{"id":"W4379745810","doi":"10.1109/lnet.2023.3283936","title":"Heterogeneous GNN-RL-Based Task Offloading for UAV-Aided Smart Agriculture","year":2023,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Nokia","keywords":"Computer science; Reinforcement learning; Robustness (evolution); Internet of Things; Task (project management); Edge computing; Distributed computing; Network topology; Enhanced Data Rates for GSM Evolution; Embedded system; Real-time computing; Computer network; Artificial intelligence; Engineering","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":[0.0003276487,0.0005845214,0.0004642933,0.0001960316,0.0003021861,0.0003834967,0.0007356205,0.0004891351,0.001452251],"category_scores_gemma":[0.000668826,0.0001703168,0.0002169628,0.0001718563,0.0003929173,0.0004564207,0.0006151942,0.0004852576,0.0002145712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005322209,"about_ca_system_score_gemma":0.0006213809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005351478,"about_ca_topic_score_gemma":0.007546919,"domain_scores_codex":[0.9998472,0.00002731355,0.000005470129,0.0000402336,0.00003220761,0.00004761874],"domain_scores_gemma":[0.9997532,0.00009946983,0.00003949357,0.00002207374,0.00005637631,0.00002941632],"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":[0.00007950959,0.00005465156,0.0004017187,0.00002280705,0.00001271307,0.00005297092,0.00001827549,0.9737309,0.004344371,0.0008182032,0.0006110502,0.01985282],"study_design_scores_gemma":[0.000004410185,0.00001622303,0.00006661878,0.000001271097,0.000001910834,0.000003922029,0.000003994577,0.9990885,0.0003508928,0.0003435369,0.0001171716,0.00000149665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321964,0.0004335999,0.8561401,0.0003130244,0.0001570738,0.00006975812,0.00006842714,0.0008325026,0.009789196],"genre_scores_gemma":[0.9837256,0.00003947231,0.01446815,0.00006238031,0.00001176872,0.00002863498,0.00003419642,0.00002474882,0.001605066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005351478,"threshold_uncertainty_score":0.01064068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01245797320812924,"score_gpt":0.2031550092623449,"score_spread":0.1906970360542156,"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."}}