{"id":"W2049608049","doi":"10.1109/tit.2006.887467","title":"On Designing Good LDPC Codes for Markov Channels","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Low-density parity-check code; Markov chain; Decoding methods; Computer science; Schedule; Channel (broadcasting); Algorithm; Block (permutation group theory); Markov process; Code (set theory); Message passing; Theoretical computer science; Mathematics; Parallel computing; Telecommunications; Combinatorics; Set (abstract data type); Statistics","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":[0.001728095,0.0006516998,0.000635517,0.0005023621,0.0006277571,0.0006276808,0.00064521,0.0007044009,0.001034842],"category_scores_gemma":[0.009426466,0.000415069,0.000315535,0.0005280551,0.001504551,0.001525199,0.001077937,0.000917091,0.000327873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064311,"about_ca_system_score_gemma":0.001044347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001363903,"about_ca_topic_score_gemma":0.001703986,"domain_scores_codex":[0.9988255,0.0005138799,0.00005052492,0.0001274611,0.0003841342,0.0000985221],"domain_scores_gemma":[0.9967995,0.002124976,0.0002630196,0.0004700428,0.0002717606,0.00007078444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001641409,0.00003769593,0.0007828784,0.000182306,0.00003128155,0.00007396936,0.0003407888,0.6814886,0.01373055,0.2450876,0.0007608592,0.05731934],"study_design_scores_gemma":[0.00002892042,0.00005884391,0.0001136205,0.00002377631,0.00001065764,0.00005044354,0.00002172744,0.9317625,0.006979093,0.05894469,0.001989213,0.00001654934],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04034106,0.0002860427,0.9566094,0.0001929617,0.00001786587,0.00006111518,0.00004233995,0.0002067015,0.002242519],"genre_scores_gemma":[0.62836,0.0008340694,0.3677386,0.0001678636,0.00004865072,0.0002248528,0.0001363174,0.0001167122,0.002373031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001728095,"threshold_uncertainty_score":0.009139121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01377970465499197,"score_gpt":0.2608090091921903,"score_spread":0.2470293045371983,"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."}}