{"id":"W1985033232","doi":"10.1049/iet-com.2014.0658","title":"Improved finite‐length Luby‐transform codes in the binary erasure channel","year":2015,"lang":"en","type":"article","venue":"IET Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Yarmouk University","keywords":"Erasure; Luby transform code; Binary erasure channel; Online codes; Tornado code; Computer science; Erasure code; Binary number; Channel (broadcasting); Algorithm; Decoding methods; Mathematics; Low-density parity-check code; Computer network; Error floor; Arithmetic; Channel capacity","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":[0.0009087923,0.0004409599,0.0005834678,0.0007528106,0.0006017258,0.0007993188,0.0008268207,0.0009305834,0.001388888],"category_scores_gemma":[0.004495071,0.0002361288,0.0002734426,0.000978418,0.001479748,0.001772068,0.001303862,0.001350949,0.0004249831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009086906,"about_ca_system_score_gemma":0.001384994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352783,"about_ca_topic_score_gemma":0.002180313,"domain_scores_codex":[0.9993624,0.0001519706,0.00002458304,0.00005632881,0.0002616011,0.0001431249],"domain_scores_gemma":[0.9984272,0.000718791,0.0002047323,0.0002967906,0.0002770878,0.00007554937],"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":[0.001209066,0.0001384186,0.00115599,0.0003062919,0.00004392282,0.0003806001,0.0007441959,0.2302692,0.06542366,0.5176362,0.004008314,0.1786842],"study_design_scores_gemma":[0.0001134623,0.0003108207,0.0005448989,0.0001390209,0.00003575946,0.0004375372,0.0001104469,0.807714,0.0806143,0.09717135,0.01266847,0.0001397789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2682746,0.002408105,0.7085792,0.001214071,0.0002549324,0.0001077935,0.0002045412,0.001302304,0.01765457],"genre_scores_gemma":[0.8766147,0.0009122728,0.115016,0.0002699313,0.00006201609,0.00007885879,0.0001400687,0.00007524026,0.006830794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002352783,"threshold_uncertainty_score":0.006593049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07969589066284656,"score_gpt":0.3190004978732516,"score_spread":0.239304607210405,"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."}}