{"id":"W2289194323","doi":"10.1109/glocom.2015.7417760","title":"Mobile Virtual Network Admission Control and Resource Allocation for Wireless Network Virtualization: A Robust Optimization Approach","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Admission control; Computer network; Quality of service; Wireless network; Resource allocation; Call Admission Control; Virtualization; Distributed computing; Network virtualization; Wireless; Cloud computing; Telecommunications","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":[0.001320756,0.0004057778,0.0005015749,0.00006473767,0.0008193527,0.0005991382,0.002528893,0.0003139844,0.000006498138],"category_scores_gemma":[0.0001680373,0.0004128299,0.00009910003,0.001132091,0.0002503253,0.0008188951,0.0006896976,0.0002686386,0.00001088635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339669,"about_ca_system_score_gemma":0.0005270817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007225058,"about_ca_topic_score_gemma":0.00007142137,"domain_scores_codex":[0.9968178,0.0006228955,0.0007129874,0.0007348139,0.0004512966,0.0006602078],"domain_scores_gemma":[0.9951888,0.0003903133,0.0004674718,0.002446581,0.0009972655,0.0005095616],"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":[0.00007460612,0.0001536788,0.0007351366,0.00001086881,0.00004305395,3.485461e-7,0.00026365,0.8009611,0.000001518049,0.1284754,0.05264169,0.0166389],"study_design_scores_gemma":[0.001699685,0.000324891,0.0001111145,0.00008988156,0.00005573616,0.00002196352,0.0002175711,0.9656973,0.000001437324,0.003175299,0.02815558,0.0004495599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002593927,0.002720651,0.9906061,0.001275773,0.0004623683,0.001380421,0.00004561132,0.000454011,0.002795688],"genre_scores_gemma":[0.5985789,0.0007287468,0.397752,0.001020225,0.0004303613,0.0007256272,0.000530289,0.00003634509,0.000197469],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5983195,"threshold_uncertainty_score":0.9998323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05419210974851078,"score_gpt":0.284099364546231,"score_spread":0.2299072547977202,"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."}}