{"id":"W2115650309","doi":"10.1109/glocom.1998.776488","title":"A DSP-based implementation of a turbo-decoder","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Viterbi decoder; Turbo code; Soft-decision decoder; Computer science; Convolutional code; Viterbi algorithm; Turbo equalizer; Digital signal processing; Turbo; Decoding methods; Soft output Viterbi algorithm; Block (permutation group theory); Code (set theory); Serial concatenated convolutional codes; Computer hardware; Algorithm; Block code; Concatenated error correction code; Sequential decoding; Programming language; Engineering; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002470694,0.00004846393,0.00006110682,0.0000534348,0.0000104163,0.000003173471,0.00009290744,0.00001956561,0.001130196],"category_scores_gemma":[0.000002451885,0.00004885625,0.00002052069,0.00009061385,0.00001105621,0.00006762066,0.000009689768,0.00003773082,0.00001495689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002114717,"about_ca_system_score_gemma":0.000001569927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001163162,"about_ca_topic_score_gemma":0.00002377582,"domain_scores_codex":[0.9996974,0.000007000103,0.0001350076,0.00004058102,0.00005438118,0.00006562695],"domain_scores_gemma":[0.9996918,0.00002216627,0.00001929382,0.0002302513,0.00002182497,0.00001465576],"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":[0.000004278225,0.0001245852,0.004990048,0.000211877,0.00006311955,0.000001167336,0.001067509,0.03653825,0.09888649,0.008751054,0.0232153,0.8261463],"study_design_scores_gemma":[0.0002789825,0.00002198126,0.000924343,0.00001064403,0.000003824271,4.368691e-7,0.0001083065,0.1531926,0.8409246,0.0004713785,0.003945585,0.0001173219],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1021205,0.0004883843,0.8664023,0.0002794273,0.00004215957,0.0003369367,0.000009767836,0.001914398,0.02840612],"genre_scores_gemma":[0.9401609,0.00006135698,0.05964113,0.00004166698,0.000003609695,0.00002865425,0.000003941978,0.00001222447,0.00004653677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8380404,"threshold_uncertainty_score":0.9997829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721508760512891,"score_gpt":0.2653690439746624,"score_spread":0.2481539563695335,"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."}}