{"id":"W2100358079","doi":"10.1109/tmag.2009.2022492","title":"Improved Sequential Optimization Method for High Dimensional Electromagnetic Device Optimization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Benchmark (surveying); Dimension (graph theory); Optimization problem; Mathematical optimization; Reduction (mathematics); Engineering optimization; Algorithm; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002163752,0.0003507851,0.0002807383,0.0003270361,0.0004090444,0.0001700876,0.0004638606,0.0001879763,0.0001404285],"category_scores_gemma":[0.00002989679,0.0003853551,0.0001342826,0.0008401825,0.00004409435,0.0006021808,0.000004080347,0.0002553027,0.0000081579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001787811,"about_ca_system_score_gemma":0.0001399167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001032847,"about_ca_topic_score_gemma":0.000005261064,"domain_scores_codex":[0.9977782,0.0001438983,0.000479124,0.000760405,0.0003590638,0.0004792895],"domain_scores_gemma":[0.9982832,0.0002180567,0.0001944606,0.0005556209,0.0005825959,0.0001660514],"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":[0.00005604125,0.0002541749,5.209749e-8,0.000005926253,0.00001669812,0.000001717928,0.00005885996,0.9306285,0.005656048,0.0005855516,0.00002822668,0.06270822],"study_design_scores_gemma":[0.001679533,0.001603565,0.000007951992,0.00001152956,0.00006412293,0.00002365982,0.00000615927,0.9609707,0.03464973,0.0005452442,0.0000384328,0.0003993507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003287298,0.00003417127,0.9965984,0.0009912002,0.0008101668,0.0009970615,0.00003275638,0.000407385,0.00009602927],"genre_scores_gemma":[0.02533337,0.0000510873,0.9728056,0.0009459713,0.00007901954,0.0001137055,0.00003508307,0.00004100442,0.0005951586],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06230887,"threshold_uncertainty_score":0.9998598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282474916389352,"score_gpt":0.2773621062419217,"score_spread":0.2645373570780282,"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."}}