{"id":"W3216389778","doi":"10.1109/tits.2021.3125737","title":"Detection of Train Driver Fatigue and Distraction Based on Forehead EEG: A Time-Series Ensemble Learning Method","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Science Foundation of Hunan Province; Central South University; National Natural Science Foundation of China","keywords":"Distraction; Forehead; Electroencephalography; Series (stratigraphy); Computer science; Ensemble learning; Artificial intelligence; Psychology; Cognitive psychology; Medicine; Neuroscience","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003484539,0.0002839381,0.0003813496,0.0003177406,0.0002314294,0.00004050952,0.00006915203,0.0003286439,0.0004751138],"category_scores_gemma":[0.000008223461,0.000296926,0.00021984,0.0005067135,0.00006905539,0.0001714555,1.47993e-7,0.0005061017,0.00007194409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007415625,"about_ca_system_score_gemma":0.00004255518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003510055,"about_ca_topic_score_gemma":0.0003733945,"domain_scores_codex":[0.9976776,0.0004371676,0.0007508521,0.0005239191,0.0003506734,0.0002598459],"domain_scores_gemma":[0.9987172,0.0004334926,0.000251645,0.0002794681,0.0002031953,0.0001149788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001744588,0.00121513,0.0006450095,0.0001924836,0.0007677897,0.00007417048,0.009692409,0.6675022,0.06681477,0.001409907,0.00006196649,0.2498796],"study_design_scores_gemma":[0.003931881,0.0027869,0.009697404,0.001825128,0.001081617,0.00007659815,0.01659915,0.03411464,0.9249844,0.00007110903,0.003474119,0.001357079],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07848195,0.0001179854,0.9181486,0.00008170059,0.001420167,0.0004484229,0.00008201058,0.0001445936,0.001074596],"genre_scores_gemma":[0.9977202,0.00004433932,0.0007629711,0.00003320427,0.00003471943,0.0001792243,0.0001001379,0.00004986194,0.001075338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9192383,"threshold_uncertainty_score":0.9999483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02883655132244891,"score_gpt":0.3030444507493363,"score_spread":0.2742078994268874,"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."}}