{"id":"W2973003915","doi":"10.1109/focs46700.2020.00050","title":"LDPC Codes Achieve List Decoding Capacity","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation; National Science Foundation","keywords":"Low-density parity-check code; Decoding methods; Mathematics; Code (set theory); List decoding; Discrete mathematics; Combinatorics; Concatenated error correction code; Algorithm; Computer science; Block code","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005468096,0.0004186823,0.0005002277,0.0001588741,0.0001544895,0.000604891,0.003447222,0.0003465419,0.00003554433],"category_scores_gemma":[0.0003461821,0.0004146824,0.0002343628,0.0003038821,0.0000685402,0.0002544657,0.006061168,0.001430345,0.00008353509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001913569,"about_ca_system_score_gemma":0.0001839973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102141,"about_ca_topic_score_gemma":0.0003647865,"domain_scores_codex":[0.9973677,0.0001632101,0.0004294005,0.001211134,0.0004292646,0.0003992797],"domain_scores_gemma":[0.9975749,0.000231209,0.0002806848,0.001564952,0.0001524574,0.0001957414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004022214,0.0004039465,0.009334957,0.001367412,0.0004038227,0.0003275051,0.01560598,0.0006406087,0.01641619,0.6857433,0.1273403,0.1423758],"study_design_scores_gemma":[0.0002262267,0.0002379534,0.0009924058,0.0006510894,0.00005219536,0.00008164833,0.00007862129,0.5016589,0.08977767,0.3970303,0.006812108,0.002400944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00855909,0.00005159433,0.9368371,0.004318299,0.001479768,0.0003539959,0.000008620758,0.00529664,0.04309493],"genre_scores_gemma":[0.5320355,0.00002147964,0.4666445,0.0008401934,0.0001516794,0.00003706058,0.000005177134,0.00002319307,0.0002412589],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5234764,"threshold_uncertainty_score":0.9998305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0839619599965839,"score_gpt":0.298660119496544,"score_spread":0.2146981594999601,"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."}}