{"id":"W2729475721","doi":"10.1155/2017/5274715","title":"An Optimized WSN Design for Latency-Critical Smart Grid Applications","year":2017,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Regina","funders":"","keywords":"Latency (audio); Computer science; Smart grid; Wireless sensor network; Quality of service; Computer network; Reliability (semiconductor); Internet of Things; Power consumption; Distributed computing; Low latency (capital markets); Embedded system; Power (physics); Engineering; Telecommunications","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":[0.0002580367,0.0004993189,0.0003286789,0.0002069528,0.0002225975,0.0004751373,0.0005523802,0.0003151766,0.001424144],"category_scores_gemma":[0.0005928538,0.0002260302,0.0002440117,0.0002748983,0.0001585491,0.0004858495,0.0002658044,0.0003115622,0.0002877673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005333056,"about_ca_system_score_gemma":0.0006296023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001209761,"about_ca_topic_score_gemma":0.00250827,"domain_scores_codex":[0.9998596,0.00003797022,0.000005909545,0.00002956385,0.00004863121,0.00001829105],"domain_scores_gemma":[0.9998228,0.00005231494,0.0000391992,0.00001146146,0.00006329673,0.00001097585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006946984,0.00004061267,0.0004633565,0.0001082283,0.00002171036,0.00007128058,0.0000359682,0.9440713,0.02624588,0.004792354,0.0009958886,0.02308392],"study_design_scores_gemma":[0.000009827526,0.00009425122,0.0001869898,0.000007362134,0.00001268344,0.00002914408,0.00001682667,0.9936922,0.003196453,0.001330651,0.001418084,0.000005583361],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07291315,0.0006885381,0.9148888,0.0003641062,0.00005673185,0.0001387344,0.0001231828,0.000335491,0.0104913],"genre_scores_gemma":[0.8475841,0.0006460817,0.1467243,0.00009221659,0.00003134969,0.0001572773,0.00008614505,0.0001160698,0.004562646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001424144,"threshold_uncertainty_score":0.004764199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03024443049663119,"score_gpt":0.2890138366978765,"score_spread":0.2587694062012453,"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."}}