{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009977373,0.0006364941,0.000799978,0.002481474,0.0003467435,0.001564818,0.001137174,0.0007233997,0.001032469],"category_scores_gemma":[0.004044087,0.0004131353,0.001103322,0.001589402,0.001185737,0.002698263,0.00082225,0.001156565,0.0005962731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009763397,"about_ca_system_score_gemma":0.0008087053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002252206,"about_ca_topic_score_gemma":0.001224296,"domain_scores_codex":[0.9992263,0.000180077,0.00003594454,0.0001954872,0.000303613,0.00005844152],"domain_scores_gemma":[0.9984425,0.0008631878,0.0001651802,0.0002027825,0.0002845632,0.00004177676],"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":[0.0001068507,0.00009821359,0.00179589,0.000218681,0.0001262982,0.0001997072,0.00027113,0.3908769,0.04194761,0.273696,0.002770608,0.2878921],"study_design_scores_gemma":[0.000002416073,0.0000158837,0.0003059619,0.000008525334,0.00001004361,0.00004827914,0.0000187701,0.9601949,0.003853775,0.03445078,0.001078333,0.00001236713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003221083,0.00008708585,0.9960877,0.00004426254,0.000008139957,0.0000130513,0.0000259083,0.0001323807,0.0003804294],"genre_scores_gemma":[0.2731919,0.001570459,0.7194902,0.0001302574,0.0002245099,0.0002148636,0.000581433,0.0004226767,0.004173681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002481474,"threshold_uncertainty_score":0.007083893,"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."}}