{"id":"W4387081103","doi":"10.1016/j.strusafe.2023.102391","title":"Soft Monte Carlo Simulation for imprecise probability estimation: A dimension reduction-based approach","year":2023,"lang":"en","type":"article","venue":"Structural Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Monte Carlo method; Random variable; Mathematical optimization; Mathematics; Dimension (graph theory); Benchmark (surveying); Reduction (mathematics); Interval (graph theory); Univariate; Upper and lower bounds; Probability distribution; Dimensionality reduction; Applied mathematics; Algorithm; Computer science; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003197882,0.00109082,0.002743607,0.002095512,0.0008534453,0.002086849,0.002378906,0.001787155,0.003576627],"category_scores_gemma":[0.0169073,0.001495011,0.002107399,0.001386678,0.002120812,0.002174184,0.003203187,0.002882002,0.0007489655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296669,"about_ca_system_score_gemma":0.00155631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004126523,"about_ca_topic_score_gemma":0.003207867,"domain_scores_codex":[0.9975532,0.001145455,0.0001041615,0.0002402905,0.000820443,0.0001365161],"domain_scores_gemma":[0.9853975,0.01125168,0.0007160961,0.001393284,0.001032689,0.0002087804],"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":[0.00002905028,0.00001985048,0.0002098119,0.00002694643,0.00003897294,0.00002783539,0.00002451669,0.9706702,0.000404608,0.02044944,0.0001668435,0.007932054],"study_design_scores_gemma":[0.000001839356,0.000003711992,0.00001843909,0.000003455907,0.000003981022,0.000005962548,0.000001606279,0.9935034,0.0001055673,0.00626258,0.00008641803,0.000003077525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002134886,0.00005699665,0.996877,0.00007017751,0.00001051747,0.00001780975,0.00001915992,0.0001075829,0.0007059759],"genre_scores_gemma":[0.48552,0.0004196312,0.5094495,0.0003112197,0.0001565499,0.0004258002,0.0002578316,0.0003723718,0.0030871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004126523,"threshold_uncertainty_score":0.01691222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09070345815118433,"score_gpt":0.3525732052945648,"score_spread":0.2618697471433805,"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."}}