{"id":"W2887378550","doi":"10.1109/mwsym.2018.8439360","title":"A Combline Tunable Filter with Loss Compensation Circuit","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Compensation (psychology); Bandwidth (computing); Insertion loss; Electronic circuit; Electronic engineering; Filter (signal processing); Active filter; Electronic filter; Materials science; Computer science; Electrical engineering; Optoelectronics; Engineering; Telecommunications; Voltage","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":[0.0002302855,0.0004023285,0.0003641465,0.0003978031,0.0003545716,0.000532502,0.001045258,0.001000884,0.001549915],"category_scores_gemma":[0.0003090535,0.0002290452,0.0002970423,0.000272769,0.0002297112,0.0008386872,0.0002181079,0.0003234198,0.0009067044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004888871,"about_ca_system_score_gemma":0.0002110752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005838227,"about_ca_topic_score_gemma":0.0007630941,"domain_scores_codex":[0.9997174,0.00002282851,0.00001304609,0.00009698902,0.0001156055,0.00003416597],"domain_scores_gemma":[0.9997823,0.00003895129,0.00005537135,0.00002835089,0.00007696412,0.00001798812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001532738,0.0000422015,0.0003533134,0.00006270118,0.00002202074,0.0001410552,0.00002635593,0.0005491176,0.9778962,0.0008270146,0.0005233013,0.01940339],"study_design_scores_gemma":[0.00009936773,0.0007362622,0.002466758,0.00001606543,0.00008680532,0.001427048,0.00001318683,0.03050825,0.9439396,0.000214656,0.02044487,0.00004726361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5601474,0.002199664,0.4166975,0.001084705,0.0005170403,0.0001545226,0.0004085992,0.004937654,0.01385281],"genre_scores_gemma":[0.8091208,0.0005156159,0.1780437,0.0004261748,0.0002691629,0.0001097937,0.0002975359,0.0001553524,0.0110618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001549915,"threshold_uncertainty_score":0.005185008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619183058494789,"score_gpt":0.2118880445369085,"score_spread":0.1956962139519607,"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."}}