{"id":"W2191460748","doi":"","title":"Automatic Number Carplate Recognition with Means Algorithm and Neural Network","year":2015,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Fuzzy logic; Artificial intelligence; Sensitivity (control systems); Process (computing); Algorithm; Sample (material); Image (mathematics); Pattern recognition (psychology); Computer vision; Fuzzy control system; Time delay neural network; 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":[0.0002523265,0.0001340848,0.0002978778,0.00003851245,0.00005380816,0.00001517222,0.00003323552,0.0000876227,0.000001883225],"category_scores_gemma":[0.00001159335,0.00009916449,0.00001638944,0.00009436642,0.00006108837,0.0001670985,0.00002082754,0.0004461815,0.000003500545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002629564,"about_ca_system_score_gemma":0.000008362721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.634283e-7,"about_ca_topic_score_gemma":6.631421e-7,"domain_scores_codex":[0.9992951,0.0000152545,0.0002845828,0.00007605796,0.0001586419,0.0001703698],"domain_scores_gemma":[0.9995927,0.00008297842,0.0001193356,0.0000281902,0.00006766783,0.0001091716],"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":[0.000120513,0.00001503646,0.001813639,0.0002816491,0.001138946,0.00005515508,0.006947157,0.01355909,0.000611759,0.00003862848,0.007177394,0.968241],"study_design_scores_gemma":[0.02047188,0.00138895,0.02218875,0.003964329,0.003689822,0.01742481,0.05211898,0.8145154,0.005115286,0.05016132,0.005589948,0.003370542],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942901,0.003902463,0.00079361,0.0001540347,0.0001496454,0.00009096113,0.000003267207,0.00005414729,0.0005618226],"genre_scores_gemma":[0.9847794,0.004799779,0.00982658,0.00009560274,0.0004639553,0.000005790734,0.000001550263,0.00002169787,0.000005602419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9648705,"threshold_uncertainty_score":0.4043811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03470792291996981,"score_gpt":0.2572645347065808,"score_spread":0.222556611786611,"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."}}