{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000930278,0.001390629,0.001108978,0.0007249829,0.0002956303,0.0004582255,0.00226711,0.0009494835,0.001177277],"category_scores_gemma":[0.001727372,0.0005513146,0.001020213,0.0006416325,0.0001510563,0.0008825605,0.0007544506,0.001929295,0.0006106581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006287606,"about_ca_system_score_gemma":0.0008902678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02871235,"about_ca_topic_score_gemma":0.03738235,"domain_scores_codex":[0.9996197,0.00006752936,0.0000186758,0.0001442794,0.00007372432,0.00007599471],"domain_scores_gemma":[0.9995407,0.0001486832,0.0000363108,0.0000572289,0.0001796073,0.00003754552],"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":[0.000430331,0.0009679766,0.01256999,0.000107475,0.0006397223,0.0001773714,0.0001160949,0.5428801,0.004812493,0.00102507,0.01259955,0.4236738],"study_design_scores_gemma":[0.0000102273,0.00007375201,0.0007160128,0.000007611772,0.00003497463,0.00001604512,0.000007593957,0.9977203,0.0005902314,0.0002857572,0.0005301847,0.000007228687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2674703,0.004736375,0.7085543,0.001066583,0.0005615304,0.0002395063,0.002624137,0.009919931,0.004827396],"genre_scores_gemma":[0.8617706,0.0009465704,0.1246055,0.0005471372,0.0001586971,0.0002016139,0.004022745,0.0001429605,0.007604148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02871235,"threshold_uncertainty_score":0.05709046,"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."}}