{"id":"W4380739491","doi":"10.1139/cgj-2022-0671","title":"Reliability assessment of rainfall-induced slope stability using Chebyshev–Galerkin–KL expansion and Bayesian approach","year":2023,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Chebyshev polynomials; Galerkin method; Eigenfunction; Applied mathematics; Reliability (semiconductor); Stability (learning theory); Fredholm integral equation; Landslide; Discretization; Chebyshev filter; Slope stability; Geotechnical engineering; Mathematical optimization; Eigenvalues and eigenvectors; Integral equation; Mathematical analysis; Geology; Computer science; Engineering; Structural engineering; Finite element method","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001712953,0.0005757652,0.0004637278,0.001425078,0.0002040844,0.0005022003,0.0005656756,0.0006253413,0.0006084531],"category_scores_gemma":[0.003846777,0.0003648945,0.0006299167,0.0006580139,0.0004690813,0.0008284991,0.0006082098,0.0004656334,0.0001337414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005730991,"about_ca_system_score_gemma":0.0006334161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00451187,"about_ca_topic_score_gemma":0.003326231,"domain_scores_codex":[0.9991822,0.0003186515,0.00004352154,0.0001164279,0.0002811205,0.00005822161],"domain_scores_gemma":[0.9984321,0.0008873921,0.0002161874,0.00009117811,0.0003365728,0.00003654075],"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":[0.00006165082,0.00003323136,0.004022514,0.00005274994,0.00003305392,0.00007725713,0.00006846169,0.9551528,0.005151588,0.005192176,0.0001793501,0.0299752],"study_design_scores_gemma":[0.000001425647,0.00001146651,0.00104556,0.000003202804,0.000003907718,0.00001371701,0.00000548052,0.9974062,0.0005638392,0.0008843883,0.00005390895,0.000006892479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1110671,0.0001828062,0.886948,0.00007296725,0.000009258625,0.00003831341,0.00007003084,0.0001674701,0.001444162],"genre_scores_gemma":[0.9684655,0.0001360119,0.03078389,0.00001202768,0.00001012919,0.00004262192,0.00009151991,0.00001883611,0.0004393888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00451187,"threshold_uncertainty_score":0.009059072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02200877144326927,"score_gpt":0.2412316992879668,"score_spread":0.2192229278446975,"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."}}