{"id":"W3086990738","doi":"10.1109/lcomm.2020.3023074","title":"Reliable Broadcast Based on Online Fountain Codes","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glycemic Index Laboratories; University of Alberta","funders":"Natural Science Foundation of Beijing Municipality; China Scholarship Council; National Natural Science Foundation of China","keywords":"Fountain code; Computer science; Fountain; Decoding methods; Raptor code; Luby transform code; Scheme (mathematics); Code (set theory); Wireless; Theoretical computer science; Algorithm; Computer network; Telecommunications; Block code; Concatenated error correction code; Mathematics","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":[0.0002437098,0.0001621194,0.0001670139,0.0001215255,0.0002762406,0.0001291689,0.004185984,0.0000500288,0.000006154919],"category_scores_gemma":[0.0001326852,0.0001734544,0.00007939699,0.0006748016,0.0001124345,0.0002452595,0.0004019336,0.0004499317,0.00009890179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009500487,"about_ca_system_score_gemma":0.00005731585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001007263,"about_ca_topic_score_gemma":0.00002928137,"domain_scores_codex":[0.9987198,0.0001953318,0.0002664883,0.0003394648,0.0002422442,0.000236693],"domain_scores_gemma":[0.9960552,0.0004074571,0.0001251471,0.003230187,0.00007598137,0.0001060662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000938767,0.001884956,0.007973455,0.0001288963,0.0001090415,0.00006647831,0.006695458,0.03688217,0.2452724,0.02620483,0.5773799,0.09730846],"study_design_scores_gemma":[0.0003157729,0.0001692392,0.0004259965,0.00009493181,0.0000103018,0.000004965787,0.00004050378,0.9101747,0.008390526,0.0001947648,0.07980251,0.0003758167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00999414,0.00006758679,0.7445524,0.241506,0.0001795461,0.0002651407,0.00001181339,0.001604605,0.001818768],"genre_scores_gemma":[0.6047639,0.00002127583,0.3432067,0.0518767,0.000043466,0.00003568948,0.0000184013,0.00001792508,0.00001596515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8732925,"threshold_uncertainty_score":0.777867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.062511879357247,"score_gpt":0.3026936390537778,"score_spread":0.2401817596965308,"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."}}