{"id":"W2039188123","doi":"10.6112/kscfe.2012.17.4.032","title":"A STUDY ON CONSTRAINED EGO METHOD FOR NOISY CFD DATA","year":2012,"lang":"en","type":"article","venue":"Journal of computational fluids engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Kriging; Mathematical optimization; Global optimization; Interpolation (computer graphics); Computer science; Function (biology); Metamodeling; Mathematics; Algorithm; Artificial intelligence; Machine learning; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.001030398,0.0001547937,0.0002468766,0.0002781949,0.00006703168,0.00006918123,0.0007332277,0.00003269489,0.00000329204],"category_scores_gemma":[0.0004662504,0.0001447151,0.00006782089,0.0002970822,0.00001044271,0.001152748,0.0001478701,0.0001738919,0.000003694825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008956057,"about_ca_system_score_gemma":0.00009639932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.43355e-7,"about_ca_topic_score_gemma":4.920172e-8,"domain_scores_codex":[0.9986163,0.00005257626,0.0004693317,0.000188268,0.0004387556,0.0002347197],"domain_scores_gemma":[0.9979871,0.0009674089,0.0001899088,0.0002813969,0.0004214674,0.0001527494],"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":[0.00001300274,0.0002353954,0.00008155443,0.000007373746,0.00007625346,0.000007882181,0.0004826216,0.984314,0.0003429815,0.008623273,0.0001434246,0.005672216],"study_design_scores_gemma":[0.001322603,0.0002743153,0.002560489,0.00002285598,0.00001919526,0.0001782438,0.00007509011,0.9942735,0.000218231,0.0004154832,0.0004845323,0.0001554882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001353401,0.00008204322,0.9971852,0.0001783297,0.0008412597,0.0002577947,0.00001512806,0.00005043121,0.00003647097],"genre_scores_gemma":[0.2020066,0.000001064363,0.7975659,0.00007639583,0.0003117755,0.000005500741,0.000007047911,0.00001560398,0.0000101109],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2006532,"threshold_uncertainty_score":0.5901312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04870646548112034,"score_gpt":0.3532155430837276,"score_spread":0.3045090776026073,"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."}}