{"id":"W2127615762","doi":"10.1109/isit.1997.613368","title":"Concurrent turbo-decoding","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Turbo code; Decoding methods; Serial concatenated convolutional codes; Turbo; Sequential decoding; Turbo equalizer; List decoding; Algorithm; Berlekamp–Welch algorithm; Factor graph; Theoretical computer science; Parallel computing; Concatenated error correction code; Low-density parity-check code; Error floor; Block code; Engineering","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":[0.00001989437,0.00005443069,0.00005434901,0.00003105897,0.00002119104,0.000009786805,0.0001579928,0.00002196143,0.0006976325],"category_scores_gemma":[0.00000541571,0.0000541904,0.00001715803,0.00006375428,0.00001193154,0.00008305834,0.00002915433,0.00007825028,0.0001594648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002860373,"about_ca_system_score_gemma":2.702024e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.272955e-7,"about_ca_topic_score_gemma":0.000001083025,"domain_scores_codex":[0.9997239,0.000003137296,0.00009052883,0.00005273312,0.00004137546,0.0000883657],"domain_scores_gemma":[0.9996899,0.00001837239,0.000007285956,0.0002485708,0.00001000463,0.00002591187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[1.066279e-7,0.00002262457,0.0002410891,0.00002423593,0.00001327951,0.000001172647,0.0002162183,0.003038191,0.004467493,0.02886626,0.02837016,0.9347392],"study_design_scores_gemma":[0.0001599329,0.00001209095,0.00008292336,0.00002596715,0.000003017944,0.000004733392,0.00003725853,0.7457177,0.1224915,0.001521177,0.1296439,0.0002997843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01509161,0.003381176,0.4848577,0.0002328091,0.0002701131,0.0001813521,0.000001772351,0.006922747,0.4890607],"genre_scores_gemma":[0.9894863,0.0007876659,0.009050285,0.00003427697,0.00001483801,0.00001549662,9.99121e-7,0.00001429891,0.0005958866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9743946,"threshold_uncertainty_score":0.7638586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680470077516639,"score_gpt":0.2111672974227266,"score_spread":0.1843625966475602,"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."}}