{"id":"W4353072056","doi":"10.3390/en16062900","title":"Optimized Hierarchical Tree Deep Convolutional Neural Network of a Tree-Based Workload Prediction Scheme for Enhancing Power Efficiency in Cloud Computing","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Workload; Cloud computing; Benchmark (surveying); Scalability; Convolutional neural network; Data mining; Artificial neural network; Redundancy (engineering); Data redundancy; Tree (set theory); Artificial intelligence; Database; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000931602,0.0001964708,0.0003351597,0.0002361017,0.0002716436,0.00007650758,0.0005419336,0.0001035474,0.000001591931],"category_scores_gemma":[0.0001916863,0.000195848,0.0001640466,0.001336073,0.00009923158,0.0001687966,0.0002784378,0.0002103667,0.000003980711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007503128,"about_ca_system_score_gemma":0.000147617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001369572,"about_ca_topic_score_gemma":0.00001058784,"domain_scores_codex":[0.9978588,0.0001273604,0.0005704262,0.0004540981,0.0003306844,0.0006586778],"domain_scores_gemma":[0.9983241,0.001051706,0.0001666998,0.0002802196,0.0001052679,0.00007201262],"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":[0.00008091758,0.00005649835,0.002036022,0.00002678871,0.00001346866,0.000006296955,0.0006546517,0.9827054,0.0009868068,0.002237263,0.001063776,0.01013211],"study_design_scores_gemma":[0.001388677,0.0001278053,0.008826159,0.0001628349,0.000004604854,0.000002235398,0.0000346336,0.9873325,0.0009763804,0.0007810611,0.0001849798,0.0001780929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.452788,0.000202108,0.5417386,0.0002266914,0.004384201,0.0001607292,5.525114e-7,0.0002958993,0.000203178],"genre_scores_gemma":[0.9027647,0.000003098809,0.09549799,0.00008094432,0.001562604,0.00001956058,0.00001563944,0.00001621486,0.00003929471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4499766,"threshold_uncertainty_score":0.798645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398449962483513,"score_gpt":0.2426568693450467,"score_spread":0.2286723697202116,"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."}}