{"id":"W1652860189","doi":"10.1109/izsbc.2002.991760","title":"Turbo decoding with erasures for high-speed transmission in the presence of impulse noise","year":2003,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Turbo code; Impulse noise; Turbo equalizer; Concatenated error correction code; Decoding methods; Erasure; Turbo; Serial concatenated convolutional codes; Impulse (physics); Electronic engineering; Algorithm; Block code; Engineering; Physics; Artificial intelligence","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":[0.0002079273,0.00006481152,0.00008386622,0.00004285124,0.00002835873,0.000009509003,0.0001872407,0.00002373404,0.00002288789],"category_scores_gemma":[0.0000268917,0.00003826612,0.00002069319,0.0001463495,0.00001542975,0.00006386947,0.000004303625,0.00006538774,5.405554e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007653864,"about_ca_system_score_gemma":0.00001094689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000306277,"about_ca_topic_score_gemma":0.00005313149,"domain_scores_codex":[0.9996147,0.00002777836,0.0001308975,0.00005837774,0.0000644532,0.0001037668],"domain_scores_gemma":[0.9994768,0.0001914038,0.0000125637,0.0002765615,0.00002347715,0.00001918443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005689083,0.00130207,0.02079739,0.001196805,0.0002824152,0.00001317902,0.01952914,0.344033,0.3474648,0.1328284,0.01293449,0.1190494],"study_design_scores_gemma":[0.006740813,0.0005272155,0.04328606,0.0005884318,0.0001208748,0.00002850108,0.001909319,0.2768953,0.6108317,0.006392275,0.0515559,0.001123549],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9142622,0.001237469,0.07189263,0.0002868648,0.00004972142,0.0005238553,0.000005448242,0.00007246363,0.01166937],"genre_scores_gemma":[0.9838805,0.0001130176,0.01589904,0.0000160981,0.000004644704,0.0000181263,0.000002239037,0.000009563659,0.00005678065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.263367,"threshold_uncertainty_score":0.1560447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01437049405513801,"score_gpt":0.2373436347783023,"score_spread":0.2229731407231643,"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."}}