{"id":"W2060919708","doi":"10.1007/s12083-011-0105-7","title":"I-Swifter: Improving chunked network coding for peer-to-peer content distribution","year":2011,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Linear network coding; Computer science; Coding (social sciences); Scheduling (production processes); Distributed computing; Download; Computer network; Peer-to-peer; Encoder; Content distribution; Mathematical optimization; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001229062,0.0008192354,0.0009296528,0.001009812,0.000875333,0.0009935451,0.001983469,0.001050756,0.004701631],"category_scores_gemma":[0.006741023,0.0003192754,0.0003767488,0.001074657,0.0007845594,0.002346595,0.002039361,0.001714735,0.001239639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254755,"about_ca_system_score_gemma":0.001975833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007224031,"about_ca_topic_score_gemma":0.009753847,"domain_scores_codex":[0.9987908,0.0002294353,0.00005268459,0.0001414921,0.0006451749,0.0001404963],"domain_scores_gemma":[0.9965556,0.001124915,0.0001786268,0.0009930125,0.0009783923,0.0001694655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008535663,0.0004065752,0.001038354,0.0002282536,0.00009925816,0.000225541,0.0002612208,0.2398444,0.06830543,0.04223502,0.02366531,0.6228371],"study_design_scores_gemma":[0.00004947767,0.00009880779,0.0001439868,0.0000184993,0.00001739643,0.00009819231,0.00003405395,0.9549804,0.02965524,0.009812156,0.005062755,0.00002904459],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01800979,0.0003799058,0.9727876,0.0002247302,0.0002529907,0.0001491716,0.000143224,0.004718079,0.003334489],"genre_scores_gemma":[0.3054384,0.000419196,0.683795,0.0003186063,0.0001493273,0.000258887,0.0004506375,0.0005482912,0.008621698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007224031,"threshold_uncertainty_score":0.01572859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09307694516990196,"score_gpt":0.2922369882356663,"score_spread":0.1991600430657643,"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."}}