{"id":"W4211040998","doi":"10.1145/3412353","title":"Driver Identification Using Optimized Deep Learning Model in Smart Transportation","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Internet Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Hyperparameter; Identification (biology); Deep learning; Novelty; Artificial intelligence; Machine learning; Facial recognition system; Face (sociological concept); Novelty detection; Feature extraction","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":[0.0003362236,0.0005697887,0.0005279082,0.0004508284,0.0002255595,0.0005590615,0.0006996862,0.0008296093,0.001512086],"category_scores_gemma":[0.0006060096,0.0002907252,0.0005246119,0.0003874153,0.0002318177,0.000617702,0.0004356453,0.0009016835,0.0004126187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007727208,"about_ca_system_score_gemma":0.0009032497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01982772,"about_ca_topic_score_gemma":0.01263705,"domain_scores_codex":[0.9998815,0.00001675102,0.00000557396,0.00003965565,0.00002005385,0.00003646713],"domain_scores_gemma":[0.9998443,0.00004864948,0.00001739295,0.000009068498,0.00006914433,0.0000114347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008423001,0.00008910729,0.002837497,0.000039199,0.00004070977,0.00007554825,0.00003116071,0.9374843,0.001528124,0.001626591,0.001693176,0.05447041],"study_design_scores_gemma":[0.000001509069,0.000006234478,0.000174673,0.000001605059,0.000002844406,0.000003378048,0.000002446353,0.9992,0.000191229,0.0003008435,0.0001135116,0.000001764639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3632937,0.002420773,0.6184373,0.001135659,0.0002383446,0.00007658268,0.0007409024,0.002295607,0.01136103],"genre_scores_gemma":[0.9756763,0.0003088306,0.0165631,0.000142779,0.00002882564,0.00005222408,0.000627839,0.00003024211,0.006569921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01982772,"threshold_uncertainty_score":0.0394246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01165852772307751,"score_gpt":0.2273962607654378,"score_spread":0.2157377330423603,"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."}}