{"id":"W2162156798","doi":"10.1109/iwqos.2009.5201384","title":"Cooperative multicast scheduling with random network coding in WiMAX","year":2009,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multicast; Computer network; Computer science; Xcast; Source-specific multicast; Pragmatic General Multicast; Protocol Independent Multicast; IP multicast; Distributed computing; WiMAX; Distance Vector Multicast Routing Protocol; Linear network coding; Wireless; Network packet; Telecommunications","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":[0.002328883,0.0004988817,0.0006730796,0.0007659011,0.0005916632,0.0006035322,0.001030348,0.0006691572,0.0004661632],"category_scores_gemma":[0.005369117,0.0002917662,0.0003776935,0.0007728965,0.0009797319,0.001004877,0.0007760899,0.0004519739,0.000105042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00146735,"about_ca_system_score_gemma":0.001409921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005721345,"about_ca_topic_score_gemma":0.004879394,"domain_scores_codex":[0.9986352,0.0007231784,0.00003877965,0.00009733049,0.000317473,0.0001879147],"domain_scores_gemma":[0.9975241,0.001644716,0.0003166571,0.0001847624,0.0002754969,0.00005426993],"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":[0.0001583,0.00005098166,0.0003254704,0.00005130728,0.00002478329,0.0001150614,0.0001010854,0.9365721,0.00377332,0.04025478,0.0005742719,0.01799846],"study_design_scores_gemma":[0.00002027047,0.00003873115,0.00006764063,0.000005618314,0.000009924932,0.00002382126,0.00001297567,0.990053,0.0008501174,0.008569591,0.000339733,0.00000865412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1044279,0.0008887076,0.8882679,0.0003857887,0.00006387637,0.0001062326,0.0000596745,0.0003417092,0.005458163],"genre_scores_gemma":[0.9414721,0.0003386809,0.05694136,0.00007420134,0.000038879,0.0001316571,0.00003565714,0.00002330013,0.0009441806],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005721345,"threshold_uncertainty_score":0.01231647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02800489878788611,"score_gpt":0.2760680873738901,"score_spread":0.248063188586004,"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."}}