{"id":"W2523954942","doi":"10.1007/bf03219808","title":"On multiple slice turbo codes","year":2005,"lang":"fr","type":"article","venue":"Annals of Telecommunications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Alberta","funders":"","keywords":"Turbo code; Computer science; Serial concatenated convolutional codes; Parallel computing; Decoding methods; Turbo equalizer; BCJR algorithm; Concatenated error correction code; Algorithm; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003705579,0.0002887059,0.0003863747,0.0002444459,0.0002421636,0.00003049501,0.001838487,0.0002287636,0.0003925852],"category_scores_gemma":[0.000652433,0.000362567,0.0001965814,0.0004827244,0.0003126275,0.0004522154,0.0003335872,0.0007373772,0.0003453084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009500947,"about_ca_system_score_gemma":0.00004800016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002999272,"about_ca_topic_score_gemma":0.0005619,"domain_scores_codex":[0.9981961,0.0002529689,0.0007437394,0.0002019565,0.0001952195,0.0004099624],"domain_scores_gemma":[0.9934821,0.002251697,0.0002627171,0.003509968,0.0003611787,0.000132381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002620809,0.001471929,0.0003180349,0.0001680997,0.0002738602,5.079375e-7,0.00155324,0.1369609,0.005857994,0.2226687,0.07569853,0.555002],"study_design_scores_gemma":[0.000257013,0.000128147,0.001889787,0.0005055851,0.00003223051,0.000005849066,0.0001448994,0.03926159,0.2559043,0.009891376,0.6914607,0.0005185329],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1026629,0.2257659,0.07129128,0.3330628,0.000643787,0.002358011,0.001135713,0.003397897,0.2596818],"genre_scores_gemma":[0.75655,0.04274837,0.1981095,0.0006874673,0.00006182509,0.0001072907,0.0000816615,0.00006837117,0.001585574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.653887,"threshold_uncertainty_score":0.9998826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06287295394917686,"score_gpt":0.3450530734404925,"score_spread":0.2821801194913157,"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."}}