{"id":"W2153253113","doi":"10.1109/vetecf.2005.1558449","title":"Simplified turbo decoding by way of selective trellis pruning","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; Computer science; Pruning; Trellis (graph); Turbo; Turbo equalizer; Turbo code; Concatenated error correction code; Algorithm; Engineering; Block code","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":[0.0008167688,0.001130309,0.001537089,0.0008941316,0.000646223,0.001103268,0.000866992,0.0009332622,0.004526698],"category_scores_gemma":[0.003044092,0.0004655657,0.0008472363,0.001271772,0.0006869861,0.001562928,0.001304701,0.00133255,0.002963827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004146617,"about_ca_system_score_gemma":0.001742697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001204736,"about_ca_topic_score_gemma":0.00295466,"domain_scores_codex":[0.9988974,0.0003438989,0.00006904815,0.000108385,0.0004548781,0.0001263286],"domain_scores_gemma":[0.9987992,0.0005326463,0.00004335779,0.0003849195,0.0002005907,0.00003921468],"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":[0.001073855,0.0001890193,0.0007696238,0.0005720496,0.0002620392,0.0006967628,0.0002464254,0.1683531,0.1125135,0.1847932,0.01191012,0.5186203],"study_design_scores_gemma":[0.0001375743,0.0001917243,0.0006752882,0.0001054613,0.0001567639,0.001191712,0.00003049915,0.8313776,0.06509247,0.08648051,0.0144729,0.00008740457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0124841,0.000839941,0.9777151,0.0001842739,0.0001459178,0.00005872511,0.0001906841,0.00079886,0.007582492],"genre_scores_gemma":[0.2410381,0.001406392,0.7448296,0.0002547239,0.000270779,0.0002489266,0.0006565523,0.0004018262,0.01089322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004526698,"threshold_uncertainty_score":0.01514333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005360817016385939,"score_gpt":0.2151863400006227,"score_spread":0.2098255229842368,"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."}}