{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003409637,0.00007270924,0.0001017989,0.0001086025,0.00001379813,0.000003876208,0.00002677991,0.00003994914,0.00001819823],"category_scores_gemma":[0.00003991165,0.00006299795,0.00001688747,0.00007672398,0.00002468887,0.000292053,0.00001881905,0.00002030121,2.757832e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003131488,"about_ca_system_score_gemma":0.000003651439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000459166,"about_ca_topic_score_gemma":0.000002107629,"domain_scores_codex":[0.9996197,0.000005037533,0.0001732814,0.00007443305,0.00005634339,0.00007116988],"domain_scores_gemma":[0.9997206,0.00005815327,0.00007030044,0.00007261662,0.00006236988,0.00001599457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003473791,0.000002593152,0.00008115134,0.00005101394,0.000008439186,8.992937e-8,0.00007378182,0.3890213,0.6059719,0.0002885874,0.000005390876,0.004492285],"study_design_scores_gemma":[0.00009980034,0.00001509988,0.0001872271,0.0003049225,0.000006455515,0.000001947137,0.00002256146,0.6377373,0.3611247,0.0004383991,0.000001849352,0.00005976365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08845091,0.000008015989,0.9109097,0.00001055502,0.0000172584,0.0001259098,0.00001042025,0.0001141669,0.0003530448],"genre_scores_gemma":[0.5559762,0.00001037809,0.443989,0.000001279365,0.000003844568,0.000002028798,0.000003104677,0.000008336523,0.00000587419],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4675252,"threshold_uncertainty_score":0.2568982,"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."}}