{"id":"W7125513531","doi":"10.1109/icft66708.2025.11336635","title":"Energy-Aware and Performance Enhanced Mobile Computing","year":2025,"lang":"","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Mobile computing; Mobile device; Key (lock); Field (mathematics); Mobile telephony","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001615136,0.0003818512,0.0002324223,0.0002876743,0.0003436368,0.0009781809,0.0006374466,0.000471936,0.005537165],"category_scores_gemma":[0.0006193662,0.0001301993,0.0001497413,0.0005524136,0.0002918037,0.001541618,0.0008755002,0.000543516,0.00104753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003340766,"about_ca_system_score_gemma":0.0002876269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007134278,"about_ca_topic_score_gemma":0.001801532,"domain_scores_codex":[0.9998384,0.00002142113,0.000004070298,0.00003171344,0.00005533951,0.00004917667],"domain_scores_gemma":[0.9998203,0.00005617834,0.0000138983,0.00003649864,0.0000562681,0.00001689261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006341109,0.0005909447,0.004345131,0.0004782368,0.00008566687,0.0005532792,0.0003684628,0.173018,0.2337095,0.140295,0.01712687,0.4287949],"study_design_scores_gemma":[0.0000412537,0.0003791533,0.003797685,0.00008673349,0.00008496972,0.000450932,0.0004793669,0.767061,0.08056408,0.0840452,0.06294868,0.00006101531],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3945988,0.005995295,0.4420708,0.003403117,0.0009237005,0.0001256208,0.000444285,0.002183231,0.1502551],"genre_scores_gemma":[0.972741,0.0007479036,0.01623512,0.0001894668,0.00008509281,0.00001604301,0.00009825191,0.00008734007,0.009799775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005537165,"threshold_uncertainty_score":0.01852363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002821417233643481,"score_gpt":0.2034666280746386,"score_spread":0.2006452108409951,"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."}}