{"id":"W2755417146","doi":"10.1007/s12083-017-0594-0","title":"Adaptive Flow Rate Control for Network Utility Maximization Subject to QoS Constraints in Wireless Multi-hop Networks","year":2017,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Research and Development Corporation of Newfoundland and Labrador; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Quality of service; Hop (telecommunications); Computer network; Utility maximization; Wireless network; Maximization; Flow control (data); Wireless; Control (management); Distributed computing; Mathematical optimization; Telecommunications; Artificial intelligence; Mathematics; Mathematical economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009731056,0.0004373743,0.0005463707,0.0001438293,0.0008356731,0.0002840271,0.0004670344,0.0002291602,0.000008041366],"category_scores_gemma":[0.0001263379,0.0005155578,0.00008082602,0.0005772968,0.0001123023,0.0002014079,0.0001180816,0.000327862,0.00001575062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001558535,"about_ca_system_score_gemma":0.00003154898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002453041,"about_ca_topic_score_gemma":0.0004091786,"domain_scores_codex":[0.9974068,0.00007283715,0.0005928489,0.0007590927,0.0002483018,0.0009201402],"domain_scores_gemma":[0.9977809,0.0003789562,0.0001630544,0.0008039783,0.0004542893,0.0004187758],"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":[0.00009234926,0.00003129259,0.002591811,0.00001623006,0.00003775094,0.000001025462,0.0001354067,0.8631659,0.00002864238,0.0004942963,0.002635677,0.1307697],"study_design_scores_gemma":[0.001226925,0.00004058175,0.009434184,0.0001504118,0.00004136991,0.000002134892,0.00004159056,0.9727335,0.00001960795,0.0003058917,0.01547109,0.0005326887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002010339,0.0001462782,0.9913711,0.0009544285,0.0007667983,0.003849854,0.0001163831,0.000352843,0.000432005],"genre_scores_gemma":[0.9212871,0.00007611089,0.07271859,0.0003730407,0.001939562,0.003112533,0.0001806618,0.0001186694,0.0001936939],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9192768,"threshold_uncertainty_score":0.9997296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139233600541616,"score_gpt":0.2666702693977178,"score_spread":0.2452779333923016,"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."}}