{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00186829,0.001153466,0.0006433976,0.001075695,0.0004929909,0.00176409,0.003322451,0.0006198162,0.004224888],"category_scores_gemma":[0.008373208,0.0006568913,0.000599497,0.0008325593,0.0007093273,0.002549702,0.001895862,0.001496333,0.001506476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007066874,"about_ca_system_score_gemma":0.001348361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003122974,"about_ca_topic_score_gemma":0.00158449,"domain_scores_codex":[0.9970406,0.0006384823,0.0003117581,0.0004707337,0.001160001,0.00037844],"domain_scores_gemma":[0.9944267,0.001400417,0.0003616534,0.002622936,0.0009387669,0.0002495334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002647822,0.001011256,0.0261091,0.0008900295,0.0002649897,0.001246516,0.001608377,0.08943893,0.2350244,0.0165733,0.0476763,0.577509],"study_design_scores_gemma":[0.0001779178,0.0004229487,0.006450429,0.00007674848,0.0001139805,0.0004414016,0.0001427812,0.7549559,0.1891247,0.005133388,0.04280077,0.000158973],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1744796,0.0006635232,0.6014513,0.0004229952,0.0002983296,0.0002352354,0.000483397,0.2139508,0.008014877],"genre_scores_gemma":[0.7322801,0.0002323511,0.2518821,0.0001910905,0.0000512328,0.0001415521,0.001396844,0.009767002,0.004057689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004224888,"threshold_uncertainty_score":0.01413369,"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."}}