{"id":"W4282928664","doi":"10.1109/noms54207.2022.9789929","title":"Convolutional and Recurrent Neural Networks for Driver Identification: An Empirical Study","year":2022,"lang":"en","type":"article","venue":"NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Convolutional neural network; Identification (biology); Benchmark (surveying); Personalization; Machine learning; Variety (cybernetics); Artificial intelligence; Task (project management); Deep learning; Convolution (computer science); Recurrent neural network; Artificial neural network; Data mining; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005563482,0.0002431636,0.0002363078,0.0001315158,0.001607549,0.0001198327,0.0002406951,0.00007738743,0.0001186351],"category_scores_gemma":[0.000001712753,0.0002757292,0.00005378913,0.0003566226,0.00009376079,0.0002419155,0.0002435599,0.0003619074,0.000002416968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001229117,"about_ca_system_score_gemma":0.00001252777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005890284,"about_ca_topic_score_gemma":0.00007460379,"domain_scores_codex":[0.9983218,0.0001230918,0.0004311623,0.0005435052,0.0001932654,0.0003871931],"domain_scores_gemma":[0.9994,0.00004470593,0.00004421512,0.0003732091,0.00003726807,0.0001006459],"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":[0.00004304124,0.0002749862,0.007051984,0.00002652429,0.00023609,0.00001026366,0.0004609883,0.9660371,0.00005724088,0.006367838,0.01460265,0.004831298],"study_design_scores_gemma":[0.0008575868,0.0003218653,0.01560675,0.000003624739,0.0001589172,0.00001375834,0.0008625002,0.9598181,0.000002496478,0.0001442763,0.02188306,0.000327065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.828104,0.00261541,0.1528447,0.0026048,0.00589909,0.005821783,0.0001900702,0.001098178,0.0008220156],"genre_scores_gemma":[0.9946558,0.0006017354,0.0006000185,0.0002676101,0.000370364,0.002128282,0.000325598,0.00004360711,0.00100694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1665519,"threshold_uncertainty_score":0.9999695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177548300861323,"score_gpt":0.2496739750925362,"score_spread":0.237898492083923,"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."}}