{"id":"W2001872373","doi":"10.1109/icip.2006.312969","title":"Video Event Detection using ICA Mixture Hidden Markov Models","year":2006,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Hidden Markov model; Independent component analysis; Pattern recognition (psychology); Event (particle physics); Feature extraction; Feature (linguistics); Mixture model","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002265407,0.00009868724,0.00008787574,0.0001107619,0.00009603769,0.0001572935,0.0003203311,0.00008643411,0.00001663901],"category_scores_gemma":[0.000005019795,0.00008938535,0.00005674855,0.0002715959,0.00001340041,0.0007042209,0.0001101051,0.000102127,0.00001262217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006528745,"about_ca_system_score_gemma":0.00003408104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000338941,"about_ca_topic_score_gemma":0.00009423326,"domain_scores_codex":[0.9991033,0.00006825896,0.0001872558,0.0002611307,0.000221883,0.0001581451],"domain_scores_gemma":[0.9994585,0.00002341655,0.00006137292,0.0003510994,0.00007035692,0.00003524622],"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":[0.00002031754,0.0002893507,0.0001902926,0.00002517389,0.00002911036,0.00001843901,0.0009627486,0.01691807,0.09766281,0.5854309,0.009742261,0.2887105],"study_design_scores_gemma":[0.0000866402,0.00002527383,0.0001953828,0.000006746024,0.000002888791,0.00001814634,0.000005202091,0.8136511,0.07620054,0.1085004,0.001165333,0.0001422585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03106275,0.00003365994,0.9567316,0.0004754419,0.00008477375,0.0001374935,3.594535e-7,0.0006216111,0.01085231],"genre_scores_gemma":[0.736964,0.000001606181,0.2619595,0.0003330381,0.0000391279,0.000007106296,8.166472e-7,0.000006157346,0.0006886734],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7967331,"threshold_uncertainty_score":0.3645029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537917107095207,"score_gpt":0.2528003813390551,"score_spread":0.2374212102681031,"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."}}