{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001123805,0.00008602121,0.0001359454,0.0003740619,0.00001485873,0.00001440616,0.00009841923,0.00005468441,0.00000374706],"category_scores_gemma":[0.00001928461,0.00008071678,0.00002270653,0.0002556047,0.00001327331,0.00008355572,0.000006502836,0.0002659806,6.907052e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001047042,"about_ca_system_score_gemma":0.00002581229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003726532,"about_ca_topic_score_gemma":0.0002155889,"domain_scores_codex":[0.9993927,0.00000228446,0.000209537,0.0000521186,0.00005242962,0.0002908665],"domain_scores_gemma":[0.9996844,0.00006074784,0.00001715206,0.00004075074,0.00002032581,0.0001766164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001974438,0.00001878392,0.008982364,0.00004916378,0.0001266508,0.001489185,0.0004701895,0.3193658,0.0008629064,0.02121378,0.001045479,0.6463559],"study_design_scores_gemma":[0.001170746,0.0006015944,0.0398019,0.0002356979,0.00001917893,0.0006047947,0.00006627383,0.9177437,0.003332578,0.002478052,0.03323274,0.0007127703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2220007,0.002812307,0.7745253,0.00002210624,0.0003454052,0.00003705126,6.813326e-7,0.00004201286,0.0002144387],"genre_scores_gemma":[0.9904057,0.00004783897,0.009400935,0.00002506565,0.0001045239,4.417144e-7,1.408754e-7,0.00000994323,0.000005345358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.768405,"threshold_uncertainty_score":0.3291535,"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."}}