{"id":"W2149936524","doi":"10.1109/vetecs.2005.1543578","title":"Asymptotic Performance Analysis of LDPC codes with Channel Estimation Error","year":2005,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Low-density parity-check code; Additive white Gaussian noise; Algorithm; Channel (broadcasting); Computer science; Decoding methods; Turbo code; Error floor; Mathematics; Block code; Telecommunications","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":[0.003971768,0.0008669474,0.0007603713,0.001356592,0.0006274431,0.001353276,0.0008879187,0.001185209,0.001278669],"category_scores_gemma":[0.05144424,0.0004647986,0.0003659427,0.00105103,0.002256475,0.002306854,0.001659457,0.00145497,0.0003113274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00233476,"about_ca_system_score_gemma":0.001765893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004389083,"about_ca_topic_score_gemma":0.001819593,"domain_scores_codex":[0.9973622,0.000980054,0.00007222624,0.0002221286,0.001036124,0.0003272692],"domain_scores_gemma":[0.9668201,0.02511808,0.00198212,0.001749429,0.003996393,0.0003337839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001637048,0.00002569737,0.002569886,0.00009508125,0.00004666067,0.0001290147,0.0001601733,0.9263986,0.004308593,0.05426484,0.0004256037,0.01141212],"study_design_scores_gemma":[0.000004111382,0.00001932457,0.0002670077,0.00001235389,0.00000581174,0.00003283006,0.0000125405,0.9911542,0.001474038,0.00692737,0.00008144657,0.000008899821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1903681,0.001527224,0.7995517,0.0006278969,0.00004615214,0.00005634396,0.000135553,0.0006695935,0.007017481],"genre_scores_gemma":[0.9668868,0.0007628367,0.03031703,0.00007562253,0.00004312636,0.00007495357,0.0001850851,0.000120967,0.001533681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004389083,"threshold_uncertainty_score":0.02100497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483333054158815,"score_gpt":0.2580844437101876,"score_spread":0.2432511131685994,"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."}}