{"id":"W2249198112","doi":"10.1109/cscloud.2015.82","title":"Using ELM Techniques to Predict Data Centre VM Requests","year":2015,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cluster analysis; Extreme learning machine; Feature (linguistics); Resource allocation; Data mining; Data modeling; Machine learning; Database; Artificial neural network","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":[0.00054171,0.00008644548,0.00009083739,0.00006710188,0.0000515074,0.0001466136,0.001473919,0.00003696915,0.00001736189],"category_scores_gemma":[0.0001561213,0.00007039423,0.0000119475,0.000237049,0.000009700353,0.0004763512,0.001214888,0.00008000558,0.0000926315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003193657,"about_ca_system_score_gemma":0.00008362949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005512624,"about_ca_topic_score_gemma":0.00003480734,"domain_scores_codex":[0.998991,0.00006465773,0.000119884,0.0003718339,0.0002360529,0.0002165932],"domain_scores_gemma":[0.9984348,0.00001853475,0.00003018031,0.001244958,0.00006239163,0.0002091758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001178321,0.0001371833,0.01222533,0.00002054713,0.00002178586,0.00006204339,0.001755427,0.0004339952,0.000725499,0.02084368,0.4759514,0.4878113],"study_design_scores_gemma":[0.0001550171,0.0001001521,0.0004478424,0.00005215807,0.00000553125,0.00004012815,0.00003499345,0.4918889,0.00175067,0.001159155,0.5040975,0.0002679344],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003041128,0.00002848481,0.9469885,0.004017665,0.0002154797,0.000116846,0.000003315388,0.0009918286,0.04459678],"genre_scores_gemma":[0.1425473,0.0000016816,0.8532172,0.001244546,0.0001929637,0.000001260784,0.00001051882,0.00001005345,0.002774419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4914549,"threshold_uncertainty_score":0.2870593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1173715454460748,"score_gpt":0.3573875494958737,"score_spread":0.2400160040497989,"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."}}