{"id":"W1537941984","doi":"10.1109/mobserv.2015.22","title":"Share Sens: An Approach to Optimizing Energy Consumption of Continuous Mobile Sensing Workloads","year":2015,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Merge (version control); Android (operating system); Power consumption; Energy consumption; Mobile device; Embedded system; Wearable computer; Real-time computing; Power (physics); Electrical engineering; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001805966,0.001760373,0.0009207921,0.0009098012,0.001059741,0.001710919,0.002691435,0.000635125,0.002581159],"category_scores_gemma":[0.004806371,0.0007505639,0.0005607752,0.00119639,0.0008098952,0.001978461,0.001928175,0.0007202561,0.0005019048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331673,"about_ca_system_score_gemma":0.002557127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004921397,"about_ca_topic_score_gemma":0.01112553,"domain_scores_codex":[0.9986249,0.0003034878,0.00008466371,0.0003127458,0.0004517709,0.0002224349],"domain_scores_gemma":[0.9982704,0.0007245417,0.0001601533,0.0004391744,0.0002649911,0.000140637],"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":[0.001815217,0.001077018,0.01134974,0.0004108434,0.0002784981,0.0002545941,0.0009325643,0.4323045,0.07812632,0.012391,0.01321522,0.4478445],"study_design_scores_gemma":[0.00009683591,0.0003307447,0.001627622,0.00001243998,0.00006465777,0.00008571892,0.0002858499,0.9678054,0.01818213,0.005574563,0.00589482,0.00003922926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2627598,0.001033874,0.7081155,0.0008002577,0.0002473343,0.0008456152,0.0004985135,0.01049231,0.01520682],"genre_scores_gemma":[0.8477418,0.0001609412,0.1468361,0.0001748566,0.00007565411,0.0002856549,0.0002564535,0.0006433875,0.003825049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004921397,"threshold_uncertainty_score":0.009785533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04065829011448823,"score_gpt":0.2562034478166034,"score_spread":0.2155451577021152,"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."}}