{"id":"W1971592988","doi":"10.1109/csics.2006.319954","title":"Methodology for Simultaneous Noise and Impedance Matching in W-Band LNAs","year":2006,"lang":"en","type":"article","venue":"","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"CMOS; Impedance matching; Dissipation; Electronic engineering; Electrical impedance; Noise figure; Noise (video); Matching (statistics); Electrical engineering; Low-noise amplifier; Computer science; Engineering; Amplifier; Physics; Mathematics; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0002457805,0.0001312499,0.0001971037,0.0000698865,0.00002183769,0.00002211975,0.00007723844,0.0001004536,0.00001488013],"category_scores_gemma":[0.00009313762,0.0001247771,0.00002535849,0.000106144,0.00002453242,0.00007562406,8.096285e-7,0.0001207835,0.000005087334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006840723,"about_ca_system_score_gemma":0.000009603602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004669647,"about_ca_topic_score_gemma":0.0005067866,"domain_scores_codex":[0.9992735,0.00003760582,0.0002149418,0.0001657042,0.00004535682,0.0002628849],"domain_scores_gemma":[0.9989268,0.0008928712,0.00001479559,0.0001160832,0.0000194861,0.00002998109],"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":[0.00001257077,0.00001166816,0.0004352704,0.000088776,0.00001791817,0.00005216331,0.0002729366,0.3048234,0.6683591,0.01225747,0.0005638402,0.01310489],"study_design_scores_gemma":[0.001533118,0.0001099014,0.0008896731,0.00006447388,0.00003273033,0.0002111483,0.000279215,0.296565,0.5698571,0.1274821,0.002208645,0.0007669644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6606147,0.001310065,0.3100151,0.00004326965,0.0002136194,0.0003327184,0.000009184825,0.0003018064,0.02715961],"genre_scores_gemma":[0.9860845,0.00002907429,0.01341403,0.00003812751,0.00005757809,0.00003856604,0.000003476911,0.0000325236,0.0003021766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3254698,"threshold_uncertainty_score":0.5088261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181645962736187,"score_gpt":0.2513858889991699,"score_spread":0.229569429371808,"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."}}