{"id":"W1988384836","doi":"10.1109/tcomm.2015.2396916","title":"Queue-Aware Transmission Scheduling for Cooperative Wireless Communications","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Dynamic priority scheduling; Mathematical optimization; Fair-share scheduling; Round-robin scheduling; Scheduling (production processes); Rate-monotonic scheduling; Queue; Computer network; Mathematics; Quality of service","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007986386,0.0003133565,0.0003342785,0.0002844414,0.002113485,0.0002916004,0.006197935,0.0001537686,0.0000171085],"category_scores_gemma":[0.00003117578,0.0003222542,0.0002061988,0.001151796,0.0003473673,0.0008210823,0.00008828632,0.0007338129,0.00008790977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002468436,"about_ca_system_score_gemma":0.0004758609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002365773,"about_ca_topic_score_gemma":0.000266387,"domain_scores_codex":[0.9975364,0.0007237248,0.0006064185,0.0004542757,0.0003033736,0.0003758556],"domain_scores_gemma":[0.9906036,0.001106095,0.0001691262,0.006708163,0.001063886,0.0003491434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001081919,0.002733557,0.00001287035,0.00003736001,0.0002837596,0.000001167,0.01082742,0.02789948,0.004162681,0.2769224,0.003219896,0.6737912],"study_design_scores_gemma":[0.001346132,0.0002225832,0.000008708222,0.000145169,0.00004319875,0.0000115232,0.0005549458,0.9183315,0.005665161,0.001750906,0.07143842,0.0004817473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001603433,0.001130803,0.974417,0.01979792,0.000280183,0.0009194408,0.00004102151,0.0005240696,0.002729253],"genre_scores_gemma":[0.8274953,0.003880562,0.1662448,0.0007730963,0.00002373582,0.0008548306,0.00005032942,0.00003727988,0.0006399826],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.890432,"threshold_uncertainty_score":0.9999229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1322445801214301,"score_gpt":0.3476252542649352,"score_spread":0.2153806741435051,"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."}}