{"id":"W3211571646","doi":"10.1155/2021/3515512","title":"Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province","keywords":"Computer science; Artificial intelligence; Dictionary learning; Computer vision; Natural language processing; Pattern recognition (psychology); Sparse approximation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003453066,0.0001453978,0.0002451804,0.0001896178,0.0003216411,0.00007011037,0.0001650175,0.00006782339,0.000008554128],"category_scores_gemma":[0.00002907741,0.0001513507,0.0002131618,0.0005203312,0.00003071359,0.001451059,0.000008861281,0.0003298566,0.000001080892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002383017,"about_ca_system_score_gemma":0.0001211795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004144572,"about_ca_topic_score_gemma":0.0002318463,"domain_scores_codex":[0.998567,0.00008162219,0.0005703325,0.000244457,0.0003415653,0.000194966],"domain_scores_gemma":[0.9987099,0.000111679,0.0004782115,0.0001553605,0.0004579922,0.0000869003],"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":[0.00005852746,0.00007528137,0.0005689438,0.00001700252,0.00004425464,0.00002515159,0.001338143,0.842205,0.1427921,0.0002799829,0.000003614095,0.01259206],"study_design_scores_gemma":[0.002275273,0.0003314117,0.111475,0.0001543358,0.00009511261,0.0009143296,0.001230779,0.8730525,0.007439675,0.00136582,0.001287575,0.0003780987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4636962,0.00008729596,0.5356736,0.00004722768,0.0004098862,0.00003226874,0.000002032273,0.00003103713,0.00002054234],"genre_scores_gemma":[0.7418228,0.0000384079,0.2579341,0.00003838254,0.00009696841,0.00000164496,0.00001192152,0.00001298147,0.00004275901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2781266,"threshold_uncertainty_score":0.6171904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05919482883379214,"score_gpt":0.3122454324832336,"score_spread":0.2530506036494415,"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."}}