{"id":"W2060060388","doi":"10.5539/cis.v2n3p3","title":"A Hybrid Resource Allocation Strategy with Queuing in Wireless Mobile Communication Networks","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computer network; Handover; Quality of service; Channel allocation schemes; Queueing theory; Resource allocation; Blocking (statistics); Cellular network; Call blocking; Wireless; Channel (broadcasting); Radio resource management; Wireless network; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009303289,0.0004686304,0.0005791738,0.0003704058,0.0006165571,0.0009602925,0.001254914,0.0006281646,0.00106231],"category_scores_gemma":[0.0009413877,0.0002195394,0.0003033648,0.0004890274,0.0006117439,0.0009428032,0.000551194,0.0005019256,0.0002289752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008401854,"about_ca_system_score_gemma":0.001005947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003793497,"about_ca_topic_score_gemma":0.003863547,"domain_scores_codex":[0.9992513,0.0003044591,0.00004000697,0.0001058506,0.0001953117,0.0001029581],"domain_scores_gemma":[0.9995415,0.00021053,0.00003372724,0.00004133825,0.000128368,0.0000445346],"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":[0.0005848047,0.0005370625,0.00206003,0.0002322729,0.0001921904,0.0005052898,0.0004023724,0.4956004,0.06550153,0.09934068,0.00642642,0.328617],"study_design_scores_gemma":[0.00003813714,0.0001958951,0.0002126539,0.000007179094,0.00002906294,0.00009135651,0.00002980577,0.9855112,0.003455746,0.007894832,0.002506288,0.0000279341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05283995,0.001360174,0.9402418,0.0004140108,0.0002754341,0.000112224,0.00002795917,0.000639585,0.004088935],"genre_scores_gemma":[0.8696889,0.0004686546,0.1241478,0.0002961952,0.0001342291,0.0001442371,0.00004024809,0.00004113333,0.005038451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003793497,"threshold_uncertainty_score":0.007542849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163452107892984,"score_gpt":0.2589391216359843,"score_spread":0.2473046005570544,"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."}}