{"id":"W2043153978","doi":"10.1049/iet-com.2013.0558","title":"Uplink scheduling solution for enhancing throughput and fairness in relayed long‐term evolution networks","year":2014,"lang":"en","type":"article","venue":"IET Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Telecommunications link; Computer science; Maximum throughput scheduling; Term (time); Scheduling (production processes); Throughput; Computer network; Fairness measure; Telecommunications; Dynamic priority scheduling; Mathematical optimization; Round-robin scheduling; Wireless; Quality of service; 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":[0.00111164,0.0005522497,0.000747413,0.000342518,0.0006138087,0.0008679475,0.0008222073,0.0006073536,0.001809784],"category_scores_gemma":[0.002321621,0.000215176,0.0003274756,0.0006047725,0.0004741591,0.0006457326,0.0007311977,0.0005593229,0.0002127768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048176,"about_ca_system_score_gemma":0.001583713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003760816,"about_ca_topic_score_gemma":0.003549322,"domain_scores_codex":[0.9994305,0.0002227672,0.00002367107,0.00008433256,0.0001155554,0.0001231557],"domain_scores_gemma":[0.9991686,0.000443948,0.00009594651,0.00004609191,0.0001912464,0.00005409771],"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":[0.0001204112,0.00005567553,0.0003049713,0.00006854263,0.00002454134,0.0001091142,0.00009400798,0.9415066,0.004871132,0.02212553,0.001324108,0.02939535],"study_design_scores_gemma":[0.000006799131,0.00003067493,0.00005112706,0.000002333999,0.000004645323,0.00001329466,0.00001532787,0.9963715,0.0005572837,0.002683019,0.000261038,0.000003062122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04123652,0.0004553573,0.9527385,0.0002296041,0.0000991035,0.00005341248,0.00003612984,0.000113075,0.005038263],"genre_scores_gemma":[0.914528,0.0003505697,0.08179197,0.00008008301,0.00008323266,0.00006069533,0.00003777169,0.00003309778,0.003034589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003760816,"threshold_uncertainty_score":0.007605076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04230175406491025,"score_gpt":0.3090671617321022,"score_spread":0.2667654076671919,"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."}}