{"id":"W7086737277","doi":"10.1109/tvt.2025.3619529","title":"UAV Aided Integrated Sensing, Communication and Computing: Optimization via Federated Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Overhead (engineering); Software deployment; State (computer science); Resource (disambiguation); Federated learning; Baseline (sea); Kalman filter; Wireless; The Internet","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":[0.0009719076,0.0005691509,0.0007728865,0.0003358304,0.0004305247,0.0009024076,0.001043362,0.000802701,0.0007367332],"category_scores_gemma":[0.001547271,0.000241919,0.000456013,0.0004186827,0.0006432499,0.0009426344,0.001301689,0.0008121979,0.0001749119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006001399,"about_ca_system_score_gemma":0.001375085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003975852,"about_ca_topic_score_gemma":0.003084847,"domain_scores_codex":[0.9994623,0.0001318881,0.00002485652,0.0001354154,0.0001337056,0.0001117749],"domain_scores_gemma":[0.9994023,0.0002353401,0.00009468955,0.00009613338,0.0001208655,0.00005070474],"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":[0.00007461765,0.00006898741,0.0008412361,0.00003155015,0.00003479978,0.00005211084,0.0000336203,0.940073,0.002953738,0.005376938,0.000528968,0.04993048],"study_design_scores_gemma":[0.000003234267,0.00001815694,0.00006192371,0.000001767065,0.000002743265,0.000007306587,0.000005463135,0.9977731,0.0005224236,0.001453089,0.0001488456,0.000001955761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01789219,0.0001214571,0.9802749,0.00009470643,0.00002629988,0.00002308083,0.00001682032,0.0002760364,0.001274576],"genre_scores_gemma":[0.919772,0.0001080369,0.07865737,0.00008344021,0.00001901813,0.00005713215,0.00004963912,0.00001945022,0.001233925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003975852,"threshold_uncertainty_score":0.007905364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006272526596823032,"score_gpt":0.2050999524819937,"score_spread":0.1988274258851707,"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."}}