{"id":"W2085581002","doi":"10.1145/2815675.2815713","title":"Revisiting Network Energy Efficiency of Mobile Apps","year":2015,"lang":"en","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Energy consumption; Computer science; Overhead (engineering); Energy (signal processing); Mobile apps; Efficient energy use; Consumption (sociology); Mobile device; Computer network; Cellular network; World Wide Web; Engineering; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001405353,0.0005839858,0.0004021075,0.001280561,0.000530941,0.002034111,0.0009472485,0.00052818,0.001334663],"category_scores_gemma":[0.01070278,0.0002796969,0.0003204156,0.001256072,0.0005030831,0.003037566,0.0007204943,0.0006629059,0.000360725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008519893,"about_ca_system_score_gemma":0.0005056332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006141557,"about_ca_topic_score_gemma":0.009184911,"domain_scores_codex":[0.9987738,0.0002782782,0.00007427254,0.0002110455,0.0005344842,0.0001281949],"domain_scores_gemma":[0.9934266,0.00347823,0.0005685511,0.0005838813,0.001836013,0.0001067172],"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":[0.0009610773,0.001028651,0.4143847,0.001379252,0.0003645864,0.001619467,0.006129483,0.05972503,0.06735904,0.0138712,0.006052015,0.4271256],"study_design_scores_gemma":[0.0000440018,0.001522477,0.5305213,0.0005424972,0.0004570747,0.0020407,0.008287125,0.3271241,0.07100327,0.01914006,0.03913587,0.0001815957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976751,0.001521732,0.00638679,0.0007357716,0.00004769855,0.00006716145,0.0002382852,0.0001573754,0.01409426],"genre_scores_gemma":[0.9955572,0.0005134942,0.002195397,0.0001040152,0.00001791602,0.00002448687,0.0001432555,0.00005222182,0.001391925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006141557,"threshold_uncertainty_score":0.01221162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008254892066223962,"score_gpt":0.204658762937851,"score_spread":0.1964038708716271,"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."}}