{"id":"W4214583847","doi":"10.3390/s22051858","title":"E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Distracted driving; Distraction; Overfitting; Computer science; Deep learning; Machine learning; Artificial intelligence; Ensemble learning; Generalization; Scalability; Phone; Artificial neural network","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001728903,0.0001156249,0.0001019322,0.0001504124,0.0007217872,0.00002683323,0.00006298549,0.00004599033,0.007096113],"category_scores_gemma":[0.00002063907,0.0001207688,0.00006612877,0.0002171386,0.00002499542,0.00009263708,0.00001461627,0.0005047149,0.000262773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002086852,"about_ca_system_score_gemma":0.00002209585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008941436,"about_ca_topic_score_gemma":0.0002180408,"domain_scores_codex":[0.9988211,0.0003182682,0.0002178822,0.000267146,0.0001942394,0.000181397],"domain_scores_gemma":[0.9994096,0.0001051961,0.00017328,0.0001883283,0.0000664482,0.00005715459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005955322,0.0002872455,0.0006957865,0.000006729218,0.00008758712,0.00001603825,0.004827525,0.9423094,0.003157143,0.000884314,0.002619615,0.0445131],"study_design_scores_gemma":[0.001349106,0.000304861,0.00699568,0.000006988179,0.00004406239,0.0001046387,0.007803852,0.8467228,0.0005980153,0.00005812714,0.1357207,0.0002912255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8784189,0.000006897918,0.07668423,0.001141425,0.001019408,0.0002703227,0.00001582146,0.0004914115,0.04195163],"genre_scores_gemma":[0.9894876,0.000001392633,0.0004896563,0.0002808521,0.00004984899,0.0001234964,0.00009328283,0.00002714188,0.009446703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1331011,"threshold_uncertainty_score":0.9938115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0246972613401667,"score_gpt":0.3171466706333372,"score_spread":0.2924494092931705,"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."}}