{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006041952,0.00008685664,0.0002561589,0.0002780379,0.0004648411,0.00005295432,0.0002409462,0.0000298933,0.0002264549],"category_scores_gemma":[0.00140754,0.00007124797,0.0002798165,0.001224139,0.00002542676,0.00006703187,0.0001167867,0.0001713853,0.00002862494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005938479,"about_ca_system_score_gemma":0.00003685996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000254585,"about_ca_topic_score_gemma":0.00001417472,"domain_scores_codex":[0.9981996,0.0002948487,0.0002908099,0.0003836843,0.0006360267,0.0001950096],"domain_scores_gemma":[0.9971002,0.002085294,0.0001424375,0.0004875739,0.0001160704,0.00006837609],"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":[0.00001959086,0.00001991393,0.002548743,9.635864e-7,0.00007731952,0.000004412641,0.000140518,0.9912248,0.00001188354,0.0008745299,0.00298517,0.00209215],"study_design_scores_gemma":[0.0001231986,0.00002536317,0.001071407,5.047909e-7,0.0001987705,0.000001954112,0.0001902419,0.9687369,0.00001388626,0.01548929,0.01404642,0.0001020212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04729387,0.00006494339,0.9516891,0.00009576057,0.0002177758,0.0001516852,0.0001936578,0.00004991734,0.0002432736],"genre_scores_gemma":[0.9686213,0.000007014304,0.03017228,0.00004955681,0.00005155417,0.00003332865,0.000007752553,0.000007898433,0.001049384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9215168,"threshold_uncertainty_score":0.3575229,"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."}}