{"id":"W2809793459","doi":"10.1007/978-3-319-94589-7_16","title":"Expert Elicitation Methodology in the Risk Analysis of an Industrial Machine","year":2018,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Expert elicitation; Supervisor; Computer science; Judgement; Failure rate; Set (abstract data type); Reliability (semiconductor); Process (computing); Human error; Risk analysis (engineering); Operations research; Artificial intelligence; Reliability engineering; Engineering; Statistics; Mathematics","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":[0.01217143,0.0002723222,0.001332889,0.001687081,0.0001287122,0.0001019966,0.0007915627,0.0002941428,0.0001002299],"category_scores_gemma":[0.001277321,0.0001699075,0.0002886572,0.000976814,0.0002099522,0.0002022376,0.000119279,0.0004464327,0.000008376653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004583289,"about_ca_system_score_gemma":0.0000264374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001920654,"about_ca_topic_score_gemma":0.004390247,"domain_scores_codex":[0.9944686,0.001634008,0.002078698,0.0007024352,0.000901754,0.000214554],"domain_scores_gemma":[0.9905198,0.006932336,0.001623517,0.000649842,0.0002264317,0.00004808427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001107187,0.00004269252,0.01186995,0.00001272065,0.0004578974,0.00001308297,0.01186171,0.1152231,0.000002356691,0.02315208,0.00004395435,0.8372098],"study_design_scores_gemma":[0.0004534435,0.0004241656,0.001639777,0.0004275522,0.0008553595,0.00001385609,0.02013436,0.757916,0.00001864525,0.1222537,0.09521893,0.0006441674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1505629,0.1111618,0.6645634,0.0002655801,0.004026125,0.001736754,0.0001918538,0.00005101336,0.06744059],"genre_scores_gemma":[0.9754521,0.01631287,0.003858312,0.00009552345,0.0007506156,0.00001204008,0.00006863687,0.00003015401,0.003419797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8365656,"threshold_uncertainty_score":0.6928626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2535001467966326,"score_gpt":0.445615514187176,"score_spread":0.1921153673905434,"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."}}