{"id":"W4205790976","doi":"10.3390/s22020660","title":"ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Distributed computing; Bottleneck; Cloud computing; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Edge computing; Orchestration; Energy consumption; Resource allocation; Workload; Task (project management); Computer network; Embedded system; Engineering","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":[0.0003234584,0.0001166757,0.00009547431,0.00005035206,0.001086665,0.00008357326,0.0008253815,0.00002806525,0.00001678545],"category_scores_gemma":[0.00001891874,0.00009475336,0.00006909012,0.0005495095,0.00003981516,0.00006081058,0.001151304,0.0001614663,0.00003525773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001804109,"about_ca_system_score_gemma":0.00004229595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007293213,"about_ca_topic_score_gemma":0.000006453736,"domain_scores_codex":[0.9986165,0.0002315692,0.0001683581,0.0003309991,0.0003428808,0.0003096978],"domain_scores_gemma":[0.9990554,0.0001449724,0.00008801042,0.0006124031,0.0000409971,0.00005818079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003213601,0.0001186414,0.0007995073,0.00001348742,0.00005439376,0.00008158073,0.005073264,0.04051728,0.001241404,0.01047311,0.7869129,0.1546823],"study_design_scores_gemma":[0.0001223752,0.00002911044,0.000495824,0.000002747135,0.000004188376,0.00004129043,0.0001013121,0.2070127,0.00160002,0.0002789501,0.7901678,0.000143632],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7414593,0.0007476239,0.2061619,0.02493071,0.01510045,0.000336684,0.00001565312,0.001083899,0.01016388],"genre_scores_gemma":[0.9911026,0.000003326304,0.0002720594,0.0011679,0.001082923,0.00001991626,0.000103132,0.00001698801,0.006231107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2496434,"threshold_uncertainty_score":0.8357856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01112837332107949,"score_gpt":0.2083552798251578,"score_spread":0.1972269065040783,"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."}}