{"id":"W2151900023","doi":"10.1109/lcomm.2009.082047","title":"Error performances of multi shift-register convolutional self-doubly-orthogonal codes","year":2009,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Convolutional code; Decoding methods; Algorithm; Sequential decoding; Computer science; Serial concatenated convolutional codes; List decoding; Encoder; Turbo code; 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":[0.0004853419,0.0001801304,0.0002211973,0.0002164115,0.0003079742,0.00006805066,0.003484779,0.00007282491,0.000005313309],"category_scores_gemma":[0.00003115999,0.0001836428,0.0001108037,0.0004117897,0.0002546301,0.0006192715,0.0002833041,0.0003633124,0.00002600868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000865788,"about_ca_system_score_gemma":0.00008563466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004908484,"about_ca_topic_score_gemma":0.00006376454,"domain_scores_codex":[0.9985052,0.000191933,0.0004428489,0.0002829256,0.000308815,0.0002683031],"domain_scores_gemma":[0.9968073,0.0002491817,0.0002852433,0.002459728,0.0001322462,0.00006625027],"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":[0.0001579048,0.007336651,0.1135258,0.0003022832,0.0007528007,0.00003768163,0.04524398,0.004999702,0.2755519,0.3847228,0.09377722,0.07359123],"study_design_scores_gemma":[0.002559063,0.0006413463,0.2844748,0.0005016515,0.0001248782,0.0001956672,0.0001623978,0.6321929,0.03030595,0.004836532,0.04176531,0.00223948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4010566,0.0005467554,0.5527523,0.04136309,0.0005023573,0.000508108,0.00001941216,0.001640255,0.001611033],"genre_scores_gemma":[0.7211241,0.00003504311,0.2767624,0.001993081,0.00002405254,0.00002486879,0.00001031034,0.000007092162,0.00001902092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6271932,"threshold_uncertainty_score":0.7488737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05356096881300976,"score_gpt":0.3154537368189577,"score_spread":0.261892768005948,"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."}}