{"id":"W2973236520","doi":"10.1051/epjconf/201921407021","title":"Sim@P1: Using Cloudscheduler for offline processing on the ATLAS HLT farm","year":2019,"lang":"en","type":"article","venue":"EPJ Web of Conferences","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Cloud computing; Computer science; Atlas (anatomy); Provisioning; ATLAS experiment; Resource (disambiguation); Large Hadron Collider; Database; Operating system; Telecommunications; Computer 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":[0.0002978526,0.0001657372,0.0002463156,0.00009312877,0.0001120456,0.0001099339,0.001528379,0.00008119861,0.00002975254],"category_scores_gemma":[0.0002252373,0.0001066956,0.00005659494,0.0002953841,0.0001768309,0.0003376055,0.0003055461,0.000157127,0.00001864939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000021411,"about_ca_system_score_gemma":0.0004803551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001263051,"about_ca_topic_score_gemma":0.00001330371,"domain_scores_codex":[0.998798,0.00002885396,0.0002599881,0.0003684949,0.0002790157,0.0002656856],"domain_scores_gemma":[0.9984835,0.0003692259,0.0002678349,0.000684614,0.0001692139,0.00002560161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000297447,0.00006523899,0.001004277,0.00007641855,0.00002440107,0.000001497338,0.0002069106,0.0006727809,0.01624161,0.879327,0.0001964405,0.1021537],"study_design_scores_gemma":[0.00103038,0.000789113,0.0007117782,0.0004781212,0.00002711188,0.000008896926,0.001030648,0.6600528,0.1552429,0.1249181,0.05500157,0.0007085385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4638952,0.0002632513,0.5282501,0.001763775,0.000398255,0.0005842139,0.00002110833,0.0002889879,0.004535075],"genre_scores_gemma":[0.9306003,0.00001218806,0.06906579,0.0001318059,0.00003902516,0.00001630541,0.000003204284,0.000007952738,0.0001234591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7544088,"threshold_uncertainty_score":0.435092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05249391362687832,"score_gpt":0.2971274705535999,"score_spread":0.2446335569267216,"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."}}