{"id":"W2110262961","doi":"10.1109/istel.2010.5734050","title":"Iterative soft decoding of Reed-Solomon codes using information correction","year":2010,"lang":"en","type":"article","venue":"","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Decoding methods; List decoding; Sequential decoding; Berlekamp–Welch algorithm; Computer science; Belief propagation; Parity-check matrix; Algorithm; Concatenated error correction code; Low-density parity-check code; Binary symmetric channel; Serial concatenated convolutional codes; Theoretical computer science; Arithmetic; Mathematics; Block code","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":[0.0002977244,0.00005953287,0.00007780369,0.0001588985,0.0001133545,0.0001016079,0.0002136603,0.00004427404,0.0000236111],"category_scores_gemma":[0.00006094507,0.00005560469,0.00004405026,0.0003233997,0.00003821086,0.001515837,0.00006979259,0.0001088055,0.000006635754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006944957,"about_ca_system_score_gemma":0.00001989359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003417612,"about_ca_topic_score_gemma":0.00003282661,"domain_scores_codex":[0.9995143,0.0000276957,0.0001721247,0.00008717516,0.0001021052,0.00009661333],"domain_scores_gemma":[0.9994753,0.0000928382,0.0001082711,0.0001883321,0.0001067843,0.00002846689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003371038,0.0000530455,0.006064036,0.000020974,0.00002395494,6.770808e-7,0.00709635,0.0005455988,0.1175509,0.5638149,0.0002513299,0.3045445],"study_design_scores_gemma":[0.0002590755,0.0001295966,0.002080293,0.00003954476,0.000008812005,0.00003387256,0.0003079461,0.8230401,0.1554333,0.01765793,0.0007889881,0.0002205338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3115736,0.000001941052,0.6835526,0.00001807775,0.0007630841,0.00004210769,4.406874e-7,0.00007460851,0.00397358],"genre_scores_gemma":[0.950482,8.135364e-7,0.04940159,0.00007238366,0.00002175221,0.000001483731,0.000001236779,0.000001523105,0.00001719004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8224945,"threshold_uncertainty_score":0.2267493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090130175463782,"score_gpt":0.243398353821196,"score_spread":0.2324970520665582,"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."}}