{"id":"W4387431552","doi":"10.1016/j.probengmech.2023.103544","title":"Polynomial response surface-based transformation function for the performance improvement of low-fidelity models for concrete gravity dams","year":2023,"lang":"en","type":"article","venue":"Probabilistic Engineering Mechanics","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Transformation (genetics); Fidelity; Probabilistic logic; Nonlinear system; Function (biology); Range (aeronautics); Computer science; Finite element method; Seismic loading; Structural engineering; Engineering; Mathematical optimization; Algorithm; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001616083,0.0002559642,0.0002757725,0.00009527423,0.00009813497,0.00002474755,0.0002016169,0.0001261015,0.00000168486],"category_scores_gemma":[0.0002107129,0.0002307436,0.0001609591,0.0003062455,0.00001299055,0.0001494076,0.00001604896,0.0001560281,0.00000193401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001675452,"about_ca_system_score_gemma":0.00005471471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004181129,"about_ca_topic_score_gemma":0.000001409838,"domain_scores_codex":[0.9986545,0.00001217043,0.0005034607,0.0002062854,0.0002065656,0.0004169533],"domain_scores_gemma":[0.9986414,0.0007408552,0.0000530718,0.0003510657,0.000143055,0.0000704916],"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":[0.0003637371,0.00000479163,3.733334e-7,0.001677349,0.00004764086,7.318182e-8,0.0001143078,0.9151886,0.0798942,0.001102943,0.0001089497,0.001497004],"study_design_scores_gemma":[0.0009007096,0.0002662731,0.00004284107,0.00008360812,0.00007205251,4.961523e-7,0.00002095697,0.9683071,0.02906718,0.0002797915,0.0007153978,0.000243594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2668469,0.00005432685,0.7301481,0.00004781695,0.000866434,0.001224425,0.0002295438,0.0005789707,0.000003408763],"genre_scores_gemma":[0.9956606,0.00003427717,0.003565111,0.000009860705,0.00006547054,0.0004769952,0.0001008026,0.00007405931,0.00001283692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7288136,"threshold_uncertainty_score":0.940945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149601652574281,"score_gpt":0.2126445871528879,"score_spread":0.1976844218954598,"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."}}