{"id":"W3169817290","doi":"10.1109/jsac.2021.3088626","title":"UAV-LEO Integrated Backbone: A Ubiquitous Data Collection Approach for B5G Internet of Remote Things Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":161,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Relay; Data collection; Upload; Cache; Backhaul (telecommunications); Energy consumption; Data transmission; Telecommunications link; Real-time computing; Computer network; Base station","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004482607,0.0007086719,0.0005947792,0.0003758408,0.0005759548,0.0006995345,0.0009372883,0.0005884327,0.000774009],"category_scores_gemma":[0.0008096154,0.0002539987,0.0004200411,0.0007626819,0.0004139703,0.001332839,0.001142264,0.0006511574,0.0001939517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005579976,"about_ca_system_score_gemma":0.0008145486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004310753,"about_ca_topic_score_gemma":0.006315666,"domain_scores_codex":[0.9996347,0.0001057715,0.00001296186,0.00008451212,0.00009567158,0.00006631041],"domain_scores_gemma":[0.9997714,0.00006912109,0.00003931818,0.00004017826,0.00005311781,0.00002684906],"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":[0.0001793267,0.0001562946,0.001627268,0.000167306,0.00006714407,0.0003985162,0.0002474262,0.7564448,0.01847225,0.02554195,0.003832038,0.1928658],"study_design_scores_gemma":[0.000006289684,0.00006717338,0.0001717324,0.000005417498,0.000009367564,0.00006368478,0.00004129629,0.9947202,0.001193023,0.002453094,0.001261699,0.00000687978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02212927,0.0005083868,0.9740337,0.0001656779,0.00004591067,0.00006562048,0.00004096224,0.0002044701,0.002806022],"genre_scores_gemma":[0.7524926,0.0007069993,0.2441545,0.0001570287,0.00006248384,0.0001485127,0.0001551088,0.00004517562,0.002077517],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004310753,"threshold_uncertainty_score":0.008571327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03988887690602254,"score_gpt":0.27726215395561,"score_spread":0.2373732770495875,"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."}}