{"id":"W4403466407","doi":"10.2139/ssrn.4952697","title":"Unveiling Regulatory Operations: A Data Set of the Determinants, Process, and Outcomes of Product Defect Investigations by the U.S. Automotive Safety Regulator&amp;nbsp;","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Safety Systems Engineering in Autonomy","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Western University","funders":"","keywords":"Regulator; Automotive industry; Product (mathematics); Set (abstract data type); Process (computing); Business; Risk analysis (engineering); Process management; Chemistry; Computer science; Engineering; Biochemistry; Mathematics; Gene","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":[0.001767298,0.000203773,0.000274455,0.00008922745,0.0001708392,0.00005175072,0.0006188572,0.00006354557,0.000004058263],"category_scores_gemma":[0.0003384939,0.0001279453,0.00008911865,0.0004047857,0.000156623,0.0003380564,0.0000924653,0.0009366026,0.000001996342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003379547,"about_ca_system_score_gemma":0.001213526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002696976,"about_ca_topic_score_gemma":0.000264676,"domain_scores_codex":[0.9982268,0.00009178633,0.0005738029,0.000233529,0.0002694577,0.0006046724],"domain_scores_gemma":[0.9989088,0.000141475,0.00008760591,0.0007269116,0.00009260944,0.00004260708],"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":[0.00005139072,0.0001437547,0.08373109,0.004669171,0.009169237,0.000005297816,0.01709461,0.7057679,0.02379892,0.08737683,0.007145678,0.0610461],"study_design_scores_gemma":[0.002358159,0.0002868575,0.07908288,0.004526943,0.001884346,0.004410043,0.005727659,0.7801291,0.03414029,0.03379061,0.05130155,0.002361584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9374803,0.04986442,0.00945381,0.001147009,0.000788314,0.0007601954,0.0001878817,0.0002212343,0.00009687559],"genre_scores_gemma":[0.9986468,0.0006074584,0.0002824583,0.00001037398,0.0001054743,0.00001479675,0.00001534238,0.00005073081,0.0002665742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07436115,"threshold_uncertainty_score":0.5217459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01199447439389818,"score_gpt":0.2493744694771927,"score_spread":0.2373799950832945,"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."}}