{"id":"W4401110095","doi":"10.1109/cai59869.2024.00094","title":"Efficient Offloading in UAV-MEC IoT Networks: Leveraging Digital Twins and Energy Harvesting","year":2024,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Internet of Things; Energy harvesting; Energy (signal processing); Efficient energy use; Computer network; Embedded system; Electrical 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":[0.0002634249,0.0003950248,0.0003606543,0.0002140728,0.000427809,0.0005764895,0.0004924567,0.0003416207,0.0006859757],"category_scores_gemma":[0.0007347353,0.0001399744,0.0001892349,0.0003316091,0.0004156554,0.0009721761,0.0007969235,0.0002882358,0.0000729843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004465973,"about_ca_system_score_gemma":0.0003928501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001361474,"about_ca_topic_score_gemma":0.002939031,"domain_scores_codex":[0.9998679,0.00003288101,0.000005652257,0.0000282438,0.00002854802,0.00003671922],"domain_scores_gemma":[0.9997521,0.0001203794,0.00003292012,0.00003679373,0.00002812753,0.00002973963],"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":[0.0001180802,0.00005562198,0.001088115,0.00004985851,0.00001777945,0.0001996831,0.00006304515,0.9239533,0.01890704,0.01173434,0.0005669593,0.04324622],"study_design_scores_gemma":[0.000002609601,0.00002862679,0.0000998054,0.000002249849,0.000003346783,0.00002573239,0.00002215662,0.9955455,0.001914972,0.002038236,0.000313771,0.000002854221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2358461,0.0002937142,0.7552788,0.0001689098,0.00006830393,0.00004843122,0.000038043,0.0001433765,0.008114314],"genre_scores_gemma":[0.9682216,0.000078357,0.03090005,0.00002665157,0.000005546266,0.00001452117,0.00001933793,0.0000114354,0.0007224281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001361474,"threshold_uncertainty_score":0.003240347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323914605103048,"score_gpt":0.2138788730647252,"score_spread":0.2006397270136947,"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."}}