{"id":"W3156188754","doi":"10.1016/j.aap.2021.106121","title":"Knowledge of and trust in advanced driver assistance systems","year":2021,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Poison control; Human factors and ergonomics; Advanced driver assistance systems; Occupational safety and health; Injury prevention; Suicide prevention; Engineering; Computer security; Transport engineering; Computer science; Medical emergency; Medicine; Artificial intelligence","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":[0.0002895832,0.00008273581,0.0002822578,0.0003343209,0.00003564571,0.00002762452,0.00006565775,0.00006387175,0.003231021],"category_scores_gemma":[0.00003703844,0.00008840119,0.0001668467,0.000791065,0.00001706968,0.0001817406,0.00002678299,0.00008196226,0.00006803871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005704195,"about_ca_system_score_gemma":0.00001759061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008730237,"about_ca_topic_score_gemma":0.005631277,"domain_scores_codex":[0.9987247,0.0003074181,0.0005026538,0.0002602064,0.0001007018,0.0001043418],"domain_scores_gemma":[0.9992558,0.00006311427,0.0002474215,0.0002538083,0.000147188,0.00003262339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005772523,0.0004134085,0.9623556,0.00001305915,0.00108941,0.00001075631,0.002423818,0.0005671621,0.0008835535,0.01198165,0.0005493311,0.01965449],"study_design_scores_gemma":[0.0006064436,0.00001652555,0.9917371,0.00005430438,0.000319167,0.000003441047,0.002489257,0.002150982,0.000132638,0.0001449464,0.002255635,0.00008952559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773108,0.001558905,0.005083669,0.00005193014,0.0002730774,0.0001096068,5.256943e-7,0.00002294676,0.01558859],"genre_scores_gemma":[0.9799592,0.00007245142,0.00009691854,0.00001052681,0.00002354531,0.00003263795,0.00004876075,0.000005650639,0.01975033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0293815,"threshold_uncertainty_score":0.9976802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02216769638109743,"score_gpt":0.3823729100922647,"score_spread":0.3602052137111673,"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."}}