{"id":"W2093546964","doi":"10.1109/cwit.2013.6621611","title":"Streaming erasure codes under mismatched source-channel frame rates","year":2013,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Binary erasure channel; Computer science; Decoding methods; Erasure; Network packet; Erasure code; Channel (broadcasting); Frame (networking); Luby transform code; Online codes; Encoding (memory); List decoding; Code (set theory); Code rate; Raptor code; Upper and lower bounds; Channel capacity; Computer network; Algorithm; Sequential decoding; Concatenated error correction code; Block code; Mathematics; Set (abstract data type)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002230538,0.0013244,0.001216564,0.0008600717,0.0007711613,0.001259047,0.001217745,0.00135248,0.001687029],"category_scores_gemma":[0.0212044,0.0006054034,0.0003863839,0.00119839,0.001805502,0.003998969,0.002544879,0.001733572,0.0003036214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001924727,"about_ca_system_score_gemma":0.001554746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003072001,"about_ca_topic_score_gemma":0.00196869,"domain_scores_codex":[0.9983436,0.000402756,0.0001036445,0.0002088806,0.0004875579,0.000453525],"domain_scores_gemma":[0.9783588,0.01668381,0.001730452,0.001261386,0.001461296,0.0005043041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004269335,0.00004159717,0.0008487518,0.000117615,0.00002954478,0.0004578487,0.0002131021,0.8233076,0.01256194,0.1511391,0.0005682051,0.01028774],"study_design_scores_gemma":[0.0000168135,0.00005272557,0.000190024,0.00002421573,0.00001063176,0.0001010053,0.00004036519,0.9551275,0.007364176,0.03678837,0.0002589621,0.00002517972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3538567,0.001080987,0.6372198,0.0005526193,0.00007108903,0.00007190958,0.0004047223,0.0005674034,0.006174706],"genre_scores_gemma":[0.9689451,0.0007792604,0.02783623,0.00004941091,0.0000732285,0.00009140941,0.0001489257,0.00007163532,0.002004914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003072001,"threshold_uncertainty_score":0.01396489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01855447991526202,"score_gpt":0.2553868627273503,"score_spread":0.2368323828120883,"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."}}