{"id":"W2124158303","doi":"10.1109/twc.2010.061810.091283","title":"Congestion-Based Pricing Resource Management in Broadband Wireless Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Computer network; Quality of service; Network congestion; Network traffic control; Wireless broadband; Wireless network; Fairness measure; Provisioning; Bandwidth allocation; Wireless; Network packet; Telecommunications; Throughput","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"],"consensus_categories":[],"category_scores_codex":[0.0002123047,0.0003176079,0.0002827551,0.000432593,0.0004073512,0.00006820094,0.0008147266,0.00023489,0.00004041279],"category_scores_gemma":[0.000002364107,0.000386165,0.00009219645,0.001068214,0.0001982643,0.0002233091,0.000007405994,0.001325844,0.00002239541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001739326,"about_ca_system_score_gemma":0.00002366699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002075429,"about_ca_topic_score_gemma":0.0008968473,"domain_scores_codex":[0.9983963,0.0001084512,0.0005238323,0.00031794,0.0002193582,0.0004341514],"domain_scores_gemma":[0.9974266,0.0004354039,0.00008220123,0.001859828,0.00006765359,0.0001282782],"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":[0.00001941578,0.0001816637,0.00007397707,0.00002548568,0.00003788226,0.000002672226,0.00009567444,0.9154293,0.0005942966,0.0008773915,0.00004942932,0.08261279],"study_design_scores_gemma":[0.0008415914,0.00001802743,0.0005469852,0.0001468971,0.00003884053,0.000003704868,0.00008524011,0.9947578,0.001545726,0.00004000819,0.001588904,0.0003862722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05036456,0.0001086517,0.9440916,0.0002415053,0.0004793195,0.0006263562,0.0000151404,0.0007440873,0.003328737],"genre_scores_gemma":[0.9835369,0.000839136,0.01474646,0.0001061506,0.0000366128,0.0004640646,0.00005483265,0.0001092831,0.0001065474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9331723,"threshold_uncertainty_score":0.999859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099391798208488,"score_gpt":0.2340775657553655,"score_spread":0.2230836477732806,"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."}}