{"id":"W2483067896","doi":"10.1016/b978-0-12-411519-4.00018-5","title":"Mining Software Logs for Goal-Driven Root Cause Analysis","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Root cause analysis; Root (linguistics); Computer science; Data mining; Software engineering; Engineering; Reliability engineering; Philosophy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008034799,0.0006352979,0.001217548,0.0005001973,0.0002382187,0.0001857717,0.001537842,0.0006211876,0.00003530258],"category_scores_gemma":[0.0001023446,0.0005401233,0.0008858552,0.0001118102,0.000137053,0.000211317,0.0005130818,0.0003656393,0.0001637792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000284667,"about_ca_system_score_gemma":0.0004399292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000214084,"about_ca_topic_score_gemma":0.0001176485,"domain_scores_codex":[0.996658,0.00004624484,0.0008441336,0.001180641,0.0006991041,0.0005719331],"domain_scores_gemma":[0.9961212,0.0002650846,0.0005538377,0.002074256,0.0006742763,0.000311379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001465007,0.00001313303,0.0009785594,0.0002748962,0.001296732,0.00003578599,0.001098834,0.0001233421,9.5558e-7,0.001384145,0.001651236,0.9931277],"study_design_scores_gemma":[0.0005485028,0.0001881944,0.0001506313,0.000337277,0.001103902,0.00002538356,0.0000098796,0.001685915,0.00001166501,0.006028951,0.9889342,0.000975569],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0007082332,0.003404281,0.1088548,0.0002271555,0.003480343,0.003362147,0.0001981661,0.001573916,0.8781909],"genre_scores_gemma":[0.002360369,0.00001722216,0.03343072,0.0001655736,0.0004991596,0.0001900403,0.00008814828,0.00008717921,0.9631616],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9921522,"threshold_uncertainty_score":0.999705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100444830357659,"score_gpt":0.2724281780965521,"score_spread":0.2414237297929755,"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."}}