{"id":"W3181853338","doi":"10.3390/risks10070141","title":"Reverse Sensitivity Analysis for Risk Modelling","year":2022,"lang":"en","type":"article","venue":"Risks","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Connaught Fund","keywords":"Sensitivity (control systems); Monte Carlo method; Measure (data warehouse); Random variable; Distortion (music); Set (abstract data type); Probability distribution; Mathematics; Variance (accounting); Computer science; Baseline (sea); Mathematical optimization; Statistics; Engineering; Data mining","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":[0.01511862,0.002605679,0.002194197,0.002467501,0.0007208133,0.003196589,0.002665585,0.002731468,0.00542579],"category_scores_gemma":[0.04502001,0.001349427,0.003716572,0.001190383,0.003115321,0.003191504,0.004386866,0.004842603,0.0006552179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002957889,"about_ca_system_score_gemma":0.002069203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00515353,"about_ca_topic_score_gemma":0.002294915,"domain_scores_codex":[0.9915839,0.00552049,0.0002917522,0.001052502,0.001203943,0.0003474258],"domain_scores_gemma":[0.9758775,0.01970062,0.001542503,0.001384102,0.001177736,0.0003175465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003478789,0.00002429622,0.0006211378,0.0001105312,0.0001226731,0.0001530935,0.00008257244,0.8597977,0.0004562014,0.1311892,0.000650635,0.006757216],"study_design_scores_gemma":[0.000006203745,0.00002336431,0.0001065745,0.00003504419,0.00002357279,0.00004794479,0.00002001614,0.834212,0.0003345085,0.1641846,0.0009834546,0.00002278856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004958383,0.0002991284,0.9916607,0.0004054876,0.00003875059,0.00004860061,0.0001015685,0.0001243904,0.002363064],"genre_scores_gemma":[0.6752629,0.001241544,0.3143889,0.0007926174,0.0002197359,0.0007120728,0.0004879032,0.0004765323,0.006417659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01511862,"threshold_uncertainty_score":0.07995582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.283513783390662,"score_gpt":0.3795605023034666,"score_spread":0.09604671891280453,"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."}}