{"id":"W2982537683","doi":"10.1109/wcnc.2019.8885893","title":"Joint Optimization of UAV Trajectory and Radio Resource Allocation for Drive-Thru Vehicular Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Trajectory; Context (archaeology); Resource allocation; Resource (disambiguation); Quality of service; Wireless; Vehicular ad hoc network; Vehicle dynamics; Resource management (computing); Real-time computing; Simulation; Computer network; Automotive engineering; Engineering; Wireless ad hoc 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":[],"consensus_categories":[],"category_scores_codex":[0.00009676076,0.0000777457,0.0001131375,0.00005575347,0.00002621225,0.00001382657,0.00004104492,0.00007632452,0.00004083957],"category_scores_gemma":[0.00000515716,0.00007845822,0.0000284553,0.0001111065,0.0000134159,0.00008275705,0.000007495332,0.00003883572,0.000001702716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002415281,"about_ca_system_score_gemma":0.000005428683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000455979,"about_ca_topic_score_gemma":0.000002034475,"domain_scores_codex":[0.9995453,0.000007765932,0.0001803335,0.0001193108,0.00005521467,0.00009212142],"domain_scores_gemma":[0.9996965,0.00002745063,0.0000361483,0.0001587758,0.00005236902,0.00002876711],"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":[0.000003731614,0.000008468101,0.0001115555,0.00004216448,0.00001389023,1.275379e-8,0.00006973871,0.9953177,0.001369516,0.001260929,0.0005027243,0.0012996],"study_design_scores_gemma":[0.0003116706,0.00002735069,0.0006463414,0.00001299949,0.00001592467,7.463045e-7,0.00004642651,0.9956032,0.001443004,0.00001880985,0.001782609,0.000090938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03257123,0.0002539744,0.9646701,0.00004129703,0.00004630812,0.0005085519,0.000002074697,0.0001031552,0.001803257],"genre_scores_gemma":[0.9047718,0.0001969597,0.09447055,0.0000270919,0.00004956829,0.000067634,0.0001279589,0.00003314012,0.0002552735],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8722006,"threshold_uncertainty_score":0.3199433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005430039904800139,"score_gpt":0.1755771088054153,"score_spread":0.1701470689006152,"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."}}