{"id":"W3015476812","doi":"10.1109/lssc.2020.2986645","title":"Wideband LNA Noise Matching","year":2020,"lang":"en","type":"article","venue":"IEEE Solid-State Circuits Letters","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; CMC Microsystems","keywords":"Cascode; Noise figure; Wideband; Electronic engineering; Low-noise amplifier; CMOS; Noise (video); Electrical engineering; Computer science; Amplifier; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001575453,0.0005101584,0.0004904341,0.0001639732,0.0001172508,0.0001870078,0.0005750997,0.0001273838,0.000108415],"category_scores_gemma":[0.00003974001,0.0005609193,0.0001821874,0.0005513191,0.00009860096,0.0004864876,0.000004268712,0.0007051409,0.0008488158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002359314,"about_ca_system_score_gemma":0.00004258018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004643276,"about_ca_topic_score_gemma":0.00000654311,"domain_scores_codex":[0.9974988,0.00007569345,0.0005809889,0.0005419403,0.0004128957,0.0008897185],"domain_scores_gemma":[0.998882,0.00009821835,0.0000763017,0.0004440264,0.00005046887,0.0004489625],"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":[0.00000256823,0.000007694478,0.0001035301,0.0001018502,0.0001222821,0.0003017109,0.002405222,0.1624921,0.8100905,0.00004186737,0.0216425,0.002688223],"study_design_scores_gemma":[0.001720459,0.0001073644,0.00107392,0.0002313041,0.0001378723,0.0001700318,0.000276241,0.01420768,0.9687073,0.00119499,0.009822682,0.002350216],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8447106,0.0004447646,0.1300746,0.00221798,0.002324986,0.0005926759,0.000103533,0.002517739,0.01701313],"genre_scores_gemma":[0.9889779,0.00007018405,0.00005285813,0.01013856,0.0004496946,0.0000418979,0.00001729348,0.0002060997,0.00004548543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1586168,"threshold_uncertainty_score":0.9999291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933518711857521,"score_gpt":0.2189729011022512,"score_spread":0.199637713983676,"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."}}