{"id":"W65415241","doi":"10.1007/978-3-642-29461-7_5","title":"An Evidential Pattern Matching Approach for Vehicle Identification","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Identification (biology); Matching (statistics); Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Biology; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003244816,0.0002725658,0.0002903182,0.0001638039,0.00009017735,0.00009371524,0.0001445205,0.0002012408,0.00001381789],"category_scores_gemma":[0.000008876371,0.0003198296,0.00006946798,0.00002699432,0.00003111756,0.000520074,0.00003816434,0.0003157977,0.00001599755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007812698,"about_ca_system_score_gemma":0.000004583545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005693884,"about_ca_topic_score_gemma":0.00001536653,"domain_scores_codex":[0.9987223,0.00001298747,0.0005041265,0.0003248994,0.0001461021,0.0002896138],"domain_scores_gemma":[0.9994423,0.0001333924,0.000136321,0.0001669158,0.00005085224,0.00007025588],"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":[0.000006528441,0.00001649684,0.0002054447,0.0006074916,0.0000286625,0.000001160827,0.0008258916,0.04127244,0.0003814307,0.001101261,0.000004601077,0.9555486],"study_design_scores_gemma":[0.0002549161,0.00003455819,0.0001966756,0.0006720707,0.00008404386,0.00001884657,0.0002062878,0.9791057,0.001408502,0.01129473,0.005891205,0.0008324538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01842927,0.01116327,0.9633368,0.000003750827,0.000750045,0.0005161974,0.00001861361,0.0002404764,0.005541542],"genre_scores_gemma":[0.9918067,0.002633394,0.004008583,0.00001501277,0.0007289018,0.00001772247,0.0002639194,0.000107126,0.000418684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9733774,"threshold_uncertainty_score":0.9999254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017930361869126,"score_gpt":0.2635832362949212,"score_spread":0.24340393267623,"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."}}