{"id":"W2058277537","doi":"10.1049/iet-com.2014.0235","title":"Recursive method for generating column weight 3 low‐density parity‐check codes based on three‐partite graphs","year":2014,"lang":"en","type":"article","venue":"IET Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Parity (physics); Low-density parity-check code; Combinatorics; Discrete mathematics; Algorithm; Decoding methods; Physics","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":[0.0001911989,0.0003900476,0.0003361883,0.0007431163,0.0004961696,0.0003700916,0.0005168585,0.0003506439,0.002367609],"category_scores_gemma":[0.0008762902,0.0002812175,0.0005941749,0.0004982335,0.0004769185,0.000451371,0.0006794724,0.0005254376,0.0008256244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00027204,"about_ca_system_score_gemma":0.0006518696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001405214,"about_ca_topic_score_gemma":0.002094637,"domain_scores_codex":[0.9997287,0.00006417892,0.000013594,0.00004494456,0.0001061952,0.00004236776],"domain_scores_gemma":[0.9996326,0.0001523737,0.00003282672,0.0000896705,0.00007388407,0.00001863431],"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":[0.0003076525,0.0001711532,0.001449681,0.0003587744,0.0001039273,0.0007040181,0.0006947236,0.2334736,0.1789039,0.1783336,0.005466544,0.4000325],"study_design_scores_gemma":[0.00008532733,0.0002196548,0.0007324892,0.00003649152,0.00005287252,0.0005539611,0.00007505318,0.8477207,0.08277692,0.0523698,0.01528887,0.00008783632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03344489,0.00007445266,0.9620752,0.00005895985,0.00002649168,0.0001014232,0.00008313814,0.0007179746,0.003417478],"genre_scores_gemma":[0.2901032,0.0001347434,0.7053062,0.0001108802,0.00001744304,0.0002367746,0.0003803663,0.0001774484,0.003532928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002367609,"threshold_uncertainty_score":0.007920444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.037734728780342,"score_gpt":0.3279037485560298,"score_spread":0.2901690197756878,"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."}}