{"id":"W2292985255","doi":"10.5281/zenodo.1416109","title":"Fast Vs Slow: Learning Tempo Octaves From User Data.","year":2010,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Active listening; Task (project management); Speech recognition; Music information retrieval; Artificial intelligence; Machine learning; Simple (philosophy); Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002440433,0.001057822,0.0009758287,0.001506582,0.0005244464,0.001566887,0.001411885,0.001311743,0.008726926],"category_scores_gemma":[0.02284819,0.0004562835,0.0006327068,0.001580084,0.0003400594,0.002137829,0.001489391,0.001582919,0.004434533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002367036,"about_ca_system_score_gemma":0.0005174118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00503603,"about_ca_topic_score_gemma":0.0124456,"domain_scores_codex":[0.999172,0.0003361554,0.00004383773,0.0002096891,0.0001599264,0.00007835891],"domain_scores_gemma":[0.9945021,0.004113682,0.0001265886,0.0005765621,0.0003996427,0.0002814636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005830449,0.000423882,0.01310486,0.0003670022,0.0002793199,0.0001241594,0.000402921,0.005921178,0.009719463,0.0009752992,0.06032819,0.9025233],"study_design_scores_gemma":[0.001352712,0.002675661,0.07066185,0.0004214091,0.0009614264,0.0007450734,0.001851322,0.7935537,0.04307286,0.03097345,0.05323473,0.0004956868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4225618,0.007029908,0.4755599,0.003309536,0.003298196,0.001428735,0.02145777,0.03754372,0.02781037],"genre_scores_gemma":[0.6706719,0.002523928,0.2851702,0.001337788,0.0006701942,0.0008461685,0.01702997,0.002233023,0.01951675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008726926,"threshold_uncertainty_score":0.02919447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053431941398316,"score_gpt":0.2521769756842016,"score_spread":0.2316426562702185,"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."}}