{"id":"W2398609465","doi":"","title":"SFU at TRECVid 2010: Surveillance Event Detection.","year":2010,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; AdaBoost; Computer science; Computer vision; Background subtraction; Pattern recognition (psychology); Representation (politics); Object detection; Classifier (UML); Focus (optics); Context (archaeology); Event (particle physics); Subtraction; Mathematics; Geography; Physics; Optics","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":[0.006841613,0.00587795,0.003815239,0.006616935,0.002599091,0.00316284,0.004177175,0.003885106,0.02104477],"category_scores_gemma":[0.01231147,0.000900429,0.001305554,0.004278026,0.0008481996,0.003600867,0.00296098,0.003501229,0.03485557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003726068,"about_ca_system_score_gemma":0.002919734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06220386,"about_ca_topic_score_gemma":0.1026957,"domain_scores_codex":[0.9903541,0.002059753,0.0005241708,0.001854706,0.003997979,0.001209309],"domain_scores_gemma":[0.9919843,0.0008766256,0.0002817318,0.001505647,0.004234224,0.001117579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002631762,0.0002198462,0.0006252988,0.00027881,0.00006343471,0.0001096262,0.00003741912,0.0005650154,0.00492496,0.0003425128,0.925765,0.0668049],"study_design_scores_gemma":[0.0005355389,0.00129057,0.02384773,0.0003785981,0.0002177461,0.002632841,0.0004414659,0.09143176,0.04934826,0.004907277,0.8245983,0.0003699217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.05136804,0.01483774,0.1207534,0.006881157,0.0139992,0.007198648,0.5390022,0.1894173,0.05654235],"genre_scores_gemma":[0.0403185,0.001361679,0.08016995,0.0009556721,0.001025892,0.001703219,0.8434063,0.003338072,0.02772061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06220386,"threshold_uncertainty_score":0.1236836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009528913055639817,"score_gpt":0.2254158341934461,"score_spread":0.2158869211378063,"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."}}