{"id":"W4392175264","doi":"10.1109/globecom54140.2023.10436993","title":"Joint Admission and Power Control for Big Data Access Management Using GAT","year":2023,"lang":"en","type":"article","venue":"","topic":"Age of Information Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Victoria","funders":"National Key Research and Development Program of China; Aeronautical Science Foundation of China; National Mobile Communications Research Laboratory, Southeast University; National Natural Science Foundation of China","keywords":"Joint (building); Computer science; Control (management); Big data; Power (physics); Power control; Access control; Computer security; Computer network; Operating system; Engineering; Artificial intelligence","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":[0.0007213401,0.0009799363,0.0008971363,0.0006020218,0.000679646,0.0009405781,0.001643536,0.0008321901,0.001482353],"category_scores_gemma":[0.002199044,0.0003156694,0.000430506,0.0007812023,0.0009342177,0.001865164,0.001464763,0.001282829,0.0001740355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048715,"about_ca_system_score_gemma":0.001050014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005733981,"about_ca_topic_score_gemma":0.006873979,"domain_scores_codex":[0.9994093,0.0001585692,0.00002981098,0.0001535871,0.0001324573,0.0001163195],"domain_scores_gemma":[0.9990453,0.0004533591,0.0001397797,0.00008790691,0.0001743872,0.00009919041],"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":[0.000185968,0.000167608,0.001872236,0.00007068345,0.0000480386,0.0001340263,0.0001350497,0.8410801,0.004250401,0.0137799,0.002318576,0.1359574],"study_design_scores_gemma":[0.000004185207,0.00001715436,0.00008714526,0.000001401952,0.00000412727,0.00001270216,0.00000699508,0.9967102,0.000378656,0.002612318,0.0001621111,0.000002976097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03578478,0.0002317232,0.9608981,0.0003163459,0.0000641832,0.00005750767,0.00002756981,0.0006644261,0.00195527],"genre_scores_gemma":[0.9587042,0.0001148888,0.0393208,0.0001670017,0.00004858077,0.00006053671,0.00005868135,0.00004411778,0.00148126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005733981,"threshold_uncertainty_score":0.01140118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1308510123292008,"score_gpt":0.3273313421365932,"score_spread":0.1964803298073923,"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."}}