{"id":"W2893620045","doi":"10.1177/1541931218621444","title":"The Effects of Distraction on Anticipatory Driving","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Distraction; Anticipation (artificial intelligence); Driving simulator; Task (project management); Psychology; Poison control; Human factors and ergonomics; Applied psychology; Cognitive psychology; Injury prevention; Simulation; Computer science; Engineering; Medicine; Medical emergency","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004050472,0.0001319101,0.0001659049,0.00002188911,0.0008381003,0.00004397951,0.0002064202,0.0000930056,0.00002382016],"category_scores_gemma":[0.0001808669,0.00008338512,0.000176406,0.00006700281,0.0003544177,0.0001396035,0.00008242269,0.0002137102,0.000003643213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004591012,"about_ca_system_score_gemma":0.000008164562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005014634,"about_ca_topic_score_gemma":0.000008141377,"domain_scores_codex":[0.999146,0.0000180489,0.0003476875,0.0001848274,0.0001168265,0.0001865992],"domain_scores_gemma":[0.9988006,0.0003512219,0.0005306853,0.0001009235,0.0001771377,0.00003944273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003367623,0.0005167011,0.4833822,0.0006763947,0.000862973,1.310744e-7,0.228614,0.000006118349,0.1395963,0.1065935,0.03518299,0.004232033],"study_design_scores_gemma":[0.000450475,0.0003241639,0.9228526,0.000313681,0.00006017112,0.000001803102,0.0320227,0.0001219068,0.0384751,0.0004817982,0.00469683,0.0001987857],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931732,0.00002577936,0.000003287202,0.00007201696,0.0009589353,0.0001463874,0.000005037764,0.00003024695,0.005585093],"genre_scores_gemma":[0.9991725,0.00001871968,0.00002425867,0.00005631851,0.0002018719,0.000007026139,6.972479e-7,0.00001499938,0.0005036129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4394704,"threshold_uncertainty_score":0.6446075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01547514829620033,"score_gpt":0.3028191983799839,"score_spread":0.2873440500837836,"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."}}