{"id":"W4220735002","doi":"10.18280/isi.270109","title":"Automatic Traffic Red-Light Violation Detection Using AI","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Traffic signal; Computer science; SIGNAL (programming language); Red light; Identification (biology); Artificial intelligence; Detection theory; Traffic system; Real-time computing; Transport engineering; Engineering; Telecommunications; Detector","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000405481,0.0007579571,0.0006167311,0.001628508,0.00035799,0.0009848389,0.001108415,0.0004737173,0.002455518],"category_scores_gemma":[0.0009873102,0.0002394858,0.0005005928,0.0007594166,0.0002490102,0.0007011012,0.0006011431,0.0008350994,0.001284826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005284436,"about_ca_system_score_gemma":0.0005522075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008529063,"about_ca_topic_score_gemma":0.007696609,"domain_scores_codex":[0.9994918,0.0000686361,0.0000175795,0.0001669766,0.0001710058,0.00008392517],"domain_scores_gemma":[0.9995492,0.00007912333,0.00005628338,0.00006790237,0.0002118096,0.00003567327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007245669,0.0005969594,0.01430106,0.0002403353,0.000154987,0.0002758016,0.00009205842,0.1450726,0.0806354,0.00328024,0.01356847,0.7410575],"study_design_scores_gemma":[0.000007308038,0.00005997311,0.00244231,0.000007463991,0.00001441687,0.00004775494,0.00002231313,0.9830906,0.01217226,0.0006303123,0.001490359,0.00001492622],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1862716,0.0009763321,0.7748489,0.0004181301,0.0004761397,0.000252403,0.00105535,0.02066529,0.01503585],"genre_scores_gemma":[0.8588418,0.0003380297,0.1305685,0.0001752401,0.0001047421,0.00009292139,0.002457532,0.0002197953,0.007201455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008529063,"threshold_uncertainty_score":0.01695883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01046346193034686,"score_gpt":0.2054670712684698,"score_spread":0.195003609338123,"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."}}