{"id":"W4289890443","doi":"10.1007/s00158-022-03332-8","title":"An outer approximation bi-level framework for mixed categorical structural optimization problems","year":2022,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Categorical variable; Mathematical optimization; Optimization problem; Computation; Continuous optimization; Mathematics; Truss; Dimension (graph theory); Computer science; Algorithm; Engineering; Multi-swarm optimization; Structural engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003219886,0.001258715,0.00188885,0.001616016,0.0007442744,0.002773191,0.002837075,0.001764537,0.006184403],"category_scores_gemma":[0.006254371,0.0008941316,0.001790792,0.001226543,0.00161401,0.003040064,0.005454399,0.004160326,0.001620283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123742,"about_ca_system_score_gemma":0.001174125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002393464,"about_ca_topic_score_gemma":0.003144173,"domain_scores_codex":[0.9982246,0.0007908701,0.00007280648,0.0001560069,0.0006154259,0.0001402828],"domain_scores_gemma":[0.997855,0.0009003358,0.0001504784,0.0003629951,0.0004763558,0.0002547064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007794455,0.00009769792,0.0004948236,0.0001617216,0.00006677183,0.00006874222,0.0001157228,0.3092296,0.002330389,0.6533483,0.002918071,0.03109031],"study_design_scores_gemma":[0.000005333639,0.00002202762,0.00004205163,0.00001281215,0.00000940089,0.00001088625,0.00001195981,0.9039323,0.0002090844,0.093877,0.001860603,0.000006434444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001859878,0.00009720974,0.9949386,0.0001023985,0.00002956454,0.00001284831,0.00003685793,0.00009618511,0.002826395],"genre_scores_gemma":[0.1935135,0.0004267304,0.7898739,0.0004798691,0.0002115139,0.0003028733,0.0005692512,0.0007362288,0.01388596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006184403,"threshold_uncertainty_score":0.02068889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715035759324255,"score_gpt":0.2567526711958639,"score_spread":0.2396023136026213,"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."}}