{"id":"W2000901063","doi":"10.1109/lcomm.2011.122211.111737","title":"Efficient Stochastic Decoding of Non-Binary LDPC Codes with Degree-Two Variable Nodes","year":2012,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Low-density parity-check code; Decoding methods; Degree (music); Algorithm; Binary number; Node (physics); Variable (mathematics); Computer science; List decoding; Mathematics; Block code; Concatenated error correction code; Arithmetic","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006819632,0.0004805631,0.0006053976,0.0002690197,0.0002754823,0.0006198792,0.0008293099,0.0005150835,0.0007409186],"category_scores_gemma":[0.002359261,0.0002469076,0.0003092873,0.0004869641,0.0005352724,0.0007578557,0.0009381308,0.0006124597,0.0002787431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004529493,"about_ca_system_score_gemma":0.001521508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001221206,"about_ca_topic_score_gemma":0.003111959,"domain_scores_codex":[0.9991049,0.0003155166,0.00005016297,0.0000753455,0.0003616698,0.00009230468],"domain_scores_gemma":[0.9988849,0.0004649128,0.0001437715,0.0002804253,0.0001820183,0.00004405644],"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":[0.000506412,0.0000906514,0.001013279,0.0002056105,0.00008893661,0.0001552398,0.0001674493,0.7061461,0.06214704,0.09431277,0.001496104,0.1336704],"study_design_scores_gemma":[0.00001689899,0.00003914855,0.0001306724,0.000004718757,0.000004988547,0.00004328501,0.000005661839,0.9824542,0.01106194,0.005703246,0.0005273498,0.000007802012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07629249,0.0001643158,0.9210915,0.0001465509,0.00002915764,0.00003456373,0.0000759429,0.0003191991,0.001846179],"genre_scores_gemma":[0.5639768,0.0002350004,0.4316471,0.0001283028,0.00003513509,0.00007253492,0.0002963804,0.0001083425,0.003500364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001221206,"threshold_uncertainty_score":0.003606617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04324977171945868,"score_gpt":0.2985448453377424,"score_spread":0.2552950736182837,"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."}}