{"id":"W2112736588","doi":"10.1109/tcsii.2006.882204","title":"Degree-Matched Check Node Decoding for Regular and Irregular LDPCs","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Decoding methods; Degree (music); Low-density parity-check code; Node (physics); Mathematics; Degree distribution; List decoding; Parity (physics); Computer science; Algorithm; Discrete mathematics; Combinatorics; Arithmetic; Concatenated error correction code; Complex network","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003222317,0.0002542603,0.0003355003,0.0001924117,0.0008653695,0.001143626,0.0001720413,0.0001335196,4.192949e-7],"category_scores_gemma":[0.000006355733,0.0002339892,0.00006498193,0.0002488715,0.0001001086,0.001071724,0.000007684728,0.0001490297,3.79717e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003828794,"about_ca_system_score_gemma":0.00005240337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007030835,"about_ca_topic_score_gemma":0.00001992986,"domain_scores_codex":[0.998466,0.00002743693,0.0003769158,0.0005717686,0.0002245113,0.0003333796],"domain_scores_gemma":[0.999299,0.0001292094,0.0001439421,0.0001752962,0.0001247139,0.000127849],"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":[0.00001518999,0.0001426409,0.0003730984,0.00060601,0.00003952123,0.00001421412,0.0007824198,0.0009836336,0.008295604,0.00189605,0.0000711984,0.9867804],"study_design_scores_gemma":[0.001600806,0.0008073937,0.0005216048,0.001606013,0.000122778,0.0008985301,0.0007175836,0.9655831,0.01390675,0.0117603,0.001039497,0.001435589],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06105642,0.001485201,0.9359404,0.00007401077,0.0001118013,0.0002862937,0.00001382017,0.0003016855,0.0007304072],"genre_scores_gemma":[0.9980286,0.00001632281,0.001460406,0.00003946031,0.00006354135,0.00004472586,0.000002539179,0.00002265461,0.000321696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9853448,"threshold_uncertainty_score":0.9998933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121611143255829,"score_gpt":0.2437217073219459,"score_spread":0.2125055958893876,"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."}}