{"id":"W4210932344","doi":"10.1002/ett.4464","title":"Resource optimization in UAV‐assisted wireless networks—A comprehensive survey","year":2022,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Context (archaeology); Wireless sensor network; Open research; Resource (disambiguation); Resource management (computing); Wireless network; Quality of service; Throughput; Wireless; Resource allocation; Computer network; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002529323,0.000212798,0.0002229568,0.0007530152,0.0008553618,0.00004095876,0.0008482186,0.0001220761,0.0001274857],"category_scores_gemma":[0.00001352112,0.0002673201,0.00006111238,0.00280706,0.0001044291,0.0001339714,0.0000487116,0.0008691115,0.000006727189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382012,"about_ca_system_score_gemma":0.00002097386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001089433,"about_ca_topic_score_gemma":0.0002885211,"domain_scores_codex":[0.9986336,0.0001892019,0.0004419148,0.0002647321,0.0001700528,0.0003005248],"domain_scores_gemma":[0.9983098,0.0002817276,0.00008003269,0.001232064,0.00007203557,0.00002436249],"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":[0.00001111193,0.0001558098,0.0001662712,0.000005922229,0.00002656941,4.709182e-7,0.0001078463,0.9687418,0.00005208205,0.0001796477,0.0003234981,0.030229],"study_design_scores_gemma":[0.0003195548,0.00003691953,0.002281735,0.00001421465,0.00001420558,0.000007408542,0.001838986,0.989095,0.0001635236,0.00005017754,0.005888822,0.0002894383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.023087,0.0007811917,0.9690644,0.001302407,0.0001122996,0.0006399904,0.00007749021,0.00397352,0.0009617034],"genre_scores_gemma":[0.9742222,0.001699472,0.02245001,0.00003590203,0.000003606909,0.001117504,0.000346376,0.00006066189,0.00006419908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9511353,"threshold_uncertainty_score":0.9999779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211285196502096,"score_gpt":0.2393045411872001,"score_spread":0.2181760215369905,"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."}}