{"id":"W4405982974","doi":"10.1007/978-3-031-74491-4_18","title":"Vehicle Anti-Theft Systems Using Vision Transformer and Iris Identification","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kingston Health Sciences Centre","funders":"","keywords":"Iris recognition; Identification (biology); Computer science; Computer vision; IRIS (biosensor); Transformer; Artificial intelligence; Biometrics; Computer security; Engineering; Electrical engineering","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":[0.0001231193,0.0004055928,0.000409878,0.0005760761,0.0002937742,0.0007101067,0.0005585002,0.0004264515,0.005284346],"category_scores_gemma":[0.0001902591,0.0002291627,0.0002619884,0.0004084592,0.0001656531,0.0007328542,0.0003176508,0.0003369939,0.002414003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997665,"about_ca_system_score_gemma":0.000237065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118543,"about_ca_topic_score_gemma":0.001804729,"domain_scores_codex":[0.9998763,0.00001016011,0.00000474454,0.00002747135,0.00005971979,0.00002159548],"domain_scores_gemma":[0.9999126,0.00001803663,0.00000879628,0.00001408018,0.00004185715,0.000004590293],"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":[0.0004371491,0.0001010671,0.001220811,0.0001379188,0.00005976644,0.00009832108,0.00007569545,0.007741359,0.2706154,0.003636517,0.004264648,0.7116114],"study_design_scores_gemma":[0.00008659469,0.001042262,0.007129004,0.00006432655,0.0002672631,0.001323577,0.0001730206,0.4072486,0.5451443,0.003169628,0.03426908,0.00008224635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.12115,0.001936164,0.83155,0.0001493351,0.0002974103,0.0001318575,0.0002764386,0.00595337,0.03855544],"genre_scores_gemma":[0.8405746,0.00136747,0.122618,0.0001067495,0.00008988605,0.00005005079,0.0004786887,0.000138565,0.03457601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005284346,"threshold_uncertainty_score":0.0176779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02077745098445725,"score_gpt":0.2562958646704098,"score_spread":0.2355184136859526,"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."}}