{"id":"W2790537587","doi":"10.1007/s10664-018-9603-z","title":"Studying and detecting log-related issues","year":2018,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Logging; Computer science; Statement (logic); Data science; Software; Scale (ratio); Root (linguistics); Open source; Data mining; Operating system","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":[0.0004266994,0.0001806584,0.0002165943,0.00009731612,0.0002090299,0.0001152589,0.0003510012,0.0001158037,0.0000122249],"category_scores_gemma":[0.0006740543,0.0001540447,0.0000489221,0.0004904899,0.00005237714,0.0003938421,0.0003171944,0.0002188213,0.00007287644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005345135,"about_ca_system_score_gemma":0.00002055082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000244421,"about_ca_topic_score_gemma":0.00000155472,"domain_scores_codex":[0.9986739,0.00002926534,0.0002880434,0.0004225127,0.0002167025,0.0003696017],"domain_scores_gemma":[0.9990953,0.0002574419,0.00004651141,0.0003884165,0.00008094287,0.000131345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008522714,0.00008093378,0.8961291,0.0002545848,0.0001119274,0.00003910366,0.01081568,0.001745092,0.0005002599,0.0001904926,0.001033862,0.08909048],"study_design_scores_gemma":[0.00130191,0.0007841918,0.7239574,0.0004506828,0.00003234398,0.0002792524,0.0001850216,0.2248443,0.006434192,0.001029761,0.03905753,0.00164336],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6337518,0.0004890784,0.3636005,0.0001910778,0.0006775502,0.000112721,3.057817e-7,0.001145999,0.00003095336],"genre_scores_gemma":[0.9484857,0.000008895233,0.05110797,0.00008595525,0.0002216887,0.00001095631,3.90669e-7,0.00001665649,0.00006180712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3147339,"threshold_uncertainty_score":0.628176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901809487551466,"score_gpt":0.2728338234890282,"score_spread":0.2538157286135136,"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."}}