{"id":"W2945891083","doi":"10.1177/0361198119847985","title":"Non-Intrusive Detection of Drowsy Driving Based on Eye Tracking Data","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alcohol Countermeasure Systems (Canada)","funders":"","keywords":"Vigilance (psychology); Support vector machine; Artificial intelligence; Eye tracking; Feature extraction; Computer science; Poison control; Classifier (UML); Eye movement; Random forest; Computer vision; Pattern recognition (psychology); Psychology","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":[0.0003565671,0.0002789337,0.0002817019,0.0008374318,0.0001082557,0.0003229666,0.0001557661,0.0002245255,0.0005025381],"category_scores_gemma":[0.001771285,0.00007482696,0.0001664903,0.0003360373,0.00007648319,0.0002444882,0.0002035025,0.0001614777,0.0001814541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007492135,"about_ca_system_score_gemma":0.0001185037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226144,"about_ca_topic_score_gemma":0.003245151,"domain_scores_codex":[0.9997635,0.00005256498,0.00002579041,0.00005934699,0.00007733688,0.00002148742],"domain_scores_gemma":[0.9991092,0.0003566405,0.0002023652,0.00006340762,0.0002243322,0.00004399193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001179652,0.0003372204,0.391069,0.0005097568,0.0003196172,0.0003564071,0.0006583304,0.001951677,0.3069226,0.0001601097,0.0007094197,0.2958262],"study_design_scores_gemma":[0.00001803588,0.0007816063,0.9594799,0.00003925118,0.00009512823,0.0006728047,0.0002111168,0.01220677,0.02556262,0.0001054576,0.000796431,0.00003086233],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872072,0.000431793,0.01096165,0.00002162712,0.00001702857,0.00005914355,0.0004297365,0.00009387253,0.0007778904],"genre_scores_gemma":[0.9939282,0.000282446,0.004924014,0.00001303872,0.00001640292,0.00003195636,0.0004849299,0.00000658842,0.0003124047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001226144,"threshold_uncertainty_score":0.002438009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0894236497985717,"score_gpt":0.419526027279998,"score_spread":0.3301023774814263,"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."}}