{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001112488,0.00012407,0.0001308516,0.0009590526,0.00009148053,0.00001339923,0.0003071885,0.000103114,0.0001149752],"category_scores_gemma":[0.000005246142,0.0001631615,0.0000547607,0.0005067076,0.00003543495,0.0001328673,0.000006575111,0.0005923308,0.000007982846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000241183,"about_ca_system_score_gemma":0.000006176046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002776695,"about_ca_topic_score_gemma":0.00008606553,"domain_scores_codex":[0.9991662,0.00002475616,0.000291684,0.0002198274,0.0001228105,0.0001747183],"domain_scores_gemma":[0.9996541,0.00001401474,0.00003819049,0.0002587716,0.00001560822,0.00001929697],"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":[0.00001607296,0.00005565069,0.00006106521,0.000009308817,0.00002803685,0.000004466014,0.0003180785,0.957248,0.002972638,0.000569954,0.00008961468,0.03862706],"study_design_scores_gemma":[0.0004227803,0.0000510255,0.0001066359,0.00001050758,0.00002551409,0.000005959214,0.0004158904,0.9906625,0.006486002,0.0006258438,0.001046221,0.0001411249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430583,0.00002839606,0.8521408,0.0001235912,0.0002095077,0.0002065831,0.000007698373,0.004088536,0.0001366092],"genre_scores_gemma":[0.9862211,0.00007750642,0.01320874,0.0000278066,0.000002998509,0.0002304728,0.00002491779,0.00003207955,0.0001743476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8431628,"threshold_uncertainty_score":0.6653532,"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."}}