{"id":"W4226397034","doi":"10.1080/00207217.2022.2062797","title":"A triple band rectenna for RF energy harvesting in smart city applications","year":2022,"lang":"en","type":"article","venue":"International Journal of Electronics","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Ministry of Education, India","keywords":"Rectenna; Rectifier (neural networks); Electrical engineering; Energy harvesting; Antenna (radio); Energy conversion efficiency; Radio frequency; Voltage; Diode; Power (physics); Frequency band; Engineering; Electronic engineering; Physics; Computer science; Rectification","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":[0.0004730105,0.0001020026,0.0001582897,0.0002409577,0.00007362228,0.00004220308,0.000482666,0.00003881184,0.00003054514],"category_scores_gemma":[0.00009486349,0.0001190535,0.00009421654,0.0002319832,0.00001355873,0.0001417145,0.00003901135,0.0004219819,2.378849e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006513625,"about_ca_system_score_gemma":0.0001326792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003298807,"about_ca_topic_score_gemma":0.0002555096,"domain_scores_codex":[0.9988552,0.00003358482,0.0004592671,0.0001031271,0.0003080041,0.0002408216],"domain_scores_gemma":[0.9992501,0.00023439,0.0001831648,0.00008922966,0.0001949307,0.00004819298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000108004,0.0001019547,0.001945739,0.0000106836,0.0002101829,0.00002337133,0.00007682189,0.9339824,0.006663108,0.0122276,0.002327831,0.04232235],"study_design_scores_gemma":[0.001854949,0.0002422846,0.0006393465,0.0000622912,0.00002815466,0.000418117,0.00003486576,0.304538,0.004448044,0.008435799,0.678986,0.0003121056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3079061,0.008764897,0.6714785,0.001032502,0.003931827,0.0003782447,0.00006882949,0.0002135917,0.006225514],"genre_scores_gemma":[0.9959998,0.0002504751,0.002542481,0.0001050055,0.0004619049,0.0001257152,0.00002162611,0.00003460126,0.0004584053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6880937,"threshold_uncertainty_score":0.485486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101233410254936,"score_gpt":0.2330555589198184,"score_spread":0.2229322178943248,"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."}}