{"id":"W2167505895","doi":"10.1109/access.2015.2497312","title":"Green Internet of Things for Smart World","year":2015,"lang":"en","type":"article","venue":"IEEE Access","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":491,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Department of Education of Guangdong Province; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; National Natural Science Foundation of China","keywords":"Cloud computing; Internet of Things; Computer science; Green computing; Wireless sensor network; Telecommunications; Big data; Smart city; Computer security; Efficient energy use; Multimedia; Engineering; Computer network; Electrical engineering","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":[0.0003661207,0.00008259356,0.0001395354,0.0001164071,0.00002731423,0.0001496314,0.001555922,0.00002761059,9.664398e-7],"category_scores_gemma":[0.00004361416,0.00007378046,0.00005216399,0.000293686,0.00002269951,0.0008993263,0.0003420912,0.00006667301,0.00001549024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002247369,"about_ca_system_score_gemma":0.00005197092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005265307,"about_ca_topic_score_gemma":0.00001613389,"domain_scores_codex":[0.9992224,0.00001861068,0.0001897251,0.0002060405,0.0001612572,0.0002019889],"domain_scores_gemma":[0.9992239,0.00008994203,0.0001050156,0.0002900275,0.0002142862,0.00007679417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003562732,0.0001183446,0.0204978,0.0001438455,0.00006042081,0.00001118191,0.008119896,0.00005888915,0.0003102181,0.004280235,0.6988853,0.2674783],"study_design_scores_gemma":[0.002174056,0.0003411871,0.004505805,0.0002518609,0.00002897956,0.00001554343,0.0000233209,0.5326348,0.08075722,0.0620441,0.3163908,0.0008323476],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07393848,0.00005549121,0.8999177,0.0005620065,0.01753765,0.0001712925,1.530473e-7,0.0001279999,0.00768924],"genre_scores_gemma":[0.9685472,5.27443e-7,0.02350346,0.001177117,0.002163158,0.00001419189,0.000001827029,0.00001603159,0.004576528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8946087,"threshold_uncertainty_score":0.300868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09107935521907977,"score_gpt":0.3248495156447168,"score_spread":0.233770160425637,"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."}}