{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004188925,0.0006179979,0.0004178794,0.0006942337,0.0004801785,0.0007202458,0.0003698224,0.0002843569,0.0005097057],"category_scores_gemma":[0.001914383,0.0002091964,0.0002749518,0.0009192094,0.0001737181,0.0008812866,0.0003683606,0.0003259802,0.0002193163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004602664,"about_ca_system_score_gemma":0.0004386893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002015414,"about_ca_topic_score_gemma":0.004654699,"domain_scores_codex":[0.9995292,0.00008756576,0.00002899069,0.00009015545,0.0002108137,0.00005331909],"domain_scores_gemma":[0.9994878,0.0001746,0.00006278119,0.0001066078,0.0001363667,0.00003179976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007768469,0.0005854907,0.0945226,0.0004276886,0.0002061151,0.000622415,0.0004540994,0.229247,0.1164827,0.0159514,0.006522614,0.5342011],"study_design_scores_gemma":[0.00001094483,0.0001333804,0.02280917,0.00002570973,0.00003836544,0.0002262818,0.0001908663,0.931046,0.03305463,0.007070433,0.005364344,0.0000298597],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4443986,0.001363614,0.539553,0.0009130408,0.0001620779,0.000234607,0.0005413148,0.003091836,0.009741837],"genre_scores_gemma":[0.9635782,0.0002009687,0.03504899,0.000065969,0.00001774502,0.00005322337,0.000286196,0.00008345739,0.0006653334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002015414,"threshold_uncertainty_score":0.004007339,"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."}}