{"id":"W3165716753","doi":"10.1109/tvt.2021.3083255","title":"Drone-Small-Cell-Assisted Resource Slicing for 5G Uplink Radio Access Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Nanjing University of Posts and Telecommunications; Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Telecommunications link; Computer science; Provisioning; Computer network; Quality of service; Base station; Resource allocation; User equipment; Distributed computing","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":[0.0006415769,0.0007245702,0.0006918761,0.0003311735,0.0005721364,0.0006428371,0.001018679,0.000572123,0.0014154],"category_scores_gemma":[0.001731779,0.0003868327,0.0005482788,0.0007133438,0.0005463215,0.001188278,0.0009612037,0.0007724538,0.0001776276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001080863,"about_ca_system_score_gemma":0.001215345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008930819,"about_ca_topic_score_gemma":0.01129061,"domain_scores_codex":[0.9994683,0.000194457,0.00001631117,0.0001036525,0.0001113819,0.0001058617],"domain_scores_gemma":[0.9992219,0.0004004094,0.0001179081,0.0001051582,0.00007613895,0.00007850531],"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":[0.00006772471,0.00003662418,0.000535398,0.00004667964,0.00001785853,0.00008899664,0.00005144977,0.9614431,0.003772258,0.007232099,0.0008740211,0.02583386],"study_design_scores_gemma":[0.000003423617,0.00002382361,0.00008792096,0.000002772402,0.000003882797,0.00001939633,0.00001809289,0.9974256,0.000584022,0.001503885,0.0003235541,0.000003513225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0591666,0.0004705765,0.9370185,0.0001847718,0.0000398947,0.00008670773,0.00009908181,0.0002971826,0.002636758],"genre_scores_gemma":[0.8621923,0.0004061492,0.1360892,0.00007352389,0.00002250793,0.00007556465,0.0001447354,0.00003925353,0.0009567313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008930819,"threshold_uncertainty_score":0.01775771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01201941378220025,"score_gpt":0.2225661670719739,"score_spread":0.2105467532897737,"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."}}