{"id":"W2121122045","doi":"10.1109/ccece.2003.1226223","title":"Soft Reed-Solomon decoding for concatenated codes","year":2004,"lang":"en","type":"article","venue":"","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université du Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Decoding methods; Convolutional code; Sequential decoding; Concatenated error correction code; Algorithm; Serial concatenated convolutional codes; Computer science; List decoding; Additive white Gaussian noise; Reed–Solomon error correction; Turbo code; Block Error Rate; Block code; Channel (broadcasting); Telecommunications","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":[0.001283977,0.0004982138,0.0005332878,0.000701294,0.0003431338,0.0009658746,0.0003770065,0.0006802723,0.0006540118],"category_scores_gemma":[0.01029448,0.0002512814,0.0003980311,0.0006135073,0.001040288,0.000922985,0.0005912128,0.000491177,0.0003292431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009444391,"about_ca_system_score_gemma":0.0007024762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008466319,"about_ca_topic_score_gemma":0.0006538937,"domain_scores_codex":[0.9984818,0.0006063809,0.00006483107,0.00006786842,0.0007094066,0.00006971868],"domain_scores_gemma":[0.9951448,0.003599786,0.0003831193,0.0004430804,0.0003880054,0.00004109241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000199744,0.00001789794,0.001269949,0.0001993974,0.00005727696,0.0001926944,0.0001885206,0.8692439,0.01711729,0.07212394,0.000332451,0.03905699],"study_design_scores_gemma":[0.00001007885,0.00006071495,0.0002985125,0.000024301,0.00001478463,0.0001485454,0.00001384459,0.9617323,0.01100413,0.02584546,0.0008331182,0.00001424743],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1604258,0.002267078,0.8306517,0.0002706128,0.0000542624,0.00004743298,0.0000726303,0.0004413417,0.005769151],"genre_scores_gemma":[0.8876004,0.00113592,0.1092075,0.00005046096,0.00006752964,0.00005540991,0.00007575297,0.00008867947,0.001718357],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001283977,"threshold_uncertainty_score":0.006852388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030091934969145,"score_gpt":0.2549802408066738,"score_spread":0.2346793214569823,"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."}}