{"id":"W1969215808","doi":"10.1109/cjece.2007.365502","title":"Adaptive equalizer in CMOS","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advancements in PLL and VCO Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"CMOS; Equalizer; Adaptive equalizer; SIGNAL (programming language); Channel (broadcasting); Computer science; Power (physics); Electronic engineering; Electrical engineering; Twisted pair; Process (computing); Engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001115027,0.0002327365,0.0001519982,0.0001848736,0.0002704353,0.0005347859,0.0003675687,0.0004353441,0.005070334],"category_scores_gemma":[0.0003301442,0.0001272921,0.0001677514,0.000216026,0.0001468355,0.0004936634,0.0002696282,0.0003935979,0.001487216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003953871,"about_ca_system_score_gemma":0.0002680792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425464,"about_ca_topic_score_gemma":0.002330281,"domain_scores_codex":[0.9998195,0.00001813144,0.00001092404,0.00004868856,0.00007175624,0.00003106131],"domain_scores_gemma":[0.9999125,0.00002416545,0.00001150281,0.000008680626,0.00003827052,0.000004863904],"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":[0.0004255322,0.00004577261,0.0008015899,0.0002921015,0.00005630911,0.0003931563,0.0002013614,0.02238084,0.582648,0.04340878,0.007785148,0.3415613],"study_design_scores_gemma":[0.0001321579,0.0005350439,0.001700285,0.00009403231,0.0001021636,0.001305008,0.0001110035,0.2419299,0.6191358,0.008622762,0.1262732,0.00005867233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08097109,0.002899288,0.8661807,0.0007859075,0.0004188406,0.0001447382,0.0002521026,0.004234765,0.04411252],"genre_scores_gemma":[0.7044576,0.001397843,0.2567362,0.0004688151,0.0001642884,0.000104505,0.0001750111,0.00009721031,0.03639854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005070334,"threshold_uncertainty_score":0.01696193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007408831477350351,"score_gpt":0.1862774366532636,"score_spread":0.1788686051759133,"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."}}