{"id":"W2163704728","doi":"10.1109/ccece.2005.1557399","title":"Performance evaluation of LDPC codes in the presence of ISI with application to 10Gbase-T ethernet","year":2006,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Low-density parity-check code; Additive white Gaussian noise; Computer science; Algorithm; Forward error correction; Serial concatenated convolutional codes; Concatenated error correction code; Turbo code; Theoretical computer science; Decoding methods; Electronic engineering; Computer network; Channel (broadcasting); Block code; 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":[],"consensus_categories":[],"category_scores_codex":[0.00174825,0.0000588216,0.00008114659,0.00008199754,0.00001811006,0.00001211315,0.0006294178,0.00002295229,0.000002824731],"category_scores_gemma":[0.00003995546,0.00003787629,0.00001121778,0.0005790078,0.00002943805,0.000167731,0.00005263618,0.00005179418,0.000002360372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002663276,"about_ca_system_score_gemma":0.00005193229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112998,"about_ca_topic_score_gemma":0.000947935,"domain_scores_codex":[0.9989138,0.0001154617,0.0001841154,0.0001698551,0.0005277729,0.00008897399],"domain_scores_gemma":[0.9990025,0.0001286474,0.0001102226,0.0005042191,0.0002459633,0.00000842466],"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":[0.00008218742,0.0006171137,0.2514209,0.0001216364,0.00001165394,6.98968e-7,0.009612416,0.1181642,0.07311638,0.05844564,0.002707401,0.4856998],"study_design_scores_gemma":[0.0001388342,0.0002501994,0.09280195,0.00007041124,0.00000558598,0.000003009058,0.00008197111,0.7580706,0.1469814,0.001443769,0.00006691858,0.00008527148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6718076,0.00001370755,0.3221869,0.0001857276,0.000008405985,0.0004101095,2.900131e-7,0.00004763786,0.005339615],"genre_scores_gemma":[0.9546505,0.000001372234,0.04517135,0.00004017558,0.000007124888,0.00009529544,7.889533e-7,0.000002981783,0.00003042043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6399065,"threshold_uncertainty_score":0.1708199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02095212821227232,"score_gpt":0.2917291532299468,"score_spread":0.2707770250176745,"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."}}