{"id":"W4408324039","doi":"10.1002/ett.70078","title":"Workload Prediction in Cloud Data Centers Using Complex‐Valued Spatio‐Temporal Graph Convolutional Neural Network Optimized With Gazelle Optimization Algorithm","year":2025,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workload; Convolutional neural network; Computer science; Cloud computing; Graph; Artificial intelligence; Algorithm; Data mining; Theoretical computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005082747,0.0002812141,0.0002976811,0.000887624,0.001088951,0.0001579472,0.002880624,0.0001432311,0.00001690625],"category_scores_gemma":[0.00002482466,0.0002795582,0.00007530781,0.003498067,0.0002779882,0.0001906158,0.0003584219,0.0006249574,0.000002586333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002261771,"about_ca_system_score_gemma":0.00009127314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002354622,"about_ca_topic_score_gemma":0.0001008608,"domain_scores_codex":[0.9978081,0.0002316912,0.0005736072,0.0006547223,0.0002823742,0.0004494801],"domain_scores_gemma":[0.9966472,0.0001769741,0.0002091543,0.002820868,0.0001130974,0.00003264083],"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":[0.00002861847,0.0002345278,0.0002739709,0.00000907324,0.00009017239,0.000001389248,0.00005989882,0.9415077,0.000002375984,0.0009001116,0.0004313618,0.05646081],"study_design_scores_gemma":[0.0009818522,0.00005958783,0.0003534107,0.0002373871,0.00004694653,0.000006995696,0.0004065463,0.9963133,0.0000184347,0.0006066763,0.0007328904,0.0002359241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002105627,0.0004517834,0.9895469,0.00474118,0.0003179802,0.0005634681,0.00002265487,0.001885379,0.0003650062],"genre_scores_gemma":[0.3464469,0.000150356,0.6530631,0.00007757865,0.00001270978,0.00006255206,0.00009637472,0.00001451911,0.00007592118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3443413,"threshold_uncertainty_score":0.9999657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03473038701443979,"score_gpt":0.2731260406583758,"score_spread":0.238395653643936,"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."}}