{"id":"W2160947970","doi":"10.1109/cwit.2009.5069538","title":"Cross-layer Raptor coding for broadcasting over wireless channels with memory","year":2009,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Erasure; Computer science; Raptor code; Online codes; Fountain code; Decoding methods; Binary erasure channel; Computer network; Erasure code; Forward error correction; Tornado code; Network packet; Luby transform code; Wireless; Relay; Channel (broadcasting); Concatenated error correction code; Algorithm; Telecommunications; Channel capacity; Block code","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008439872,0.0003170121,0.0004277154,0.0003978037,0.000384223,0.0004941085,0.0004374589,0.0005393172,0.0006451491],"category_scores_gemma":[0.004101388,0.0001971995,0.000262559,0.0005113625,0.0005076954,0.0008504522,0.0004931185,0.0004376413,0.0001185878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004397744,"about_ca_system_score_gemma":0.0006117813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001378103,"about_ca_topic_score_gemma":0.001828952,"domain_scores_codex":[0.9996054,0.0001713859,0.00001643853,0.00003137769,0.0001284995,0.0000469773],"domain_scores_gemma":[0.998027,0.001364068,0.000190686,0.0002026703,0.0001935235,0.00002189073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002602826,0.00006402572,0.001271817,0.0002173913,0.000108079,0.000308306,0.0002519833,0.8251835,0.02798483,0.0582471,0.001290089,0.08481264],"study_design_scores_gemma":[0.00001221835,0.00008827895,0.00019608,0.000009631261,0.00002380239,0.0001366993,0.00001484172,0.9842174,0.008521631,0.006141194,0.000626033,0.00001214304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2659497,0.003182269,0.7251779,0.000403813,0.00004321249,0.00004924119,0.00007540549,0.000591692,0.004526752],"genre_scores_gemma":[0.942735,0.0008014881,0.05545526,0.00006133065,0.00002037408,0.00004077608,0.00003396098,0.00002234305,0.0008295406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001378103,"threshold_uncertainty_score":0.004463494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03203331859845021,"score_gpt":0.3097929057934915,"score_spread":0.2777595871950413,"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."}}