{"id":"W4384519197","doi":"10.1109/tcsii.2023.3295805","title":"A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Polynomial chaos; Sensitivity (control systems); Decorrelation; Covariance; Analysis of covariance; Polynomial; Computer science; Mathematics; Contrast (vision); Statistics; Monte Carlo method; Engineering; Artificial intelligence; Electronic engineering","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":[0.003022998,0.001083868,0.001106445,0.00107961,0.0003101451,0.0008860869,0.0007502317,0.000768733,0.001000978],"category_scores_gemma":[0.006891198,0.0003120138,0.001143567,0.0007776883,0.0007170564,0.001306446,0.00108798,0.001342702,0.0001564993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005366949,"about_ca_system_score_gemma":0.0005664707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002652219,"about_ca_topic_score_gemma":0.001514108,"domain_scores_codex":[0.998315,0.0009269941,0.00006287682,0.0001725953,0.0004598129,0.00006273181],"domain_scores_gemma":[0.9951258,0.003824241,0.0001801952,0.0002865335,0.000522692,0.00006053093],"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":[0.00007816543,0.00003572223,0.0006200599,0.0001477018,0.0001136757,0.00004531048,0.00009630875,0.894839,0.002480695,0.02580585,0.0003323195,0.07540522],"study_design_scores_gemma":[0.000001564134,0.00002035148,0.0001145225,0.000004788711,0.000007599009,0.000007212741,0.000005397113,0.9973078,0.0003656165,0.001940703,0.0002181538,0.000006282731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01138379,0.0006813052,0.9859608,0.00007730308,0.00002874842,0.00002404445,0.00002212647,0.0001243232,0.001697481],"genre_scores_gemma":[0.7504218,0.001450771,0.2453332,0.00008321951,0.0001430142,0.0001365627,0.0001430158,0.0002813828,0.002007054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003022998,"threshold_uncertainty_score":0.01598734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08377796114882108,"score_gpt":0.3814924838225168,"score_spread":0.2977145226736957,"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."}}