{"id":"W3199784387","doi":"10.22215/etd/2008-06194","title":"Performance estimation, code construction and decoding of finite-length low-density parity-check codes","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"University of Ottawa","keywords":"Decoding methods; Low-density parity-check code; Computer science; Parity bit; Parity (physics); Statistics; Arithmetic; Mathematics; Algorithm; Physics; Particle physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001965648,0.0004649819,0.0004328144,0.0008816696,0.0004382705,0.001278479,0.0006673145,0.0009399282,0.0009556354],"category_scores_gemma":[0.01650273,0.0003030075,0.0002589745,0.0006224254,0.0006390684,0.001053637,0.0009243675,0.0006416686,0.0004974946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255877,"about_ca_system_score_gemma":0.001854084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004023208,"about_ca_topic_score_gemma":0.004669748,"domain_scores_codex":[0.9985172,0.0006221111,0.00005924317,0.0001689452,0.0004872957,0.0001451449],"domain_scores_gemma":[0.9925511,0.005002911,0.0004659832,0.0007396619,0.001134455,0.0001059791],"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":[0.001219311,0.0002246565,0.02853549,0.0003692289,0.0001410514,0.0002198388,0.0003682727,0.6281332,0.07484309,0.03178257,0.002745357,0.2314179],"study_design_scores_gemma":[0.00002226457,0.0001208922,0.004061525,0.00001946863,0.00002023134,0.00009584313,0.00003546348,0.9490277,0.04070944,0.005356343,0.0005041247,0.00002664243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5871651,0.0008571375,0.4048735,0.0006875074,0.00005581366,0.00009127954,0.0005206318,0.001516924,0.004232045],"genre_scores_gemma":[0.9479972,0.0002379487,0.04919617,0.00004088864,0.00002590995,0.00005472758,0.0008427829,0.00008345595,0.001521048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004023208,"threshold_uncertainty_score":0.01039541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506320686093333,"score_gpt":0.2621161058208228,"score_spread":0.2470528989598895,"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."}}