{"id":"W97565026","doi":"","title":"Evaluation of automated license plate recognition for improving British Columbia's Green Light program","year":2009,"lang":"en","type":"article","venue":"16th ITS World Congress and Exhibition on Intelligent Transport Systems and ServicesITS AmericaERTICOITS Japan","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; Process (computing); Business; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00196077,0.0005739833,0.0003638234,0.001175551,0.0007937347,0.001190679,0.001004619,0.0004821504,0.003474114],"category_scores_gemma":[0.006869488,0.0002374161,0.0002255243,0.0008188441,0.0003391154,0.0004877088,0.0004501815,0.0003929211,0.0008686102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005978134,"about_ca_system_score_gemma":0.005810859,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7595828,"about_ca_topic_score_gemma":0.8319759,"domain_scores_codex":[0.997775,0.000506365,0.00007343433,0.0002863747,0.001177845,0.0001808861],"domain_scores_gemma":[0.9933316,0.001092789,0.0002074304,0.0001945535,0.004812112,0.0003614271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005624957,0.005784881,0.1593588,0.000834166,0.0002528862,0.0004358865,0.001312774,0.032839,0.04569636,0.0009574448,0.02424433,0.7226585],"study_design_scores_gemma":[0.0007725869,0.006773405,0.6116655,0.0002026604,0.0005196281,0.0001869457,0.003362436,0.2894609,0.06022113,0.0001890066,0.02644757,0.0001982049],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731548,0.0002949012,0.003238617,0.0003026804,0.00004746487,0.0008184142,0.001291343,0.0008047594,0.0200471],"genre_scores_gemma":[0.9774495,0.0002057972,0.008256745,0.0001076923,0.000008315992,0.0001623503,0.002157784,0.00004569571,0.01160611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2404172,"threshold_uncertainty_score":0.4836661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399594845377402,"score_gpt":0.2481012465485855,"score_spread":0.2241052980948115,"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."}}