{"id":"W4288630321","doi":"10.48550/arxiv.1901.05999","title":"Robust Design of AC Computing-Enabled Receiver Architecture for SWIPT\\n Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Energy harvesting; Maximum power transfer theorem; Wireless; Computation; Context (archaeology); Block (permutation group theory); Energy (signal processing); Efficient energy use; Power (physics); Channel (broadcasting); Decoding methods; Electronic engineering; Electrical engineering; Computer network; Telecommunications; Algorithm; Engineering; Mathematics","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":[0.0003272219,0.0006408063,0.0004508561,0.0002201867,0.0003707056,0.0009196873,0.0009905862,0.0006734539,0.001819405],"category_scores_gemma":[0.0005748522,0.0002801859,0.0003123822,0.0002649172,0.0004720287,0.0005188838,0.0005803729,0.0005888792,0.0005261191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006884893,"about_ca_system_score_gemma":0.0007682805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001258256,"about_ca_topic_score_gemma":0.002098805,"domain_scores_codex":[0.9997193,0.00005769174,0.00001186672,0.00007805715,0.00009273602,0.00004023798],"domain_scores_gemma":[0.9997774,0.00005106007,0.00005423339,0.00002094049,0.00008284887,0.00001358085],"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":[0.0002047012,0.00009111635,0.0007544722,0.0002850674,0.00008691523,0.0002174924,0.0001501802,0.753517,0.1003238,0.05692105,0.002772163,0.084676],"study_design_scores_gemma":[0.00001104087,0.00009268134,0.00008628526,0.00000889229,0.00001616161,0.00004728475,0.00001188926,0.9884848,0.006496144,0.002720823,0.002015971,0.000008007736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01293578,0.0003156967,0.9787003,0.0001637144,0.00004832743,0.00004076119,0.00003528044,0.0002252379,0.007534909],"genre_scores_gemma":[0.843702,0.0004771116,0.1505932,0.0001067643,0.00005606668,0.0001401677,0.00005559568,0.00003784873,0.004831264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001819405,"threshold_uncertainty_score":0.006086528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06354914728300486,"score_gpt":0.166101984956339,"score_spread":0.1025528376733342,"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."}}