{"id":"W3014682316","doi":"10.1109/tgcn.2020.3035512","title":"Analysis of Asymmetric Dual-Hop Energy Harvesting-Based Wireless Communication Systems in Mixed Fading Environments","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Green Communications and Networking","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Khalifa University of Science, Technology and Research","keywords":"Fading; Relay; Nakagami distribution; Hop (telecommunications); Computer science; Energy harvesting; Wireless; Energy (signal processing); Node (physics); Topology (electrical circuits); Transmission (telecommunications); Monte Carlo method; Bit error rate; Telecommunications; Electronic engineering; Mathematics; Power (physics); Channel (broadcasting); Statistics; Physics; 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":[0.0009633229,0.0007632512,0.0008414785,0.0004962582,0.0004007343,0.001052637,0.0007536878,0.0008603499,0.001208084],"category_scores_gemma":[0.003393692,0.0003211299,0.0004172114,0.0005653557,0.001181429,0.0009889845,0.001045464,0.0004540564,0.0001910763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008618244,"about_ca_system_score_gemma":0.0005569333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002022218,"about_ca_topic_score_gemma":0.001200761,"domain_scores_codex":[0.9994404,0.0001855998,0.00001883266,0.00007151129,0.0001670354,0.0001166463],"domain_scores_gemma":[0.9978339,0.001430697,0.0002725155,0.00008900693,0.0003083609,0.00006560469],"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":[0.00006368535,0.00001793413,0.000695082,0.00004488715,0.00003479978,0.0001916196,0.00005706231,0.9810749,0.003931768,0.01049873,0.0001915791,0.003198014],"study_design_scores_gemma":[0.000002425191,0.00001606555,0.0001725729,0.000002432849,0.000005482552,0.00002631763,0.00001352165,0.9982482,0.0002567764,0.001206922,0.00004549447,0.000003814851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4210277,0.001143905,0.5625862,0.0005045441,0.00004660015,0.00007808255,0.0001538431,0.0001970032,0.01426213],"genre_scores_gemma":[0.9938047,0.0002991306,0.003893979,0.00003401866,0.0000151176,0.00002835716,0.00003087489,0.00001596524,0.001877847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002022218,"threshold_uncertainty_score":0.006253004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03308566567073225,"score_gpt":0.2358102271838471,"score_spread":0.2027245615131148,"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."}}