{"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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001450295,0.0001387201,0.0001436846,0.0001425143,0.0002498858,0.0001327058,0.005526125,0.00008360673,0.000001291756],"category_scores_gemma":[0.0002649706,0.0001107351,0.00005895058,0.0008252178,0.0001172267,0.0004237911,0.0006118448,0.0005332868,0.00002089041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007457878,"about_ca_system_score_gemma":0.0001359205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004741772,"about_ca_topic_score_gemma":0.001456615,"domain_scores_codex":[0.9986708,0.0003786485,0.0002713264,0.0002129655,0.0002170784,0.0002491731],"domain_scores_gemma":[0.9954311,0.0007238078,0.00008680444,0.003527225,0.0001667191,0.00006429922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009432299,0.005358965,0.005577221,0.0001245462,0.0001886713,0.00005186966,0.3987718,0.001791033,0.004932924,0.3068675,0.09511362,0.1811275],"study_design_scores_gemma":[0.0009726564,0.0005485381,0.003197036,0.0001575214,0.00002538783,0.00006684312,0.004594817,0.8598064,0.00137111,0.06653262,0.06195005,0.0007770087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01887836,0.005167148,0.6873381,0.1828408,0.001020168,0.002976412,0.00004465829,0.003744845,0.09798956],"genre_scores_gemma":[0.9513708,0.0001988852,0.04729026,0.0007854696,0.00002049533,0.0002210693,0.00001406178,0.00001198086,0.00008694615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9324925,"threshold_uncertainty_score":0.9998544,"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."}}