{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007633145,0.0003383465,0.0001824082,0.0004121442,0.000216197,0.0004822954,0.0002325276,0.0002973319,0.001212782],"category_scores_gemma":[0.006793968,0.0002024373,0.0002760995,0.0001285159,0.0002535073,0.0002126475,0.0004258885,0.0003871285,0.0001193459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001714975,"about_ca_system_score_gemma":0.0002495447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163718,"about_ca_topic_score_gemma":0.001329794,"domain_scores_codex":[0.9994944,0.0001662898,0.00005313207,0.00008146594,0.0001268107,0.00007791199],"domain_scores_gemma":[0.9895403,0.008040042,0.00109723,0.0003566049,0.000483055,0.0004827649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01533991,0.002854082,0.5106165,0.0009893419,0.0005846159,0.001199501,0.006240654,0.001681976,0.2850828,0.0004822597,0.0006240814,0.1743042],"study_design_scores_gemma":[0.00004804211,0.003403554,0.9825823,0.00004387637,0.0001355603,0.0005197914,0.0005460503,0.0007504301,0.01115554,0.0002115823,0.0005800555,0.00002312568],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985909,0.0003013496,0.0003877632,0.00001740511,0.000009080828,0.00001076159,0.00003649108,0.000006903785,0.0006393733],"genre_scores_gemma":[0.9992087,0.0001781125,0.000336162,0.00002136219,0.000008347764,0.00001668005,0.00005858687,0.000004208891,0.0001678139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001212782,"threshold_uncertainty_score":0.004057109,"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."}}