{"id":"W2914207527","doi":"10.1007/s11042-019-7275-3","title":"Bayesian frameworks for traffic scenes monitoring via view-based 3D cars models recognition","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Taif University","keywords":"Computer science; Machine learning; Markov chain Monte Carlo; Inference; Artificial intelligence; Flexibility (engineering); Bayesian inference; Reversible-jump Markov chain Monte Carlo; Bayesian probability; Dirichlet distribution; Dirichlet process; Data mining","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":[0.000314783,0.0001397102,0.0001859246,0.00006609518,0.0001696866,0.0001755296,0.0002741829,0.0001604927,0.000004644285],"category_scores_gemma":[0.00002333331,0.000136375,0.0000657321,0.0002345614,0.00002922604,0.0003715148,0.00003231708,0.0001760558,0.00002429279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001902955,"about_ca_system_score_gemma":0.00003558585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007750645,"about_ca_topic_score_gemma":0.000003057027,"domain_scores_codex":[0.9989504,0.000042702,0.0002127347,0.0004287198,0.0001269424,0.0002385019],"domain_scores_gemma":[0.9986901,0.0005995784,0.00008342649,0.0004190157,0.0001124507,0.0000954536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003376688,0.00004293236,0.0006143778,0.00003319034,0.000008274082,1.584865e-7,0.0001163169,0.007513329,0.0005865267,0.0001942589,0.000009901521,0.9908773],"study_design_scores_gemma":[0.0004600823,0.00003503664,0.001950241,0.00005315753,0.0000111237,0.000001604907,0.0000156149,0.9885585,0.001647591,0.004769797,0.002285991,0.0002112681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01184206,0.0002874229,0.9860513,0.0003172597,0.0002179406,0.0009643817,0.00003297583,0.0001641182,0.0001225828],"genre_scores_gemma":[0.5080369,0.00004798314,0.4908618,0.00009592838,0.0001603762,0.0007363951,0.00003616215,0.00001232807,0.00001217245],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9906661,"threshold_uncertainty_score":0.5561209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04677493950763927,"score_gpt":0.3007108778880702,"score_spread":0.2539359383804309,"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."}}