{"id":"W2168706009","doi":"","title":"\"Name That Song!\" A Probabilistic Approach to Querying on Music and Text","year":2002,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Music and Audio Processing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lyrics; Computer science; Probabilistic logic; Maximum a posteriori estimation; Music information retrieval; Information retrieval; Statistical model; Natural language processing; Modal; Topic model; A priori and a posteriori; Artificial intelligence; Speech recognition; Maximum likelihood; Mathematics; Musical; Statistics; Literature; Art","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":[0.003223405,0.001098993,0.001168819,0.003523896,0.00151916,0.003133462,0.003417047,0.002374415,0.006342464],"category_scores_gemma":[0.01556013,0.001060693,0.001826352,0.004627163,0.001980714,0.008174112,0.002843962,0.002376823,0.00277425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001161601,"about_ca_system_score_gemma":0.001490333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007346654,"about_ca_topic_score_gemma":0.01337631,"domain_scores_codex":[0.9962759,0.001313221,0.0002330601,0.0009162778,0.001105322,0.0001562556],"domain_scores_gemma":[0.9944885,0.003332488,0.0004088039,0.0009167376,0.0006687575,0.0001848021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005382276,0.0003342913,0.004740389,0.0006467052,0.0003048572,0.0004798758,0.00155782,0.0700077,0.0140145,0.469612,0.04533536,0.3924283],"study_design_scores_gemma":[0.0000434116,0.00008321965,0.001352067,0.00006240085,0.00008907729,0.0006789072,0.0001665046,0.7259629,0.003007673,0.2337347,0.03469723,0.0001218915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002669459,0.0003919852,0.9922449,0.00098102,0.00004931206,0.00006029051,0.0006520569,0.0009153177,0.00203558],"genre_scores_gemma":[0.1389129,0.0008956981,0.8446664,0.0009513657,0.0003723911,0.0003920007,0.002500071,0.0004357157,0.01087348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007346654,"threshold_uncertainty_score":0.0212177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05687555581886768,"score_gpt":0.2288296507336617,"score_spread":0.171954094914794,"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."}}