{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006618687,0.0005041468,0.0003263343,0.0003756079,0.0003457377,0.0004963782,0.0005329783,0.0004450587,0.001156218],"category_scores_gemma":[0.002203803,0.0002301632,0.0002851183,0.0005063933,0.0005089692,0.0006162702,0.0005723793,0.000607695,0.0004568689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000445609,"about_ca_system_score_gemma":0.0008054836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000831567,"about_ca_topic_score_gemma":0.001185379,"domain_scores_codex":[0.9994529,0.0001563594,0.00002661048,0.00004869485,0.0002655695,0.00004985551],"domain_scores_gemma":[0.9990817,0.0004069849,0.00006336966,0.0001658811,0.0002620252,0.00002000569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003918539,0.0001133899,0.001053802,0.000196437,0.00008493518,0.0003173259,0.0002047017,0.4062694,0.09078492,0.1517022,0.003939256,0.3449417],"study_design_scores_gemma":[0.00002094972,0.00006281469,0.0001447059,0.00000952445,0.00001028055,0.0001017394,0.000006590237,0.960108,0.0197695,0.0175184,0.00223581,0.00001166898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01554459,0.0001530635,0.9815247,0.00007786624,0.00003175792,0.00002717248,0.00002549826,0.00039781,0.002217518],"genre_scores_gemma":[0.4288836,0.0004266615,0.5660421,0.00011075,0.00006654584,0.0002039796,0.0001503001,0.0001068535,0.004009206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001156218,"threshold_uncertainty_score":0.003867924,"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."}}