{"id":"W3203700235","doi":"10.1109/aces53325.2021.00162","title":"Recent Advance in Neuro- Transfer Function-Assisted Yield-Driven EM Optimization","year":2021,"lang":"en","type":"article","venue":"2021 International Applied Computational Electromagnetics Society Symposium (ACES)","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Yield (engineering); Computer science; Process (computing); Component (thermodynamics); Transfer function; Engineering; Materials science; Physics; Electrical engineering","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":[0.0008279699,0.0008974723,0.0005700779,0.0003944433,0.0001733445,0.0007288987,0.0007311518,0.0008746661,0.001359505],"category_scores_gemma":[0.001993589,0.0003402708,0.0006104649,0.0004622333,0.000483181,0.0006994924,0.0006611018,0.0009140848,0.0004827059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004130295,"about_ca_system_score_gemma":0.0005108115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001084875,"about_ca_topic_score_gemma":0.0009234493,"domain_scores_codex":[0.9997531,0.00007300257,0.00001808221,0.00004194944,0.00009995003,0.00001390427],"domain_scores_gemma":[0.9991904,0.0004562018,0.00007044873,0.00005426237,0.0001985498,0.00003016241],"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":[0.00005623951,0.00003995333,0.0004095198,0.0001769814,0.00005706885,0.00005032846,0.00004195219,0.8816049,0.004618554,0.01047864,0.001244295,0.1012215],"study_design_scores_gemma":[0.000002624471,0.00002270956,0.00007651572,0.00001084835,0.000008504999,0.00001429684,0.000004752698,0.9938106,0.00133953,0.001801614,0.002902338,0.000005562521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01182392,0.004248949,0.9753706,0.0004663804,0.00008315888,0.00001724234,0.00003561133,0.0003052423,0.007648907],"genre_scores_gemma":[0.5198006,0.01142727,0.4577253,0.0006814659,0.000396265,0.0001672787,0.000342676,0.0004448598,0.009014385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001359505,"threshold_uncertainty_score":0.004547954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005399185673029076,"score_gpt":0.1894009583042979,"score_spread":0.1840017726312688,"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."}}