{"id":"W4315882058","doi":"10.1007/s41781-023-00101-0","title":"Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing","year":2023,"lang":"en","type":"article","venue":"Computing and Software for Big Science","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; High Energy Physics; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Computer science; Cloud computing; Inference; Data processing; Central processing unit; Grid; Event (particle physics); Speedup; Process (computing); Parallel computing; Real-time computing; Operating system; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002746239,0.000889581,0.0009457844,0.0007381855,0.0009497844,0.001702525,0.002952291,0.0008616561,0.003269393],"category_scores_gemma":[0.009008706,0.0005350584,0.0007258574,0.002899935,0.001056993,0.002439162,0.001015608,0.002154732,0.001319786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001593339,"about_ca_system_score_gemma":0.002266464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02516934,"about_ca_topic_score_gemma":0.02715416,"domain_scores_codex":[0.9984136,0.0003361557,0.00007887156,0.0003985688,0.0004472415,0.0003255365],"domain_scores_gemma":[0.9952955,0.001975256,0.0001590198,0.001209147,0.001031632,0.000329487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00478605,0.001750058,0.07117554,0.0006644285,0.0006706011,0.000824638,0.001091907,0.5442498,0.03869944,0.02201091,0.084711,0.2293657],"study_design_scores_gemma":[0.0002160136,0.00021268,0.005912623,0.00001845668,0.00003113034,0.00007624362,0.0001854202,0.9677307,0.01398595,0.004399811,0.007198085,0.0000330502],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"software","genre_scores_codex":[0.8379654,0.001818121,0.1129825,0.001932021,0.0009558686,0.0001854121,0.002680429,0.02459303,0.0168872],"genre_scores_gemma":[0.8423612,0.0002871593,0.1498445,0.000488959,0.00007216662,0.00009019064,0.003670089,0.001084814,0.002100979],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02516934,"threshold_uncertainty_score":0.05004567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.063915036091877,"score_gpt":0.3422667227260621,"score_spread":0.2783516866341851,"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."}}