{"id":"W4413883363","doi":"10.1016/j.ress.2025.111588","title":"Systematic investigation on surrogate and active learning-based multivariate seismic fragility analysis under multiple sources of uncertainties","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Nova; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Fragility; Multivariate statistics; Multivariate analysis; Computer science; Surrogate model; Econometrics; Reliability engineering; Forensic engineering; Statistics; Engineering; Data mining; Machine learning; Mathematics; Chemistry","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":[0.004945252,0.0007998911,0.0008651373,0.0008647664,0.0002746583,0.001098098,0.0011766,0.001098195,0.0009890689],"category_scores_gemma":[0.01020797,0.0003939726,0.0008567579,0.0005900928,0.001236274,0.002063448,0.001481469,0.001348762,0.0001602971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004149559,"about_ca_system_score_gemma":0.0008496321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005437033,"about_ca_topic_score_gemma":0.0005050895,"domain_scores_codex":[0.9985267,0.0007063876,0.0000659612,0.0001660892,0.0004708321,0.00006413348],"domain_scores_gemma":[0.9946091,0.004019164,0.0003619334,0.0004286705,0.0005070167,0.00007403906],"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":[0.00004450812,0.00004818147,0.001262971,0.0003137331,0.00006311508,0.00008963073,0.00009649983,0.860648,0.005652824,0.06103644,0.000334464,0.07040957],"study_design_scores_gemma":[0.000001730528,0.0000318838,0.0001500199,0.00002552222,0.000007651439,0.00002640569,0.00001149763,0.9880255,0.001671938,0.009387171,0.0006539504,0.000006728626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006438249,0.000274333,0.9923744,0.0001007431,0.000008505271,0.00001338179,0.00001617092,0.00005665129,0.000717466],"genre_scores_gemma":[0.6673377,0.002018411,0.3286355,0.0001154288,0.00009894905,0.0001982811,0.0001910479,0.00009751435,0.001307182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004945252,"threshold_uncertainty_score":0.02615327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005630162392156093,"score_gpt":0.1943206994108465,"score_spread":0.1886905370186904,"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."}}