{"id":"W7091059776","doi":"10.5281/zenodo.17339127","title":"Artifact for : Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Cloud computing; Artifact (error); Inference; Power (physics); Component (thermodynamics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003795738,0.001549352,0.0008257945,0.0009432193,0.0007933945,0.004008041,0.002240502,0.00193843,0.06118257],"category_scores_gemma":[0.02168086,0.0008157634,0.001270173,0.001276557,0.0008497463,0.002038954,0.00291226,0.002390155,0.039008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009331364,"about_ca_system_score_gemma":0.002245292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01082659,"about_ca_topic_score_gemma":0.01264442,"domain_scores_codex":[0.9964024,0.0008380163,0.0001975812,0.0005358933,0.001817819,0.0002084216],"domain_scores_gemma":[0.9903235,0.002835816,0.0003360768,0.004733681,0.001522329,0.0002485687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005612948,0.0001562142,0.003266307,0.0005721168,0.000140638,0.0004022251,0.0002290154,0.03272137,0.005853424,0.03619112,0.784149,0.1357573],"study_design_scores_gemma":[0.0002426765,0.00008387274,0.002734591,0.0002020613,0.000041433,0.0004055038,0.00008112451,0.2441042,0.02353849,0.05086361,0.6775829,0.0001196103],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.006337744,0.0003631269,0.6935643,0.003622625,0.00378562,0.0003714352,0.06738014,0.1813397,0.04323532],"genre_scores_gemma":[0.1811669,0.0006802552,0.5234238,0.002863113,0.001027986,0.0009717628,0.1216221,0.08750233,0.08074179],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06118257,"threshold_uncertainty_score":0.2046761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02983478382441825,"score_gpt":0.2774757010486628,"score_spread":0.2476409172242446,"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."}}