{"id":"W2138021345","doi":"10.1109/euma.2001.338979","title":"Yield-Driven EM Optimization using Space Mapping-Based Neuromodels","year":2001,"lang":"en","type":"article","venue":"","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; McMaster University","funders":"","keywords":"Space mapping; Electromagnetics; Yield (engineering); Filter (signal processing); Computational electromagnetics; Computer science; Space (punctuation); Microstrip; Microwave; Jacobian matrix and determinant; Mathematical optimization; Algorithm; Electronic engineering; Mathematics; Applied mathematics; Engineering; Physics; Telecommunications; Electromagnetic field","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":[0.0004476411,0.0005526119,0.0003073777,0.0002691181,0.0002280092,0.0003829397,0.0004205241,0.0003196747,0.001134641],"category_scores_gemma":[0.00110793,0.000246538,0.0003769913,0.0001777967,0.0003796161,0.0007006413,0.0004034219,0.0003571516,0.0002610576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006471817,"about_ca_system_score_gemma":0.0005768819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007281614,"about_ca_topic_score_gemma":0.00134882,"domain_scores_codex":[0.9998671,0.00003387547,0.000005087267,0.00001934685,0.00006367711,0.00001093277],"domain_scores_gemma":[0.9997582,0.0001321834,0.00003239702,0.00003203437,0.00003855469,0.00000664149],"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":[0.00002507458,0.00002444144,0.0002241238,0.00003727866,0.00001657452,0.00003122856,0.00003440145,0.9247453,0.02608451,0.02665455,0.000221127,0.02190143],"study_design_scores_gemma":[0.000002282933,0.00001770835,0.00005426907,0.000001435686,0.000002777001,0.000009833538,0.000005047727,0.9911124,0.004602529,0.003768509,0.0004199411,0.000003302038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0195746,0.00003388584,0.9782557,0.00005474636,0.000006606906,0.00001737265,0.00002408756,0.0002224376,0.001810607],"genre_scores_gemma":[0.6757376,0.0001348368,0.3204387,0.00005026599,0.00001402995,0.0001630072,0.00008974154,0.0001984781,0.003173485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001134641,"threshold_uncertainty_score":0.004695714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02849579963566717,"score_gpt":0.2064633333812002,"score_spread":0.177967533745533,"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."}}