{"id":"W4392985400","doi":"10.32920/25413001","title":"Modeling Music Emotion Judgments Using Machine Learning Methods","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Psychology; Cognitive psychology; Emotion detection; Machine learning; Emotion recognition","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001219243,0.0003553945,0.0003737121,0.0003055192,0.0002497681,0.001018284,0.0008652055,0.0002462982,0.00007323348],"category_scores_gemma":[0.00004967141,0.0003193919,0.0001818796,0.0003501188,0.00001865595,0.0002580598,0.005236843,0.001454168,0.00004268602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001538523,"about_ca_system_score_gemma":0.0002309814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003918808,"about_ca_topic_score_gemma":0.000006402306,"domain_scores_codex":[0.9975412,0.0002319047,0.0004502669,0.001038251,0.0003666682,0.0003717268],"domain_scores_gemma":[0.9990657,0.0000362624,0.0001484383,0.0005559528,0.00009565393,0.00009797328],"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":[0.000001248975,0.00002093159,0.00001621455,0.000529395,0.00006146892,0.00001636178,0.0013594,0.7499893,0.002799373,0.002641183,0.00005561653,0.2425095],"study_design_scores_gemma":[0.00006378605,0.000007545225,9.345304e-7,0.0005336949,0.0000391742,0.00001489762,0.00002304188,0.9569936,0.001135811,0.04065339,0.0001996183,0.0003344654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01912189,0.001692077,0.9712492,0.0003844143,0.0023784,0.0001519846,9.662767e-7,0.0007371925,0.004283897],"genre_scores_gemma":[0.2139574,0.00002633512,0.7843266,0.0004480747,0.0002652654,0.00000665767,0.000008952718,0.00003570887,0.0009251116],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.242175,"threshold_uncertainty_score":0.9999258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1221948991033433,"score_gpt":0.3678108533166406,"score_spread":0.2456159542132973,"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."}}