{"id":"W3101813057","doi":"","title":"Wireless information and power transfer for IoT applications in overlay cognitive radio networks","year":2019,"lang":"en","type":"article","venue":"UTS ePRESS (University of Technology Sydney)","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Cognitive radio; Computer network; Relay; Nakagami distribution; Overlay; Energy harvesting; Information transfer; Wireless; Throughput; Maximum power transfer theorem; Channel (broadcasting); Fading; Energy (signal processing); Telecommunications; Power (physics)","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":[0.000398636,0.0003075812,0.0002334315,0.000275255,0.0003201307,0.0008391917,0.0004634055,0.0007055881,0.001230463],"category_scores_gemma":[0.0007192756,0.0001468222,0.0002129682,0.0004131686,0.000417714,0.0009018222,0.0006282158,0.0004339199,0.000315161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004677688,"about_ca_system_score_gemma":0.0003748215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008834401,"about_ca_topic_score_gemma":0.0012146,"domain_scores_codex":[0.9997543,0.00007186249,0.000009711629,0.00003579415,0.0001003009,0.00002808109],"domain_scores_gemma":[0.9998028,0.00009289928,0.00001930254,0.00002697664,0.00004375614,0.00001427092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002157234,0.0002225603,0.003268189,0.0003630428,0.0001012793,0.001770844,0.0004488765,0.2497024,0.0665686,0.1775148,0.01524968,0.484574],"study_design_scores_gemma":[0.000007668757,0.000151149,0.001103644,0.00003539858,0.00003341497,0.0005184651,0.0001821133,0.913164,0.006457889,0.05217726,0.02614668,0.00002232224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06305522,0.007514076,0.8892842,0.0007396784,0.0003047308,0.00006500112,0.0000521378,0.000360869,0.038624],"genre_scores_gemma":[0.9302921,0.003699546,0.05752722,0.0001544752,0.0001301045,0.00005167376,0.0000503796,0.00002226377,0.008072405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001230463,"threshold_uncertainty_score":0.004116237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003104798443025857,"score_gpt":0.1648618913181743,"score_spread":0.1617570928751484,"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."}}