{"id":"W2508741751","doi":"10.1109/mwsym.2016.7539995","title":"Fast yield estimation and optimization of microwave filters using a cognition-driven formulation of space mapping","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Carleton University","funders":"","keywords":"Filter (signal processing); Algorithm; Feature (linguistics); Computer science; Yield (engineering); Monte Carlo method; Ripple; Mathematical optimization; Mathematics; Microwave; Statistics; Power (physics); Physics","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":[0.0005827489,0.0006192287,0.0003207288,0.0002846224,0.0002309804,0.0004355608,0.0004561133,0.0004154266,0.00102036],"category_scores_gemma":[0.001896108,0.0002370135,0.0004582269,0.0002565758,0.0005278954,0.0008357764,0.0004946273,0.0004935551,0.0001101926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005406479,"about_ca_system_score_gemma":0.0006266127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002372953,"about_ca_topic_score_gemma":0.002411696,"domain_scores_codex":[0.9998176,0.00005399879,0.000009215506,0.00003536338,0.00006764032,0.00001618],"domain_scores_gemma":[0.999586,0.0002387272,0.00005090076,0.00003523853,0.00007966338,0.000009416358],"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":[0.00005405403,0.00002917704,0.0006179452,0.00005421066,0.00003298951,0.00004815865,0.00009450963,0.8811706,0.01293532,0.04359106,0.0003128427,0.06105918],"study_design_scores_gemma":[0.00000283022,0.00001803395,0.0001052609,0.000001951177,0.000003798936,0.000007908283,0.000004494892,0.9932185,0.001618182,0.004796985,0.0002184327,0.000003611627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006343499,0.00001929633,0.9930387,0.00002607917,0.000003947358,0.000006953253,0.000004920323,0.00003655342,0.0005200119],"genre_scores_gemma":[0.6153162,0.0001332901,0.381631,0.00006996904,0.00002323259,0.0001348871,0.00004848038,0.00006872691,0.002574292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002372953,"threshold_uncertainty_score":0.004718363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02618513602420175,"score_gpt":0.2344256644858106,"score_spread":0.2082405284616089,"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."}}