{"id":"W4205908004","doi":"10.1142/9789811239014_0011","title":"A Comprehensive Unconstrained, License Plate Database","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"License; Database; Computer science; MIT License; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005603803,0.00163033,0.00113759,0.006142699,0.000681127,0.00149835,0.002610875,0.0006246426,0.0552155],"category_scores_gemma":[0.001785219,0.0006139746,0.0005167885,0.006908322,0.0002264311,0.002464863,0.001269155,0.0006793046,0.06852131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004209645,"about_ca_system_score_gemma":0.001808292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009163763,"about_ca_topic_score_gemma":0.01834403,"domain_scores_codex":[0.9993954,0.00003658413,0.00006975758,0.0001303323,0.0003344655,0.00003351183],"domain_scores_gemma":[0.9989147,0.0001043126,0.00004042644,0.0003259348,0.0005248593,0.00008973395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002120047,0.0002329919,0.001041641,0.0006080017,0.00004973857,0.0001855079,0.00003311935,0.001640267,0.008754799,0.00112653,0.6006076,0.3855078],"study_design_scores_gemma":[0.0001613212,0.0001784996,0.01321705,0.0002235524,0.0001341456,0.001599123,0.0001822055,0.02422123,0.04116671,0.002591282,0.9161984,0.0001264451],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01958092,0.00734816,0.1282334,0.0003721087,0.0006605535,0.001077309,0.716203,0.06238974,0.06413492],"genre_scores_gemma":[0.011601,0.001967313,0.05624673,0.0001488789,0.00007503205,0.0003285267,0.8876616,0.001430139,0.04054081],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0552155,"threshold_uncertainty_score":0.1847143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03309967527934524,"score_gpt":0.2298853703006805,"score_spread":0.1967856950213353,"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."}}