{"id":"W4416707208","doi":"10.1109/tits.2025.3633499","title":"Driver State Classification: Identifying High Cognitive Load and Drowsiness Through Driver Performance and Physiology","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognition; Driving simulator; Cognitive load; Arousal; Recall; Standard deviation; Effects of sleep deprivation on cognitive performance; Steering wheel; Elementary cognitive task","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"],"consensus_categories":[],"category_scores_codex":[0.0001551602,0.0002836911,0.0003311354,0.0002192866,0.0003326486,0.00006028391,0.0001024712,0.0002370774,0.0001922398],"category_scores_gemma":[0.000001830797,0.0002826922,0.0000739537,0.0004622481,0.000246205,0.0002992663,5.04628e-7,0.0003583294,0.000116716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007377879,"about_ca_system_score_gemma":0.00004101872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005533876,"about_ca_topic_score_gemma":0.0001483576,"domain_scores_codex":[0.9981299,0.0001353604,0.0006192939,0.0006115007,0.0002190514,0.0002848801],"domain_scores_gemma":[0.9990326,0.0002314488,0.000161087,0.0002302793,0.0002715715,0.00007298032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005148381,0.002774702,0.03382576,0.001513516,0.007431454,0.0001029847,0.1409221,0.02318561,0.008053328,0.03715675,0.001385734,0.7384997],"study_design_scores_gemma":[0.01099983,0.0009609229,0.8908354,0.005997533,0.002502672,0.00004296214,0.04111445,0.004714644,0.03661894,0.0006314375,0.002956415,0.002624769],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6118525,0.0003692666,0.3840434,0.00006930544,0.002279066,0.0005196825,0.00008038237,0.00009819242,0.0006881634],"genre_scores_gemma":[0.997105,0.0008945516,0.00005037145,0.0001275315,0.00003328666,0.0002892418,0.00004835872,0.00002756478,0.001424074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8570096,"threshold_uncertainty_score":0.9999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798165093754652,"score_gpt":0.3072315874268108,"score_spread":0.2692499364892643,"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."}}