{"id":"W4295962519","doi":"10.1007/s00158-022-03363-1","title":"Multi material topology and stacking sequence optimization of composite laminated plates","year":2022,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Topology optimization; Lamination; Stacking; Topology (electrical circuits); Stiffness; Material selection; Isotropy; Sequence (biology); Process (computing); Computer science; Composite number; Materials science; Structural engineering; Mathematical optimization; Algorithm; Engineering; Finite element method; Composite material; 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":[0.00007467726,0.0001884851,0.0002198695,0.0001676646,0.000352872,0.00002266945,0.00009251533,0.0000786649,0.0001713158],"category_scores_gemma":[0.00001062428,0.0002023412,0.00001967942,0.0001992612,0.0001445876,0.0002536154,0.000199189,0.0001413713,1.167407e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005317635,"about_ca_system_score_gemma":0.00000850943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000126913,"about_ca_topic_score_gemma":0.000001724382,"domain_scores_codex":[0.9991108,0.00005125888,0.0003100566,0.0002302821,0.00009935094,0.0001982563],"domain_scores_gemma":[0.9996709,0.00003670423,0.00008202637,0.0001095254,0.00004849505,0.00005236566],"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":[0.0000338986,0.000005632244,0.001328805,0.00008367664,0.00002328511,0.000003646819,0.0008180095,0.9863716,0.01095376,0.0001197952,0.000002818244,0.0002551036],"study_design_scores_gemma":[0.0005796537,0.00006637823,0.002394508,0.00001075638,0.00003056271,0.00008995731,0.0003824415,0.9945647,0.001633185,0.00003429293,0.000003739852,0.0002098938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8149102,0.0002435608,0.1833324,0.00005426456,0.0007455499,0.0002620142,0.0001634108,0.000249327,0.00003928516],"genre_scores_gemma":[0.8148831,0.0001269226,0.184547,0.000004704298,0.00002444838,0.00002151885,0.0003418289,0.00002996929,0.0000205936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009320569,"threshold_uncertainty_score":0.8251236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053475596974553,"score_gpt":0.2389604757042681,"score_spread":0.2284257197345225,"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."}}