{"id":"W2350415793","doi":"","title":"Performance research on a low rate of LDPC codes in LTE-advanced","year":2010,"lang":"en","type":"article","venue":"Electronic Test","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Low-density parity-check code; Turbo code; Serial concatenated convolutional codes; Concatenated error correction code; Computer science; Algorithm; Linear code; Construct (python library); Block code; Decoding methods; Computer engineering; Computer network","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":[0.001118895,0.0004910839,0.000282403,0.0007693861,0.0005204476,0.000799696,0.0004276384,0.0005887583,0.001075672],"category_scores_gemma":[0.004655031,0.0001474218,0.0002327698,0.000632038,0.0006717156,0.001181639,0.0004647038,0.0006503924,0.0003411563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028075,"about_ca_system_score_gemma":0.0008773566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003320884,"about_ca_topic_score_gemma":0.001922359,"domain_scores_codex":[0.9986872,0.0004472549,0.00004645168,0.0001620205,0.0005523872,0.0001046839],"domain_scores_gemma":[0.9972547,0.001145584,0.0001789923,0.0003335493,0.001027385,0.00005974321],"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":[0.001106792,0.0001642311,0.009899929,0.0005249375,0.0001274658,0.0004777573,0.0007578108,0.442035,0.1575339,0.1129239,0.002424733,0.2720234],"study_design_scores_gemma":[0.00003983962,0.000505005,0.002157796,0.00004578049,0.00004773998,0.0004737086,0.00007828949,0.9028597,0.08093742,0.008427504,0.00437181,0.00005523482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2125359,0.002091304,0.7694479,0.0004740044,0.00008758863,0.00007701415,0.00009536643,0.0007343263,0.01445657],"genre_scores_gemma":[0.9330402,0.0009155184,0.06350637,0.00005805767,0.00003517442,0.00004083161,0.00009077533,0.00004467793,0.002268316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003320884,"threshold_uncertainty_score":0.007459223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02046271475259484,"score_gpt":0.3316735202436826,"score_spread":0.3112108054910877,"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."}}