{"id":"W2036184214","doi":"10.1145/1143549.1143676","title":"An efficient predictive admission control policy for heterogenous wireless bandwidth allocation in next generation mobile networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Handover; Computer network; Wireless network; Bandwidth allocation; Blocking (statistics); Wireless; Bandwidth (computing); Channel allocation schemes; Dynamic bandwidth allocation; Distributed computing; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0007253982,0.000178867,0.0002088327,0.0002861511,0.0002735771,0.0003431512,0.001099191,0.0001468087,0.000006254562],"category_scores_gemma":[0.00002283255,0.000165307,0.00006163568,0.0007323232,0.00004941486,0.0005426422,0.0001316381,0.000184694,0.000004384593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003113672,"about_ca_system_score_gemma":0.000239799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005873659,"about_ca_topic_score_gemma":0.000399484,"domain_scores_codex":[0.9978214,0.000368862,0.0004393647,0.0005089057,0.0003569028,0.0005045922],"domain_scores_gemma":[0.9981782,0.0002230818,0.0001235028,0.001031486,0.0002895214,0.0001542526],"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":[0.00003857656,0.0003760721,0.000314434,0.000004818499,0.000005585723,7.505673e-7,0.000149961,0.94774,0.005735146,0.01262062,0.0002521067,0.03276193],"study_design_scores_gemma":[0.00112975,0.000316018,0.00158245,0.00001521773,0.000002516263,0.000003034574,0.00002035902,0.9945558,0.001789665,0.0001752285,0.0002406806,0.000169327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.153368,0.0002635058,0.8440089,0.0005528491,0.00008862241,0.001398089,0.000003150985,0.0001573488,0.0001595872],"genre_scores_gemma":[0.991257,0.00005108258,0.006957917,0.0002077377,0.000450037,0.0008815238,0.0001067804,0.00002106246,0.00006685308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8378891,"threshold_uncertainty_score":0.6741022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02407964178878745,"score_gpt":0.2969935157389663,"score_spread":0.2729138739501788,"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."}}