{"id":"W2134379862","doi":"10.1109/icc.2005.1494428","title":"DC-free convolutional codes and DC-free turbo codes","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Turbo code; Convolutional code; Serial concatenated convolutional codes; Computer science; Puncturing; Turbo equalizer; BCJR algorithm; Encoding (memory); Algorithm; Concatenated error correction code; Encoder; Block code; Electronic engineering; Decoding methods; Telecommunications; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004010843,0.0003981643,0.0002356709,0.0005198769,0.0003578683,0.0005782372,0.000499722,0.0006830925,0.002059862],"category_scores_gemma":[0.00198329,0.0001897958,0.0002702281,0.0005010272,0.0006521661,0.0008016668,0.0006529833,0.0006818381,0.0007998153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004777525,"about_ca_system_score_gemma":0.0006447997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009742472,"about_ca_topic_score_gemma":0.001535627,"domain_scores_codex":[0.9993857,0.00009554327,0.00003220257,0.00007668315,0.0003251067,0.00008478479],"domain_scores_gemma":[0.9987499,0.0002771969,0.0001873768,0.0002945255,0.0004131117,0.00007790334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003937393,0.0000626279,0.002010068,0.000364576,0.00007535629,0.0007817238,0.0002137319,0.08191497,0.08486366,0.6169496,0.003728259,0.2086418],"study_design_scores_gemma":[0.00008093147,0.0003244069,0.002124408,0.0001630912,0.0001184841,0.002853438,0.00006819118,0.4459141,0.2570639,0.1932923,0.09781945,0.0001773128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07612967,0.001807839,0.8862184,0.000443025,0.0003872873,0.0001134022,0.0002520843,0.0005200349,0.03412832],"genre_scores_gemma":[0.7216931,0.001505652,0.2460691,0.0005628451,0.0002334261,0.0001348641,0.0003799055,0.0001535059,0.02926754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002059862,"threshold_uncertainty_score":0.006890893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159903021241384,"score_gpt":0.238264871887666,"score_spread":0.2266658416752521,"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."}}