{"id":"W4200004457","doi":"10.1109/vtc2021-fall52928.2021.9625536","title":"Artificial Neural Networks-based Ambient RF Energy Harvesting with Environment Detection","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta University of the Arts; University of Calgary","funders":"","keywords":"Artificial neural network; Computer science; Radio frequency; Energy (signal processing); Wireless sensor network; Real-time computing; Electronic engineering; Artificial intelligence; Telecommunications; Engineering; Computer network; Mathematics; Statistics","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":[0.0005556287,0.0005280516,0.0003989226,0.0002613849,0.0001890933,0.0004399647,0.00081078,0.0006320468,0.0008121711],"category_scores_gemma":[0.00147109,0.000270958,0.0003862629,0.0002830831,0.0003041815,0.0005664045,0.0004592972,0.0006245596,0.0002551905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003141162,"about_ca_system_score_gemma":0.0002826785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002098233,"about_ca_topic_score_gemma":0.002888006,"domain_scores_codex":[0.9997216,0.00005613948,0.00002089814,0.00007043679,0.0001019293,0.00002903722],"domain_scores_gemma":[0.9994757,0.0002820286,0.00005073899,0.00002962578,0.0001517767,0.00001012472],"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":[0.00009352416,0.00007737979,0.00129763,0.00008497568,0.00007381717,0.00008055423,0.00004941917,0.7909283,0.01184342,0.002384154,0.0007054773,0.1923814],"study_design_scores_gemma":[0.000002502014,0.00002259148,0.0001490428,0.000004819081,0.000006704402,0.00001263367,0.000002892683,0.9973013,0.001766719,0.0004336886,0.000293948,0.000003274228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03318244,0.0004165808,0.9623384,0.0001378071,0.00007461001,0.00002582393,0.00001922213,0.0004975119,0.00330754],"genre_scores_gemma":[0.7887576,0.0004088152,0.2031337,0.0002398643,0.00005777424,0.0001078359,0.00009329799,0.00004612449,0.007155131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002098233,"threshold_uncertainty_score":0.004172087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009712985143622265,"score_gpt":0.1822437931531226,"score_spread":0.1725308080095003,"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."}}