{"id":"W2889062623","doi":"10.1109/tcomm.2018.2867436","title":"Performance Analysis and Improvement of Online Fountain Codes","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Beijing Council of Science and Technology; National Natural Science Foundation of China","keywords":"Fountain code; Fountain; Computer science; Telecommunications; Electronic engineering; Engineering; Decoding methods; Block code; Concatenated error correction code; History","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":[0.0002426545,0.00009685651,0.0001574519,0.0003700334,0.0003419601,0.00002990836,0.001153721,0.00004178997,0.000009466453],"category_scores_gemma":[0.000005574505,0.00009777515,0.00007221555,0.001124657,0.0002451028,0.0002161054,0.00002244749,0.0001713096,0.000004617634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004199971,"about_ca_system_score_gemma":0.00004099163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002633283,"about_ca_topic_score_gemma":0.001957327,"domain_scores_codex":[0.9992296,0.00005994273,0.0002573253,0.0001926786,0.0001382663,0.0001221718],"domain_scores_gemma":[0.9974151,0.0001909428,0.0001092242,0.002044676,0.0001970086,0.00004308755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003245008,0.001974622,0.003662273,0.00004951525,0.0009017552,3.811975e-7,0.00446754,0.000687841,0.04670897,0.004123894,0.0001045583,0.9372862],"study_design_scores_gemma":[0.0002876923,0.001031876,0.01084764,0.00006725667,0.000329898,0.000006275024,0.000152715,0.7436309,0.2419726,0.0007437747,0.0006071251,0.0003222983],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1326367,0.00002371067,0.865714,0.0006679177,0.00006448169,0.0001206521,0.00001670594,0.0001972746,0.0005585227],"genre_scores_gemma":[0.8427994,0.0002758494,0.1567135,0.00008249429,0.000005710328,0.00003071712,0.000002416665,0.000005403409,0.00008444026],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9369639,"threshold_uncertainty_score":0.3987155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961203989107317,"score_gpt":0.3069218964144205,"score_spread":0.2773098565233473,"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."}}