{"id":"W4417509118","doi":"10.1109/icsit65336.2025.11294554","title":"Advanced Detection of Helmet Usage and Number Plates in Riders","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Law enforcement; Object detection; Human error; Data collection; Object (grammar); Automation","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":[],"consensus_categories":[],"category_scores_codex":[0.0001608434,0.0001879103,0.0002739482,0.0001744184,0.0001123692,0.00004583418,0.0003479395,0.00009238403,0.00002723102],"category_scores_gemma":[0.00005898263,0.0001981693,0.00004404102,0.001851027,0.0001685725,0.0006049018,0.0003406359,0.0002085913,0.00001291816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005579219,"about_ca_system_score_gemma":0.00004475647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006155837,"about_ca_topic_score_gemma":0.0003406304,"domain_scores_codex":[0.998414,0.00006332426,0.0004975738,0.00058824,0.0001336199,0.0003031988],"domain_scores_gemma":[0.9988323,0.0003924414,0.0001504712,0.0004927895,0.0000696533,0.00006227125],"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":[0.00006013555,0.0001357734,0.00604227,0.00009487879,0.00002423106,0.000003625673,0.000336365,0.008934739,0.05495041,0.0392678,0.00005693202,0.8900928],"study_design_scores_gemma":[0.00232767,0.0001600115,0.04633854,0.0003726089,0.00003377933,0.0000205637,0.0002881665,0.502485,0.3751605,0.06741725,0.004746749,0.0006490761],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3067711,0.0005701024,0.6865357,0.0007759916,0.0002521609,0.0004827805,0.000002072104,0.00005426527,0.004555792],"genre_scores_gemma":[0.9725178,0.0006074232,0.0253926,0.0002396792,0.0000112753,0.0000445256,5.075286e-7,0.000006889066,0.001179313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8894438,"threshold_uncertainty_score":0.8081109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009549717358566403,"score_gpt":0.2761873870102191,"score_spread":0.2666376696516526,"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."}}