{"id":"W2057542493","doi":"10.1109/lcomm.2006.1714538","title":"Parallel processing for fast iterative decoding of orthogonal convolutional codes","year":2006,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Convolutional code; Decoding methods; Sequential decoding; Computer science; Algorithm; Serial concatenated convolutional codes; List decoding; Berlekamp–Welch algorithm; Concatenated error correction code; Theoretical computer science; 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":[0.0003273579,0.0001227217,0.0001637358,0.0001506814,0.0003659627,0.00008686024,0.001680605,0.0000420678,0.00000119937],"category_scores_gemma":[0.00004199155,0.0001315243,0.00008540947,0.0002997341,0.0002214682,0.0004235564,0.0002056902,0.0001487252,0.000002193613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007468595,"about_ca_system_score_gemma":0.00008294257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004623792,"about_ca_topic_score_gemma":0.00009630683,"domain_scores_codex":[0.9989526,0.0001039004,0.0003600053,0.0002207169,0.0001657245,0.0001971108],"domain_scores_gemma":[0.9980028,0.0004733779,0.0002619234,0.0009830107,0.0002531697,0.00002574375],"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":[0.00005079929,0.0007923632,0.02090978,0.0001997161,0.0001084793,0.000003229021,0.00467869,0.008555257,0.4088779,0.4770876,0.03251009,0.04622612],"study_design_scores_gemma":[0.001165635,0.0001329082,0.01065133,0.0004740088,0.00004722506,0.00005820568,0.0001513852,0.9179973,0.04110045,0.02030283,0.006991358,0.0009273526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02540037,0.0002116837,0.9679207,0.005246464,0.00008721013,0.0002685192,0.00001483634,0.000254589,0.0005955949],"genre_scores_gemma":[0.5523425,0.000004390089,0.4471686,0.0003199212,0.00002368173,0.00009702369,0.00001706264,0.000006802361,0.00001997679],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9094421,"threshold_uncertainty_score":0.5363405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03899372540773281,"score_gpt":0.3071188366573375,"score_spread":0.2681251112496047,"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."}}