{"id":"W2119852332","doi":"10.1109/glocom.2007.301","title":"Deterministic Design of Low-Density Parity-Check Codes for Binary Erasure Channels","year":2007,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Low-density parity-check code; Erasure; Binary erasure channel; Computer science; Algorithm; Binary number; Erasure code; Theoretical computer science; Code (set theory); Decoding methods; Mathematics; Channel capacity; Channel (broadcasting); Arithmetic; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001832537,0.0001672041,0.0002714607,0.0001551153,0.0001128942,0.00004842116,0.0007908546,0.0001219084,0.000004309125],"category_scores_gemma":[0.0003304366,0.000156469,0.00009085085,0.0003020229,0.00006634521,0.0002056512,0.0002112732,0.0001236104,0.000006321575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004211942,"about_ca_system_score_gemma":0.00007440319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004122765,"about_ca_topic_score_gemma":0.00002730203,"domain_scores_codex":[0.9985947,0.00006588493,0.0003490474,0.0003870278,0.0002330476,0.000370314],"domain_scores_gemma":[0.9980242,0.0008786835,0.0001549645,0.0005909505,0.0002626509,0.00008857074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001060664,0.002794833,0.01910392,0.001940108,0.0002911857,0.0005423859,0.01058095,0.00241881,0.605993,0.1291533,0.03559179,0.1905291],"study_design_scores_gemma":[0.000395932,0.001050492,0.003290667,0.0001413264,0.00001963953,0.00006009346,0.00006696945,0.2234984,0.7511008,0.01972246,0.0001524486,0.0005007513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04320557,0.00002628851,0.9547025,0.00009586581,0.0004094348,0.0004866032,0.000001314163,0.0006414427,0.0004309574],"genre_scores_gemma":[0.6466064,0.000002122214,0.353038,0.000122062,0.00003487422,0.00001273075,6.713912e-7,0.000009660643,0.0001734751],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6034008,"threshold_uncertainty_score":0.6380622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04368098598646759,"score_gpt":0.3015250718335133,"score_spread":0.2578440858470458,"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."}}