{"id":"W581804495","doi":"","title":"INDUCTANCE-PATTERN RECOGNITION FOR VEHICLE RE-IDENTIFICATION","year":2001,"lang":"en","type":"article","venue":"8th World Congress on Intelligent Transport SystemsITS America, ITS Australia, ERTICO (Intelligent Transport Systems and Services - Europe)","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Waveform; Dynamic time warping; Computer science; Identification (biology); Inductance; Pattern recognition (psychology); Artificial neural network; Detector; Artificial intelligence; Matching (statistics); Process (computing); Signature (topology); Pattern matching; Engineering; Mathematics; Telecommunications; Voltage; Electrical engineering; Statistics","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":[0.0004191555,0.0004801919,0.0003764014,0.0009408555,0.0001935073,0.000616896,0.0005555374,0.0005101025,0.003815453],"category_scores_gemma":[0.001195942,0.0001596496,0.0002197572,0.0009193653,0.0002629671,0.0009893186,0.0003169668,0.0003826366,0.003137576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003587795,"about_ca_system_score_gemma":0.0002889916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001573419,"about_ca_topic_score_gemma":0.002151864,"domain_scores_codex":[0.9995458,0.00009304943,0.00002563415,0.00009253644,0.0002050379,0.00003799792],"domain_scores_gemma":[0.9995566,0.000108717,0.00005503427,0.00009292222,0.0001734283,0.00001328363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001297281,0.00005846488,0.001701208,0.0001409156,0.00002526972,0.00009223968,0.00005669103,0.01512134,0.07560663,0.002555295,0.002006661,0.9025055],"study_design_scores_gemma":[0.00004287956,0.0004042214,0.01318788,0.0000435628,0.00006658999,0.001038701,0.0001221288,0.660209,0.2802124,0.006802658,0.03779809,0.00007181051],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06984974,0.00149608,0.9156772,0.0004057784,0.0001907021,0.000105,0.0002752794,0.003759202,0.008241043],"genre_scores_gemma":[0.6231903,0.0008128951,0.3546081,0.0001385015,0.00008526094,0.00007954201,0.0006157398,0.0001668501,0.02030271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003815453,"threshold_uncertainty_score":0.01276392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0531900500601148,"score_gpt":0.2619268636194741,"score_spread":0.2087368135593594,"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."}}