038,0.0002199623,0.0008106655,0.001621267,0.01906565,0.1230937,0.1637619,0.004175502,0.6210366],"study_design_scores_gemma":[0.0001159477,0.0001886103,0.08405477,0.0001162404,0.00008230184,0.0007209708,0.0006224505,0.6145547,0.02374634,0.2730466,0.002625653,0.000125469],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3875472,0.0005230254,0.5872119,0.0003869091,0.000141057,0.00009099494,0.0004353298,0.001103771,0.02255988],"genre_scores_gemma":[0.9504412,0.0001176213,0.04793279,0.00006195706,0.00006786454,0.00003415358,0.0002549083,0.0001394136,0.0009501677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007777408,"threshold_uncertainty_score":0.02601808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03720806478846768,"score_gpt":0.233239637734548,"score_spread":0.1960315729460803,"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."}}