{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001861548,0.0002749192,0.0003243805,0.0001186002,0.0002377495,0.00008779017,0.0001514361,0.0001626999,0.000195779],"category_scores_gemma":[0.000009286222,0.0002864068,0.0000645812,0.000385099,0.00009340966,0.0001650727,0.0002109361,0.0002159225,0.000006945632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001511738,"about_ca_system_score_gemma":0.00007591296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001100988,"about_ca_topic_score_gemma":0.00003427718,"domain_scores_codex":[0.9986103,0.00002969125,0.0003862451,0.000375902,0.0001085989,0.0004892623],"domain_scores_gemma":[0.9993109,0.0001019574,0.0000276743,0.0003404919,0.0001237978,0.00009515724],"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":[0.00002973268,0.00005652234,0.01709376,0.001982759,0.00009087985,0.000003079473,0.000942522,0.009397235,0.0003731593,0.002187483,0.0005174616,0.9673254],"study_design_scores_gemma":[0.0005149258,0.0001141237,0.01423667,0.0001759298,0.00003475178,0.000001444985,0.001562965,0.9650202,0.01230903,0.0003524111,0.0053015,0.000375975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7931956,0.001442976,0.1710264,0.00004428272,0.000617254,0.0001975927,0.000001196154,0.0001795014,0.0332952],"genre_scores_gemma":[0.9914854,0.0003370635,0.0003103334,0.00007723709,0.00005946554,0.00001717985,0.000002727846,0.00001720948,0.007693376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9669494,"threshold_uncertainty_score":0.9999588,"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."}}