{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006094034,0.000539474,0.0006425122,0.0007370615,0.000208005,0.0003443126,0.000498788,0.0004461495,0.0005115285],"category_scores_gemma":[0.001204976,0.0001602914,0.0006322788,0.00055366,0.0001003062,0.0004960572,0.0002896285,0.0005112275,0.0001736587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001800268,"about_ca_system_score_gemma":0.0002738799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002944172,"about_ca_topic_score_gemma":0.002931322,"domain_scores_codex":[0.9997781,0.00003471661,0.00001927864,0.00008524643,0.00005067671,0.00003197115],"domain_scores_gemma":[0.9996498,0.0001169741,0.00003648974,0.00003720578,0.0001377293,0.00002177969],"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":[0.0004095315,0.0005121554,0.01985794,0.00009209372,0.0003463374,0.0002189999,0.000168797,0.1357695,0.03927282,0.0006343613,0.002306278,0.8004112],"study_design_scores_gemma":[0.000005796267,0.00007921376,0.006241739,0.000005634783,0.00005156037,0.00005528835,0.00002016631,0.989551,0.00344829,0.0001886327,0.0003420934,0.00001058589],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3703902,0.0009635565,0.6259214,0.0001361535,0.0001135589,0.00007986691,0.0002342394,0.0008341313,0.001326882],"genre_scores_gemma":[0.9257876,0.0004801186,0.07130779,0.00005862816,0.00009005642,0.0000791581,0.0005809467,0.00003077167,0.0015848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002944172,"threshold_uncertainty_score":0.00585407,"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."}}