{"id":"W2042968549","doi":"10.1145/2345396.2345436","title":"Advanced adaptive call admission control for mobile cellular networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Call Admission Control; Computer science; Computer network; Handover; Call blocking; Quality of service; Unavailability; Blocking (statistics); Cellular network; Base station; Bandwidth (computing); Voice over IP; Admission control; Call control; Wireless network; Real-time computing; Wireless; Telecommunications; The Internet","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":[0.0005705824,0.0005276856,0.000427054,0.0005645445,0.0006504693,0.0009324241,0.001055137,0.0007603691,0.001910024],"category_scores_gemma":[0.002772405,0.0001219374,0.0002392845,0.0007942452,0.0006480384,0.0005460505,0.0004986668,0.001249255,0.0004460416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000882611,"about_ca_system_score_gemma":0.0008501922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006748367,"about_ca_topic_score_gemma":0.003496301,"domain_scores_codex":[0.9991836,0.0001798381,0.00002664063,0.00008099339,0.0004488883,0.00007991249],"domain_scores_gemma":[0.9992672,0.0002870719,0.00007403336,0.00007371182,0.0002602487,0.00003780569],"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":[0.0003410094,0.0001710061,0.001223266,0.0002539604,0.00007611913,0.0003224211,0.0002264436,0.2756079,0.03354758,0.1069885,0.01069463,0.5705471],"study_design_scores_gemma":[0.00003290327,0.0000671935,0.0003647933,0.00002723782,0.00001816099,0.0001039242,0.00001792655,0.9658544,0.00288313,0.01958258,0.01101752,0.00003020195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01892803,0.008688302,0.9584973,0.0005641825,0.0007528171,0.000153442,0.00008261651,0.001678143,0.01065512],"genre_scores_gemma":[0.8909228,0.004890099,0.09251337,0.0003231326,0.0008384799,0.0002752491,0.0002235563,0.00007638853,0.009936771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006748367,"threshold_uncertainty_score":0.0134182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02393780598523525,"score_gpt":0.2927257652951779,"score_spread":0.2687879593099426,"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."}}