{"id":"W3215995821","doi":"10.1109/icsme52107.2021.00035","title":"Energy Efficient Guidelines for iOS Core Location Framework","year":2021,"lang":"en","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Energy consumption; Sample (material); Core (optical fiber); Documentation; Energy (signal processing); Service (business); Set (abstract data type); Location-based service; Database; Operating system; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00006951587,0.00006426706,0.00007635493,0.00001695781,0.00003054765,0.00001453461,0.00004499473,0.00006399616,0.00008884809],"category_scores_gemma":[0.0003209651,0.00005913739,0.00003929576,0.0001470371,0.000007878402,0.00001462652,0.00001323346,0.00003329815,0.000004473779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004537935,"about_ca_system_score_gemma":0.00003140833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001628616,"about_ca_topic_score_gemma":0.00003882992,"domain_scores_codex":[0.9995525,0.00000386317,0.0001457399,0.0001038025,0.00006452377,0.0001294979],"domain_scores_gemma":[0.9990972,0.00006372306,0.00000667954,0.0001852001,0.0006134522,0.00003380552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008226724,0.0000530121,0.0008774946,0.0002988682,0.00003447431,0.000005032738,0.0002297402,0.7722645,0.0007626668,0.1267304,0.04036204,0.05837355],"study_design_scores_gemma":[0.0001127017,0.000009941123,0.0004726111,0.00001725798,0.000007929443,0.000001623935,0.0004337076,0.9341748,0.006420192,0.0106133,0.04761108,0.0001248118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.062856,0.000473096,0.934121,0.0005400462,0.0003300107,0.00006943512,0.000002038443,0.0001791299,0.001429239],"genre_scores_gemma":[0.9779237,0.000008775856,0.02055292,0.0002464646,0.0001386165,0.00003236937,0.00001674073,0.0000128943,0.001067544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9150677,"threshold_uncertainty_score":0.2411553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946219975230235,"score_gpt":0.2959358604512886,"score_spread":0.2564736606989862,"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."}}