{"id":"W4386919836","doi":"10.1109/sas58821.2023.10254136","title":"Assessing Driver Task Engagement Through Machine Learning Classification of Physiological Response","year":2023,"lang":"en","type":"article","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Machine learning; Human–computer interaction; Engineering; Systems engineering","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":[0.0008705698,0.0004970216,0.0003343357,0.0009812831,0.000168434,0.0008877512,0.0002749764,0.0006154425,0.0009887381],"category_scores_gemma":[0.004129922,0.0001385767,0.0003644279,0.0004928643,0.0001817273,0.0005862254,0.0004246981,0.000566769,0.0004927863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003460429,"about_ca_system_score_gemma":0.0002815159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002003724,"about_ca_topic_score_gemma":0.002337484,"domain_scores_codex":[0.9995789,0.0001046101,0.00003192476,0.0001393726,0.00008020275,0.0000649822],"domain_scores_gemma":[0.9988068,0.0006597872,0.0001653797,0.00008146349,0.0002122098,0.00007434006],"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":[0.001478428,0.001879532,0.4003729,0.0002659865,0.0004138593,0.0002599293,0.0012712,0.04330482,0.0670237,0.001120737,0.00250642,0.4801025],"study_design_scores_gemma":[0.00003110809,0.0008371655,0.3882249,0.0000305747,0.00008445924,0.0001962871,0.0004848796,0.5971948,0.009989123,0.001843614,0.00102348,0.0000596523],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9384266,0.0001841994,0.05783441,0.0001559483,0.00004673293,0.0001380742,0.0004803165,0.0003744234,0.002359299],"genre_scores_gemma":[0.9922488,0.00006921062,0.006822025,0.00002189003,0.00001529159,0.00005153396,0.0003214266,0.00001026357,0.0004395682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002003724,"threshold_uncertainty_score":0.004604101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1932777365277185,"score_gpt":0.460121619705934,"score_spread":0.2668438831782155,"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."}}