{"id":"W2141578547","doi":"10.1109/tcomm.2003.818099","title":"Techniques for early stopping and error detection in turbo decoding","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Turbo code; Convolutional code; Decoding methods; Serial concatenated convolutional codes; Computer science; Redundancy (engineering); Algorithm; Cyclic redundancy check; Turbo; Encoder; Error detection and correction; Concatenated error correction code; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006211462,0.001454388,0.001976966,0.003004793,0.0008497544,0.002083487,0.001876805,0.002175919,0.0009963685],"category_scores_gemma":[0.03780651,0.0006466028,0.000780685,0.00135929,0.002589938,0.002988392,0.002710031,0.002820936,0.0007187997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197228,"about_ca_system_score_gemma":0.001574922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006083475,"about_ca_topic_score_gemma":0.0006806562,"domain_scores_codex":[0.9935864,0.002062445,0.0005070698,0.0004753858,0.003024589,0.000344137],"domain_scores_gemma":[0.9759783,0.01456007,0.002198875,0.002046796,0.004791734,0.0004242093],"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":[0.0007992082,0.0001136161,0.002619742,0.000669705,0.000130065,0.0004627406,0.0008624935,0.1591967,0.0387234,0.3647494,0.003576971,0.428096],"study_design_scores_gemma":[0.00007488004,0.0003102321,0.0006076964,0.0001486058,0.00004806007,0.0005037755,0.0000495161,0.8337238,0.04763549,0.1099149,0.006823379,0.0001596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003159386,0.0004732385,0.9952583,0.00007545106,0.00004664707,0.0000356306,0.00001193213,0.0002413306,0.0006980868],"genre_scores_gemma":[0.192029,0.0007715891,0.8044543,0.0002055235,0.0002185615,0.0002122594,0.00007485269,0.0002264323,0.00180738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006211462,"threshold_uncertainty_score":0.03284973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0342274945587065,"score_gpt":0.2920470509046826,"score_spread":0.2578195563459761,"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."}}