{"id":"W1553924878","doi":"10.1002/spe.2282","title":"Improving J9 virtual machine with LTTng for efficient and effective tracing","year":2014,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Atlantic Hydrogen (Canada); University of New Brunswick","funders":"Atlantic Canada Opportunities Agency; University of New Brunswick; International Business Machines Corporation","keywords":"Tracing; Computer science; Throughput; Kernel (algebra); Overhead (engineering); Virtual machine; Component (thermodynamics); Parallel computing; Operating system","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":[0.0008878296,0.0002029986,0.0002297855,0.00005999226,0.0004953315,0.0002471035,0.0002452801,0.00007270147,0.000001021556],"category_scores_gemma":[0.001693295,0.000146447,0.00003114733,0.0002001992,0.0001533004,0.001282338,0.0001725397,0.0001627916,0.000002970082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003266861,"about_ca_system_score_gemma":0.00004191796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001228768,"about_ca_topic_score_gemma":0.000006663047,"domain_scores_codex":[0.9984551,0.0001084118,0.0002106725,0.000642857,0.0002458076,0.0003371678],"domain_scores_gemma":[0.9972484,0.001878371,0.0001742684,0.0003957273,0.0001638799,0.0001394268],"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":[0.0003577553,0.0001587709,0.01581314,0.0003569255,0.0000358335,0.000009687869,0.03936562,0.0005674445,0.0007178469,0.002764225,0.00001842923,0.9398343],"study_design_scores_gemma":[0.01032032,0.01209146,0.03812031,0.001092511,0.0002690441,0.002315712,0.02384214,0.8601063,0.02467323,0.0009578007,0.02242918,0.003782027],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3611481,0.0002528697,0.6377063,0.0001663872,0.0001616667,0.0003878724,0.000001285555,0.0001447007,0.00003079063],"genre_scores_gemma":[0.9082777,0.00001681598,0.09106713,0.0003348401,0.00006391684,0.0002111735,9.626609e-7,0.00001172685,0.00001573725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9360523,"threshold_uncertainty_score":0.5971937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004570723289212413,"score_gpt":0.2422307863362506,"score_spread":0.2376600630470382,"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."}}