{"id":"W7138844127","doi":"10.1109/cis69366.2025.11433892","title":"MDCA Plate: A Two Stage License Plate Recognition System with Channel Attention and Multi-Dilation Features","year":2025,"lang":"","type":"article","venue":"","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Header; Channel (broadcasting); Detector; Dilation (metric space); Decoding methods; Pattern recognition (psychology); Block (permutation group theory); Context (archaeology); Pipeline (software)","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":[0.0004379011,0.001526889,0.0007954399,0.001293261,0.0004397647,0.001322716,0.002111319,0.0009398619,0.02023964],"category_scores_gemma":[0.00122029,0.0006780299,0.0006830983,0.0005139244,0.0003957368,0.001738503,0.001678671,0.001346684,0.01423978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006314128,"about_ca_system_score_gemma":0.001295025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009059844,"about_ca_topic_score_gemma":0.01546754,"domain_scores_codex":[0.9994438,0.00003165051,0.00002486493,0.0002124377,0.000194796,0.00009241809],"domain_scores_gemma":[0.9995783,0.00005746946,0.00001963273,0.0001218583,0.0001753279,0.00004748279],"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":[0.001196728,0.0003399138,0.002897054,0.0004081084,0.0002572606,0.00050074,0.0001505853,0.01146939,0.1772802,0.002343638,0.1161929,0.6869636],"study_design_scores_gemma":[0.0002425669,0.0009385141,0.01049194,0.00007125809,0.0001938293,0.001506754,0.0001694048,0.4662047,0.4005999,0.00262964,0.1166499,0.0003016053],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09147686,0.001354733,0.610105,0.0004300432,0.001030649,0.001059003,0.01013013,0.2566123,0.0278012],"genre_scores_gemma":[0.3582895,0.0004635604,0.5259247,0.000922308,0.0001894993,0.0008577873,0.03393436,0.004200504,0.07521763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02023964,"threshold_uncertainty_score":0.06770825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01604864499711805,"score_gpt":0.2329312882863945,"score_spread":0.2168826432892764,"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."}}