{"id":"W7039546823","doi":"","title":"Minimizing Energy Consumption in Data Centers Using&#13;\\nEmbedded Sensors and Machine Learning","year":2023,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Archaeology and ancient environmental studies","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs; Concordia University","keywords":"Cloud computing; Virtual machine; Energy consumption; Scalability; Software; Power consumption; Energy (signal processing); Data center","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003723804,0.0008197419,0.0006948704,0.0003210889,0.0004605135,0.001242304,0.001201992,0.000618375,0.0009130003],"category_scores_gemma":[0.001057093,0.0003458414,0.0004268528,0.000665338,0.0005067412,0.00150764,0.0008210897,0.0006336811,0.0002611564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009548136,"about_ca_system_score_gemma":0.001230074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003611719,"about_ca_topic_score_gemma":0.005307923,"domain_scores_codex":[0.9995837,0.00009376436,0.00001700072,0.000145139,0.00008369095,0.00007673916],"domain_scores_gemma":[0.9996382,0.0001431043,0.0000617029,0.00006692793,0.00005954783,0.00003049762],"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":[0.00008095178,0.0001281753,0.001695655,0.00007781761,0.00003858273,0.00004211061,0.00003989364,0.9138184,0.008075112,0.006891484,0.001071179,0.06804062],"study_design_scores_gemma":[0.000002063062,0.00002836324,0.0002324153,0.000003364242,0.000005266173,0.00000802621,0.00001386624,0.995479,0.001919829,0.001897096,0.0004072372,0.00000337421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08628707,0.0007543028,0.9077227,0.0004780826,0.00007596032,0.00007563373,0.00008978473,0.0007006638,0.003815695],"genre_scores_gemma":[0.9275271,0.0004504878,0.06992871,0.000126062,0.00005298696,0.00005199363,0.00008673001,0.00006205116,0.001713849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003611719,"threshold_uncertainty_score":0.007181406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06163101129464974,"score_gpt":0.2711757100973658,"score_spread":0.2095446988027161,"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."}}