{"id":"W3081223117","doi":"10.1109/globecom42002.2020.9322349","title":"Federated Learning for Cellular-Connected UAVs: Radio Mapping and Path Planning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Probabilistic logic; Motion planning; Path (computing); Constraint (computer-aided design); Distributed computing; Real-time computing; Computer network; Artificial intelligence; Robot; Engineering","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":[0.0008565978,0.0007681436,0.0008508567,0.0005125187,0.0005220332,0.0007392992,0.001034345,0.001071187,0.000968207],"category_scores_gemma":[0.002962722,0.0003166276,0.0005092956,0.0006827605,0.0007258073,0.0008724951,0.0009732011,0.0007152846,0.0001451086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009632393,"about_ca_system_score_gemma":0.001141824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009496211,"about_ca_topic_score_gemma":0.005685988,"domain_scores_codex":[0.9996173,0.0001154972,0.00001494453,0.0001105187,0.00006748336,0.00007429048],"domain_scores_gemma":[0.9988105,0.0006507082,0.0001790035,0.0001081524,0.0001630543,0.0000886862],"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":[0.00002137993,0.00002159505,0.00032756,0.00001044351,0.00001008377,0.00002564616,0.00001752606,0.9881655,0.0002871134,0.001286603,0.0001527852,0.009673812],"study_design_scores_gemma":[0.000002481187,0.00001045844,0.00004270702,0.000001211014,0.000001844254,0.000005776977,0.000005339412,0.9985947,0.0001237056,0.001157718,0.00005295182,0.000001038363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05101934,0.0001973117,0.9468285,0.0001946183,0.00002788368,0.00003854742,0.0000474985,0.0002566084,0.001389669],"genre_scores_gemma":[0.9180253,0.0001239058,0.08052658,0.00005624805,0.0000187757,0.00006915833,0.00007290725,0.00002276696,0.001084259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009496211,"threshold_uncertainty_score":0.01888186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140740488652586,"score_gpt":0.2149236486278426,"score_spread":0.1935162437413167,"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."}}