{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005982952,0.0002962041,0.0005594934,0.0002107973,0.0001943851,0.0002622736,0.00008757622,0.0001585175,0.00005346861],"category_scores_gemma":[0.00001463896,0.0003890761,0.00009502234,0.0004291895,0.00003915562,0.0003897975,0.000006665809,0.0001645851,0.000007078652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006051298,"about_ca_system_score_gemma":0.00002762054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00198563,"about_ca_topic_score_gemma":0.006528038,"domain_scores_codex":[0.997555,0.00009107612,0.0008825167,0.000496685,0.0005582935,0.0004164205],"domain_scores_gemma":[0.9984334,0.0000606216,0.0002746544,0.0001752316,0.0008348909,0.0002212173],"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":[0.000168217,0.000435723,0.0009889737,0.002742236,0.0002924257,0.00001523376,0.0009539186,0.003726435,0.0122265,0.00004665073,0.0001815084,0.9782222],"study_design_scores_gemma":[0.002181064,0.0009483065,0.02992269,0.002664519,0.0008715364,0.0000935373,0.0006389602,0.9508843,0.009297291,0.0001366294,0.001460208,0.0009009981],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938409,0.00129958,0.0001941557,0.00008997557,0.0003636621,0.002495818,0.0002446119,0.0008049891,0.0006662956],"genre_scores_gemma":[0.9983743,0.0004042405,0.0001652553,0.0001047656,0.0001071914,0.0003431527,0.0003420119,0.00005377775,0.0001052825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9773212,"threshold_uncertainty_score":0.9998561,"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."}}