{"id":"W2899501705","doi":"10.1145/3236024.3236081","title":"Performance comprehension at WiredTiger","year":2018,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Debugging; Computer science; Software bug; Program comprehension; Software engineering; Algorithmic program debugging; Task (project management); Process (computing); Software; Focus (optics); Field (mathematics); Comprehension; Programming language; Human–computer interaction; Software system; Engineering; Systems engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001942012,0.00009818836,0.0001096681,0.00004341799,0.0002779264,0.00003693773,0.000497519,0.00005399835,0.0001896956],"category_scores_gemma":[0.000007500596,0.00006762041,0.0000396249,0.0002416887,0.0001042864,0.0004530362,0.0003729415,0.00004739505,0.003310326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005407077,"about_ca_system_score_gemma":0.00002584531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001367895,"about_ca_topic_score_gemma":0.000006904691,"domain_scores_codex":[0.9990416,0.00002218561,0.0001736555,0.000299747,0.000225722,0.0002371461],"domain_scores_gemma":[0.9990249,0.00003070939,0.00003951492,0.0007043271,0.0001280337,0.00007255207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005856169,0.0001698253,0.7096535,0.0001469424,0.00003185621,0.000006708142,0.002304038,0.00004729355,0.008138876,0.004424146,0.06734437,0.2076739],"study_design_scores_gemma":[0.0006701865,0.0005598777,0.4827499,0.00006745532,0.000004383619,0.0001013349,0.00001630475,0.3038442,0.05579582,0.0002268682,0.1554317,0.0005319734],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474971,0.00002107093,0.02484217,0.0002577954,0.0009449479,0.000102965,1.632109e-7,0.000401946,0.02593187],"genre_scores_gemma":[0.9871871,0.00001040694,0.009439525,0.0004562346,0.0001515161,0.000005375967,6.281826e-7,0.000004346934,0.002744866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3037969,"threshold_uncertainty_score":0.9974657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339971948617336,"score_gpt":0.2267803210477097,"score_spread":0.2133806015615364,"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."}}