{"id":"W2111904465","doi":"10.1109/iccvw.2009.5457653","title":"ICA mixture hidden conditional random field model for sports event classification","year":2009,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminative model; Conditional random field; Hidden Markov model; Independent component analysis; Artificial intelligence; Pattern recognition (psychology); Mixture model; Conditional independence; Computer science; Event (particle physics); Feature (linguistics); Gaussian; Speech recognition","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":[0.00163375,0.0008376688,0.0009754829,0.001107227,0.0003192855,0.0006349985,0.001638845,0.001005518,0.002266268],"category_scores_gemma":[0.002719015,0.0003697433,0.001353634,0.0009748641,0.0006038034,0.001476316,0.0003753456,0.001492299,0.001086794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008459557,"about_ca_system_score_gemma":0.0007352873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009936334,"about_ca_topic_score_gemma":0.005922299,"domain_scores_codex":[0.9992952,0.0002122139,0.00002595488,0.0002065919,0.000179952,0.00008000354],"domain_scores_gemma":[0.9990947,0.000516766,0.0000872848,0.0001048804,0.000166479,0.00002987919],"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":[0.0002575205,0.0001054199,0.00304738,0.0001243864,0.0001641184,0.0002040231,0.00009847666,0.7802846,0.005977362,0.0561919,0.005589598,0.1479552],"study_design_scores_gemma":[0.000003421224,0.000008840472,0.0003594287,0.000003557731,0.00001073943,0.00002526306,0.000002178931,0.9949462,0.0003071098,0.003810063,0.0005144869,0.000008754141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007494909,0.0004349773,0.9902452,0.0001519676,0.00006039234,0.0000197581,0.0001579437,0.0004818653,0.000952839],"genre_scores_gemma":[0.6905044,0.001473414,0.2943983,0.0002494218,0.0003533883,0.0002635342,0.00192928,0.000217239,0.01061099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009936334,"threshold_uncertainty_score":0.01975697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366048861264645,"score_gpt":0.276200142521454,"score_spread":0.2525396539088076,"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."}}