{"id":"W2108362592","doi":"10.1007/s00158-004-0498-5","title":"Variable chromosome length genetic algorithm for progressive refinement in topology optimization","year":2005,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Topology optimization; Chromosome; Algorithm; Topology (electrical circuits); Genetic algorithm; Context (archaeology); Computer science; Mathematical optimization; Filter (signal processing); Mathematics; Engineering; Genetics; Biology; Finite element method; Combinatorics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006801181,0.0002524919,0.000239063,0.0002020825,0.0001690052,0.00003252474,0.0001031845,0.0001791707,0.000156927],"category_scores_gemma":[0.00001845547,0.0002556601,0.00002927741,0.0002454943,0.00007443261,0.0003355514,0.00005798562,0.0001271543,0.000001055438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001322812,"about_ca_system_score_gemma":0.00001823192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003943318,"about_ca_topic_score_gemma":0.000004376161,"domain_scores_codex":[0.9988078,0.00002222033,0.0004006074,0.0003218046,0.0000888561,0.0003587123],"domain_scores_gemma":[0.999594,0.00004222706,0.00006248598,0.0001514535,0.00007335456,0.00007642423],"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":[0.00001069048,0.00001138322,0.0001176225,0.00005818034,0.0000207063,0.000001904679,0.0002973772,0.9865918,0.00008144591,0.0003158839,0.00002410072,0.01246888],"study_design_scores_gemma":[0.001179551,0.00007481649,0.0005938736,0.00002541706,0.00003033683,0.00003948762,0.00008596072,0.9972568,0.0001859228,0.0001499933,0.00008536594,0.0002925199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0134559,0.0009744464,0.9836121,0.000188967,0.0005595128,0.0007275191,0.00004435166,0.0002730839,0.0001641667],"genre_scores_gemma":[0.06514408,0.0002034873,0.9337859,0.0000176522,0.0002498207,0.000205964,0.0002587398,0.00005168443,0.00008264636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05168819,"threshold_uncertainty_score":0.9999896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005988046633527588,"score_gpt":0.235775527333375,"score_spread":0.2297874806998474,"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."}}