{"id":"W4392358221","doi":"10.18280/ria.380114","title":"Superior Use of YOLOv8 to Enhance Car License Plates Detection Speed and Accuracy","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; Computer science; Automotive engineering; Artificial intelligence; Engineering","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":[0.0001316887,0.0001593247,0.0001858137,0.0001837002,0.00004403115,0.0001085862,0.00007688258,0.00009515102,0.0001026608],"category_scores_gemma":[0.0001664735,0.0001716241,0.00005494568,0.0004603287,0.00003836361,0.0003629848,0.00003334503,0.0001775878,0.0004905108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004739924,"about_ca_system_score_gemma":0.000009105282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003630094,"about_ca_topic_score_gemma":0.00002952694,"domain_scores_codex":[0.9990208,0.00002011735,0.0003502618,0.0002791946,0.00009192181,0.0002376833],"domain_scores_gemma":[0.9992422,0.0003632127,0.000021852,0.0002122457,0.00006376885,0.00009674807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001988819,0.00001449986,0.00003923858,0.0003297929,0.00003035922,0.00001854136,0.001782867,0.06487258,0.7785664,0.00006180315,0.00005516771,0.1542089],"study_design_scores_gemma":[0.0000059644,0.00004203741,0.00004278781,0.0001873334,0.00001363799,0.00003411204,0.00013596,0.4009115,0.5944839,0.0000401472,0.00398205,0.0001206057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722806,0.0007103329,0.02580339,0.00006689987,0.0004257743,0.0002629607,0.00002162966,0.0002891057,0.0001393429],"genre_scores_gemma":[0.9984274,0.0005901058,0.0006444618,0.00001837561,0.00008131327,0.00001210562,0.00000491454,0.0000393459,0.0001819858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3360389,"threshold_uncertainty_score":0.6998628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03171868288744403,"score_gpt":0.2680201942981091,"score_spread":0.236301511410665,"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."}}