{"id":"W4388430537","doi":"10.1109/tcsi.2023.3328807","title":"MMSE Equalizer Design Optimization for Wireline SerDes Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Advancements in PLL and VCO Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jitter; Wireline; Computer science; Noise (video); Sampling (signal processing); Control theory (sociology); Electronic engineering; Filter (signal processing); Engineering; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004407518,0.0007431336,0.0004984611,0.0003180499,0.0002476386,0.0008087739,0.0004926848,0.0006426647,0.002996089],"category_scores_gemma":[0.001124235,0.0003988998,0.0004539542,0.0003005579,0.0002581489,0.000651836,0.0004229241,0.0006115955,0.0009309399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008274907,"about_ca_system_score_gemma":0.0006174119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002129746,"about_ca_topic_score_gemma":0.003981964,"domain_scores_codex":[0.9997041,0.00007155881,0.00001487989,0.00005393774,0.0001283354,0.00002718281],"domain_scores_gemma":[0.9997031,0.0001178935,0.00004391318,0.00002267474,0.0001066374,0.000005795266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006411848,0.00002212908,0.000608224,0.0001319911,0.00003869685,0.00008319996,0.00009608229,0.891089,0.01932565,0.01834586,0.001521664,0.06867355],"study_design_scores_gemma":[0.000004898819,0.00004359945,0.0001077057,0.00001521254,0.00001242502,0.00003115576,0.00001307585,0.9883273,0.007000327,0.001456507,0.002981998,0.000005808466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005202583,0.0001939496,0.9919074,0.00005302567,0.0000127625,0.00001705907,0.00003424195,0.0002213612,0.002357599],"genre_scores_gemma":[0.5831833,0.001485654,0.3979158,0.0001292123,0.00006961773,0.0001980901,0.0002050117,0.0002205502,0.0165928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002996089,"threshold_uncertainty_score":0.01002294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03631792767737092,"score_gpt":0.2513528392195509,"score_spread":0.21503491154218,"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."}}