{"id":"W2744758642","doi":"10.1016/j.ssci.2017.07.010","title":"Incorporating individual differences in human reliability analysis: An extension to the virtual experimental technique","year":2017,"lang":"en","type":"article","venue":"Safety Science","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Canada Research Chairs","keywords":"Human reliability; Computer science; Reliability (semiconductor); Human error; Competence (human resources); Identification (biology); Data collection; Reliability engineering; Risk analysis (engineering); Virtual actor; Machine learning; Artificial intelligence; Data mining; Engineering; Virtual reality; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01167129,0.0009042326,0.001273788,0.0009674117,0.0006212782,0.001053001,0.002708139,0.00113838,0.004847636],"category_scores_gemma":[0.04158681,0.0006070872,0.001678366,0.0008257063,0.001851879,0.00227303,0.002204856,0.001569483,0.0003652951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003876524,"about_ca_system_score_gemma":0.001202356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00196915,"about_ca_topic_score_gemma":0.001609393,"domain_scores_codex":[0.9919508,0.005944471,0.0001555338,0.000961586,0.0007913143,0.0001964229],"domain_scores_gemma":[0.960404,0.03104645,0.001685644,0.005376591,0.001142051,0.0003453623],"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":[0.001813655,0.002625485,0.0183176,0.0005195616,0.001395437,0.0004645458,0.001526824,0.4510136,0.01185239,0.1921735,0.001641103,0.3166563],"study_design_scores_gemma":[0.0001193618,0.001160127,0.005279164,0.00002904414,0.0001767869,0.0001720276,0.00009028463,0.900001,0.001841491,0.08961561,0.001435252,0.00007983442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02800028,0.00003975111,0.9705262,0.00006904121,0.0000553684,0.00007554681,0.00004401495,0.00008643355,0.001103511],"genre_scores_gemma":[0.6757271,0.0001092943,0.3218909,0.0001204044,0.0001392659,0.0004879667,0.0000663168,0.00007849462,0.00138024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01167129,"threshold_uncertainty_score":0.06172442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1119351806188155,"score_gpt":0.4185156056680448,"score_spread":0.3065804250492293,"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."}}