{"id":"W4240718222","doi":"10.32920/ryerson.14646498","title":"Video content analysis based on statistical modeling","year":2021,"lang":"en","type":"preprint","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; Video content analysis; Video tracking; Artificial intelligence; Hidden Markov model; Segmentation; Statistical model; Parsing; Markov chain; Machine learning; Probabilistic logic; Independent component analysis; Fuzzy logic; Pattern recognition (psychology); Data mining; Object (grammar); Computer vision","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":[0.0006190073,0.0002136135,0.0004042478,0.0004164462,0.00005154259,0.0008195537,0.0009044112,0.0002014148,0.0001516885],"category_scores_gemma":[0.00008529595,0.000195685,0.0002149674,0.0004001083,0.00001665166,0.000171992,0.0005822603,0.000480686,0.000009571795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007548823,"about_ca_system_score_gemma":0.0002820107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004330707,"about_ca_topic_score_gemma":0.0001458603,"domain_scores_codex":[0.9976978,0.000315061,0.0003905981,0.0008815403,0.0005344464,0.0001805464],"domain_scores_gemma":[0.9977642,0.0001449943,0.00009089179,0.001559534,0.0003013676,0.0001389582],"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":[0.000003985008,0.0001408111,0.00005377732,0.0000103112,0.0001161515,0.00001644691,0.0002492802,0.9107277,0.00002103212,0.0861598,0.00008961598,0.002411099],"study_design_scores_gemma":[0.00006701364,0.00004513653,0.0001175629,0.00001940223,0.00007769264,2.803295e-7,0.00003000099,0.995871,0.0005153539,0.003014556,0.00001724681,0.0002247703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003133705,0.00001090696,0.9922349,0.0009216041,0.00009857523,0.0001702108,0.00001095397,0.0006531367,0.002765951],"genre_scores_gemma":[0.5430918,0.000002521273,0.4546552,0.002025669,0.0000126102,0.0000315448,0.0001182764,0.000006298194,0.00005604144],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5399581,"threshold_uncertainty_score":0.7979804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.098763645295514,"score_gpt":0.3247673535674779,"score_spread":0.2260037082719639,"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."}}