{"id":"W2106831600","doi":"10.1109/tmag.2006.871573","title":"Multiobjective approaches for robust electromagnetic design","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Compromise; Multi-objective optimization; Mathematical optimization; Optimal design; Robustness (evolution); Decision maker; Operations research; Mathematics; Machine learning","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":[0.0001545954,0.0002905945,0.0002154388,0.0002560422,0.0003376532,0.0001272332,0.0004577821,0.0001256004,0.00002516496],"category_scores_gemma":[0.0000125333,0.0003098221,0.0001329201,0.0006118429,0.00009947372,0.0003360101,0.000002752166,0.000224106,0.00003188112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001531259,"about_ca_system_score_gemma":0.00008997575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001705827,"about_ca_topic_score_gemma":0.00002007427,"domain_scores_codex":[0.9982134,0.0001059782,0.0003101971,0.000641234,0.0002644983,0.0004647326],"domain_scores_gemma":[0.9987093,0.0003744419,0.0001039109,0.0005052578,0.0002188417,0.00008826965],"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":[0.00003553234,0.0003658892,5.583251e-7,0.000007628034,0.00001346356,0.000002336329,0.0001129935,0.9281704,0.0009850176,0.001082332,0.0001074838,0.06911632],"study_design_scores_gemma":[0.001244339,0.0009404215,0.00008807804,0.000007116556,0.00003096388,0.00001791528,0.00002690024,0.9528549,0.04179801,0.002442249,0.0001892805,0.000359821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000610391,0.00007348333,0.997031,0.0002180998,0.000422651,0.001150515,0.00002089688,0.0003780204,0.0006442916],"genre_scores_gemma":[0.08752286,0.00002087795,0.90953,0.00009710528,0.00006576985,0.0004098768,0.000003951951,0.00004324197,0.002306337],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08750103,"threshold_uncertainty_score":0.9999354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04258719587666003,"score_gpt":0.2351894240415399,"score_spread":0.1926022281648799,"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."}}