{"id":"W2011493139","doi":"10.1177/1071181312561483","title":"Automated Driving: Human-Factors Issues and Design Solutions","year":2012,"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":138,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cruise control; Perspective (graphical); Computer science; Focus (optics); Human–machine system; Human–computer interaction; Control (management); Automation; Risk analysis (engineering); Systems engineering; Engineering; Artificial intelligence","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.000744117,0.0002773862,0.0003240894,0.00006367971,0.001405584,0.0001127618,0.0002318826,0.000206458,0.0001393136],"category_scores_gemma":[0.00009151069,0.0002144146,0.0001982859,0.0001150489,0.0002898661,0.0005967024,0.0002287346,0.0003117974,0.000005313414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008380911,"about_ca_system_score_gemma":0.00001036403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000199306,"about_ca_topic_score_gemma":0.000005836785,"domain_scores_codex":[0.9985371,0.00003617401,0.0005049573,0.0002892872,0.000141829,0.0004906103],"domain_scores_gemma":[0.9989395,0.0001526622,0.000477389,0.0001150548,0.0001593925,0.0001560189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002648627,0.0002806565,0.6902032,0.0001219925,0.0004169254,4.437573e-8,0.2039513,0.00001716655,0.02359367,0.03653805,0.04474533,0.0001051837],"study_design_scores_gemma":[0.0004962124,0.0000925811,0.9213745,0.0001190813,0.0001068784,0.000006636134,0.06860127,0.0005281967,0.003705073,0.0003994449,0.004131013,0.0004391205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996284,0.0002173333,0.00001654974,0.0001269412,0.0004423845,0.0002428196,0.00001910289,0.0002903308,0.002360532],"genre_scores_gemma":[0.9982757,0.00003063285,0.0004335449,0.0000638857,0.0001881962,0.00001375154,0.000006111681,0.00003532471,0.0009528625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2311713,"threshold_uncertainty_score":0.9998944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05136252691955629,"score_gpt":0.3279819495375023,"score_spread":0.276619422617946,"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."}}