{"id":"W1998696172","doi":"10.1109/vetecf.2010.5594462","title":"A Modified Belief Propagation Algorithm Based on Attenuation of the Extrinsic LLR","year":2010,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Communications Research Centre Canada","funders":"","keywords":"Belief propagation; Algorithm; Low-density parity-check code; Decoding methods; Logarithm; Sign (mathematics); Computer science; Joint (building); Bit error rate; Sequential decoding; Message passing; Berlekamp–Welch algorithm; Mathematics; Block code; Error floor; Engineering; Parallel computing","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":[0.001136176,0.0007958072,0.0008710925,0.0008465891,0.0003547137,0.0008816633,0.00179064,0.001197367,0.001470136],"category_scores_gemma":[0.00557893,0.0004293081,0.000514191,0.0008666245,0.0006035935,0.001718617,0.0007639177,0.001848198,0.00102656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005556089,"about_ca_system_score_gemma":0.001088377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002913552,"about_ca_topic_score_gemma":0.003294705,"domain_scores_codex":[0.9987932,0.0002294219,0.00006341387,0.0002041175,0.0006396266,0.00007013461],"domain_scores_gemma":[0.9978125,0.0008685368,0.0001410125,0.0002948761,0.0008260239,0.00005708518],"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":[0.0003293668,0.0001522431,0.001306114,0.0002205551,0.0001686941,0.0001539566,0.0001431578,0.1905432,0.03963412,0.02190399,0.0037336,0.7417111],"study_design_scores_gemma":[0.00004870717,0.00007877535,0.000335889,0.00001564563,0.0000467205,0.0001720005,0.00001011326,0.9748197,0.01548571,0.004925092,0.004029811,0.00003187602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002867243,0.0001141284,0.9960043,0.0001073157,0.00005475388,0.00002171513,0.00001948931,0.0003030984,0.000508004],"genre_scores_gemma":[0.134027,0.000436841,0.8601928,0.0002638709,0.0001650714,0.0001343226,0.0001767194,0.0001365941,0.004466857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002913552,"threshold_uncertainty_score":0.006008685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387850710307635,"score_gpt":0.2441717107211512,"score_spread":0.2302932036180748,"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."}}