{"id":"W2507131699","doi":"","title":"Using existing instrumentation for transaction generation and performance analysis in distributed systems","year":2004,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; A priori and a posteriori; Database transaction; Context (archaeology); Aggregate (composite); Interdependence; Construct (python library); Data mining; Information system; Distributed computing; Data science; Database","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":[0.0003351077,0.00007035689,0.0001270293,0.0001545599,0.0001311793,0.000103161,0.00007141613,0.00004554785,4.399696e-7],"category_scores_gemma":[0.000008325826,0.00006088055,0.00003138975,0.0006490584,0.00001142389,0.0007751272,0.000008656702,0.00003389826,4.171655e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001671745,"about_ca_system_score_gemma":0.00003319199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003887594,"about_ca_topic_score_gemma":0.0001018583,"domain_scores_codex":[0.9992788,0.00002042271,0.0002539942,0.000220375,0.0001040424,0.0001223968],"domain_scores_gemma":[0.9997034,0.00001713356,0.00006844346,0.0001289585,0.00005663861,0.00002539941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000119738,0.00003901556,0.1333922,0.0001702098,0.00004917448,3.676679e-7,0.0007845904,0.8477617,0.002067432,0.001223745,9.701018e-7,0.01449864],"study_design_scores_gemma":[0.0004468242,0.00004145894,0.03552442,0.00001921552,0.00002449681,0.000004511163,0.00006660817,0.9620916,0.001659345,0.00003551738,0.000005487787,0.00008055235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4956214,0.00001452932,0.5040628,0.00001824794,0.0001011065,0.0001450136,0.000001437569,0.00002883628,0.000006699556],"genre_scores_gemma":[0.9713176,0.000008707643,0.02857773,0.000008858658,0.00003155431,0.00002711989,0.00002197723,0.000002313927,0.000004065952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4756963,"threshold_uncertainty_score":0.2482637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05871308859681092,"score_gpt":0.2956587917984327,"score_spread":0.2369457032016218,"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."}}