{"id":"W4415657680","doi":"10.1038/s41597-026-07496-6","title":"Characterization of high-resolution AI data center training workloads on single and multiple GPU nodes","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Independent Electricity System Operator","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Session (web analytics); Resilience (materials science); Grid; Data center; Set (abstract data type); Energy consumption; Data set; Training set; Training (meteorology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001037031,0.0001040186,0.000135593,0.0002236514,0.0002808836,0.0005478073,0.002835094,0.00003301161,0.000003289951],"category_scores_gemma":[0.0001375957,0.00009416888,0.00001327959,0.0006514298,0.0001427563,0.0002155101,0.00477751,0.00008505554,0.000008421562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001914103,"about_ca_system_score_gemma":0.00003776707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002739512,"about_ca_topic_score_gemma":0.00001831243,"domain_scores_codex":[0.998208,0.00006890068,0.0002443438,0.0009671727,0.0002891411,0.0002224585],"domain_scores_gemma":[0.9965019,0.00007108422,0.0001086424,0.003229524,0.00004757919,0.00004123095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000394933,0.0008108129,0.001577533,0.0001776737,0.00009370812,0.000007606586,0.001770303,0.002102999,0.05979146,0.009698445,0.03481582,0.8891141],"study_design_scores_gemma":[0.0005264069,0.00002762119,0.0084433,0.0003523222,0.00001491075,0.000001428284,0.00006236272,0.9298678,0.001366731,0.000349622,0.05883305,0.0001544562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5104805,0.00007655373,0.4808622,0.003105893,0.003206268,0.0002739757,0.00120649,0.0001658073,0.0006222621],"genre_scores_gemma":[0.9912755,0.000002158187,0.005293538,0.000214935,0.00004950375,9.256962e-7,0.002061936,0.000004730619,0.001096794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9277648,"threshold_uncertainty_score":0.5954825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06182922118262564,"score_gpt":0.2691192533714195,"score_spread":0.2072900321887939,"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."}}