{"id":"W2154165522","doi":"10.1109/tcomm.2009.11.070210","title":"Waterfall Performance Analysis of Finite-Length LDPC Codes on Symmetric Channels","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Low-density parity-check code; Decoding methods; Algorithm; Binary symmetric channel; Code word; Mathematics; Channel (broadcasting); Channel capacity; Waterfall; Block code; Computer science; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004096034,0.0001953679,0.0003315098,0.00178623,0.0004376505,0.00006929424,0.00262933,0.00009683436,0.00001338425],"category_scores_gemma":[0.00002513394,0.0001922232,0.0002434522,0.004569493,0.00007417578,0.0003360968,0.00001290824,0.0004579726,0.0000349628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009682991,"about_ca_system_score_gemma":0.00004368211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006989935,"about_ca_topic_score_gemma":0.00007275913,"domain_scores_codex":[0.9984846,0.0001949735,0.0004352268,0.000324317,0.0003115708,0.0002493113],"domain_scores_gemma":[0.9951623,0.0008884748,0.0001785331,0.003486717,0.0002071242,0.00007683035],"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":[0.00007540181,0.005159158,0.0002632922,0.0000376934,0.001294516,0.000002526093,0.006301885,0.4230668,0.004020977,0.02373619,0.0003383734,0.5357031],"study_design_scores_gemma":[0.0002022933,0.000789092,0.001834575,0.00008554567,0.000314428,0.000002273195,0.00004079991,0.9254932,0.06971553,0.0006394204,0.0005478199,0.0003350315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02728241,0.00006503452,0.9655929,0.001764311,0.0002436154,0.000235964,0.00002112724,0.0006333368,0.004161302],"genre_scores_gemma":[0.9685866,0.0006144032,0.03022115,0.000281602,0.000007216805,0.00005103186,0.000007034695,0.00001057056,0.0002203604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9413042,"threshold_uncertainty_score":0.7838634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04406269363340806,"score_gpt":0.2934794131768714,"score_spread":0.2494167195434634,"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."}}