{"id":"W4383113475","doi":"10.1109/jiot.2023.3292305","title":"Energy Conservative Data Aggregation for IoT Devices: An Aerial Wake-Up Radio Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Engineering and Physical Sciences Research Council; King Abdullah University of Science and Technology","keywords":"Computer science; Testbed; Software deployment; Energy harvesting; Wireless; Real-time computing; Scalability; Energy (signal processing); Computer 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":[0.0004016689,0.0001152317,0.0001654451,0.0001403366,0.0000578718,0.0001060717,0.0005163754,0.00008171155,0.00002015327],"category_scores_gemma":[0.00003249539,0.0001105566,0.00004580869,0.0001964097,0.00003073419,0.0005571254,0.00003369537,0.0001323249,0.00000392025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004929291,"about_ca_system_score_gemma":0.00003751396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007523363,"about_ca_topic_score_gemma":0.00001750582,"domain_scores_codex":[0.9991364,0.0000256671,0.0003611765,0.0001604787,0.0001531446,0.0001631192],"domain_scores_gemma":[0.9992934,0.00006193121,0.0001689617,0.0002594793,0.0001470956,0.00006913552],"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":[0.0004691211,0.0002830597,0.000659925,0.0005455987,0.001149146,0.000009343777,0.01780082,0.5754932,0.03317744,0.01260106,0.2295063,0.128305],"study_design_scores_gemma":[0.0004680419,0.00005152441,0.00005504003,0.00005603993,0.00003127516,0.0000345366,0.0001800963,0.9819905,0.007570069,0.0006656455,0.008774016,0.0001232207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.097359,0.000081058,0.9006269,0.00007793217,0.001105155,0.0001370556,0.000044514,0.0001464995,0.0004219132],"genre_scores_gemma":[0.9386895,0.0001319355,0.05930494,0.00009494237,0.0007164426,0.00003413673,0.0004981493,0.0000572938,0.0004727163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8413305,"threshold_uncertainty_score":0.4508368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05154021251068547,"score_gpt":0.2726656183549607,"score_spread":0.2211254058442752,"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."}}