{"id":"W2946036490","doi":"10.23919/ifipnetworking46909.2019.8999456","title":"LEMoNet: low energy wireless sensor network design for data center monitoring","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Wireless sensor network; Computer science; Data center; Default gateway; Battery (electricity); Energy consumption; Real-time computing; Reliability (semiconductor); Wireless; Efficient energy use; Computer network; Embedded system; Power (physics); Electrical engineering; 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":[0.0002521928,0.0003898896,0.000170393,0.0002149824,0.0001613535,0.0002923336,0.0009312721,0.0003147022,0.002344822],"category_scores_gemma":[0.0006113094,0.0001584541,0.0001869632,0.0001396114,0.0001543051,0.0005189318,0.0003828961,0.0002961321,0.0005599895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000377867,"about_ca_system_score_gemma":0.0002772263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004251125,"about_ca_topic_score_gemma":0.001144184,"domain_scores_codex":[0.9998061,0.00005199817,0.00001042935,0.00003907895,0.00007269332,0.00001965858],"domain_scores_gemma":[0.9997905,0.0000410389,0.00004284694,0.00002386181,0.00008035808,0.00002142407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005112728,0.0002353806,0.003932432,0.0007532816,0.0001138185,0.0006686179,0.0002004978,0.2410669,0.4481094,0.01594729,0.02392271,0.2645383],"study_design_scores_gemma":[0.00008171206,0.000966221,0.0026748,0.00005484549,0.00004825888,0.0006576424,0.00007030182,0.8487074,0.09607203,0.003860723,0.04675216,0.00005378335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05454778,0.0006788733,0.9269711,0.0004986477,0.0001706107,0.000297709,0.000348544,0.00246406,0.01402268],"genre_scores_gemma":[0.6774203,0.0005740933,0.3074848,0.0004276077,0.00004926254,0.0005263845,0.0004938228,0.000231377,0.01279242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002344822,"threshold_uncertainty_score":0.00784421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03053931018778612,"score_gpt":0.2312604988736526,"score_spread":0.2007211886858665,"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."}}