{"id":"W1978289729","doi":"10.1016/j.ress.2013.07.010","title":"An effective approximation for variance-based global sensitivity analysis","year":2013,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":109,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiplicative function; Variance-based sensitivity analysis; Sensitivity (control systems); Computation; Applied mathematics; Mathematics; Quadrature (astronomy); Variance (accounting); Gaussian; Variance reduction; Function (biology); Product (mathematics); Algorithm; Algebraic number; Mathematical optimization; Computer science; Monte Carlo method; Statistics; Mathematical analysis; One-way analysis of variance; Analysis of variance","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007807939,0.0003561433,0.0008661256,0.0002455291,0.0001758882,0.0002604289,0.0004418177,0.0002316228,0.0000266505],"category_scores_gemma":[0.004608059,0.0002836053,0.000463866,0.002086337,0.00005294858,0.0004971368,0.00003250839,0.0001505458,0.0000847901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000926598,"about_ca_system_score_gemma":0.00008594969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002165107,"about_ca_topic_score_gemma":0.000009195967,"domain_scores_codex":[0.9961267,0.00047258,0.001024572,0.0010052,0.0008720697,0.0004988888],"domain_scores_gemma":[0.9934312,0.003632995,0.0002269071,0.001508196,0.0009073699,0.0002933513],"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":[0.00004153095,0.00007039006,0.003067918,0.0002051339,0.0001078967,0.000001260938,0.00004239408,0.9873576,0.0005089333,0.005460924,0.00005815567,0.003077921],"study_design_scores_gemma":[0.0003540355,0.00007844096,0.1271376,0.0000414475,0.0001660288,0.000003606237,0.00004769463,0.8710367,0.0001297861,0.0005614022,0.0001597811,0.0002834926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04576379,0.00002421618,0.9505975,0.0001075708,0.0005396128,0.002118462,0.0001611913,0.000553974,0.0001336466],"genre_scores_gemma":[0.93349,2.545763e-7,0.06586377,0.00001360805,0.0001300595,0.0004225873,0.00004121203,0.00002165282,0.00001681828],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8877262,"threshold_uncertainty_score":0.9999616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539928512278174,"score_gpt":0.2755507661292996,"score_spread":0.2601514810065179,"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."}}