{"id":"W1610238754","doi":"10.1109/ccece.2000.849670","title":"New error detection techniques and stopping criteria for turbo decoding","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoding methods; Computer science; Turbo; Turbo code; Error detection and correction; Serial concatenated convolutional codes; Algorithm; Concatenated error correction code; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00005884857,0.00009577928,0.00009633187,0.00008948643,0.00006823299,0.00003308043,0.0001116696,0.00006194741,0.00006431379],"category_scores_gemma":[0.00001604494,0.0001030945,0.00002275393,0.00007640357,0.00001117581,0.0002313716,0.0000353273,0.00007606529,0.000002125108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004504431,"about_ca_system_score_gemma":9.448094e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007202688,"about_ca_topic_score_gemma":0.00001488915,"domain_scores_codex":[0.9995816,0.000005462398,0.0001415639,0.0001103472,0.0000358104,0.0001252574],"domain_scores_gemma":[0.9996423,0.00004342377,0.00001890096,0.0002300717,0.00002133899,0.00004395118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002264175,0.000003534522,0.00001347746,0.00004327194,0.00000706138,2.815691e-7,0.0001266715,0.00003654968,0.08003747,0.0007611299,0.001792537,0.9171758],"study_design_scores_gemma":[0.0001336501,0.00003743049,0.0000169571,0.0000431721,0.000005484515,0.00001452266,0.00003921767,0.1749661,0.7851971,0.003673518,0.03566402,0.0002087911],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005389782,0.0007188435,0.9854218,0.00009641895,0.00004903657,0.0002740524,0.000001081267,0.00265266,0.005396364],"genre_scores_gemma":[0.6277348,0.0003738987,0.3713724,0.00002094186,0.00004603054,0.00006967236,9.378149e-7,0.00002619224,0.0003551103],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.916967,"threshold_uncertainty_score":0.4204071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04046888244456477,"score_gpt":0.2937245275617889,"score_spread":0.2532556451172241,"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."}}