{"id":"W2767288501","doi":"10.1155/2017/6562915","title":"Profiling Energy Efficiency and Data Communications for Mobile Internet of Things","year":2017,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Green IT and Sustainability","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; Uppsala Universitet; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China; 3D Digital Media Technology Engineering Laboratory; VINNOVA; Swedish Foundation for International Cooperation in Research and Higher Education","keywords":"Computer science; Profiling (computer programming); Cloud computing; Energy consumption; Workflow; Embedded system; Mobile device; Distributed computing; Real-time computing; Computer network; Database; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005190016,0.0001227115,0.0002278497,0.00005252389,0.0007407577,0.0001218203,0.002513362,0.00006255523,8.168488e-7],"category_scores_gemma":[0.00005811877,0.000127706,0.00002963928,0.0000505531,0.0004964154,0.000265031,0.003229343,0.0001532039,1.490344e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002097568,"about_ca_system_score_gemma":0.00002435536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002657508,"about_ca_topic_score_gemma":0.00006081214,"domain_scores_codex":[0.9991822,0.00004742206,0.000332972,0.0002041498,0.00005871406,0.0001745895],"domain_scores_gemma":[0.9944625,0.0004072509,0.0001447739,0.004800873,0.0001321235,0.0000525156],"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":[0.000009855875,0.0002534388,0.01737128,0.0007010496,0.0001044532,2.939245e-7,0.00540501,0.0008379096,0.001982631,0.03860738,0.0001235715,0.9346032],"study_design_scores_gemma":[0.0002350281,0.00004287687,0.0005347903,0.00008187201,0.00002129968,0.000002786939,0.0009287351,0.9895033,0.000294036,0.0002805559,0.007939124,0.0001355698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8377776,0.01039471,0.1489502,0.0001348458,0.00009793075,0.000822645,0.00005576706,0.0001716494,0.001594631],"genre_scores_gemma":[0.9768115,0.001405344,0.02149916,0.000009348092,0.00001252063,0.0001080926,0.0001110767,0.00001962253,0.00002329757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9886654,"threshold_uncertainty_score":0.5697384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03531952804804267,"score_gpt":0.3088011257870792,"score_spread":0.2734815977390366,"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."}}