{"id":"W2922016977","doi":"10.1109/itw44776.2019.8988926","title":"An Explicit Construction of Optimal Streaming Codes for Channels With Burst and Arbitrary Erasures","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Tornado code; Online codes; Luby transform code; Erasure; Fountain code; Erasure code; Burst error; Computer science; Block code; Generalization; Code (set theory); Channel (broadcasting); Error detection and correction; Sliding window protocol; Algorithm; Concatenated error correction code; Discrete mathematics; Mathematics; Decoding methods; Telecommunications; Window (computing)","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":[0.0008119936,0.0006203992,0.0005869371,0.0005601272,0.0004705395,0.0005540646,0.0007774353,0.0007819918,0.001090529],"category_scores_gemma":[0.003221976,0.0004220345,0.0004418475,0.0004833659,0.001312557,0.001379737,0.002004761,0.001594747,0.0002882077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005407144,"about_ca_system_score_gemma":0.0010582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002951054,"about_ca_topic_score_gemma":0.0003470977,"domain_scores_codex":[0.9994417,0.0001656243,0.00002554831,0.00006035868,0.0002391074,0.00006771517],"domain_scores_gemma":[0.9985399,0.00070873,0.0001399427,0.0003552634,0.0001716118,0.00008447521],"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":[0.00009943562,0.00004846174,0.0004038889,0.000188149,0.00002153549,0.000155266,0.000208346,0.1272976,0.02018133,0.8034855,0.001680447,0.04623014],"study_design_scores_gemma":[0.00003866327,0.0001120875,0.0001928623,0.00006102766,0.00001764796,0.0002709863,0.00003868216,0.7196581,0.01871218,0.2542754,0.006574926,0.00004750356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03529044,0.0002608809,0.9596972,0.0002010787,0.00004615004,0.00003930379,0.00006705338,0.0001923049,0.004205601],"genre_scores_gemma":[0.5107726,0.0008507048,0.4836639,0.0001925062,0.0001188581,0.0001490249,0.0002072938,0.0001812947,0.003863738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001090529,"threshold_uncertainty_score":0.004294276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03820268590730794,"score_gpt":0.2878088522016902,"score_spread":0.2496061662943822,"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."}}