{"id":"W2027241772","doi":"10.1109/icsme.2014.24","title":"Understanding Log Lines Using Development Knowledge","year":2014,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Queen's University","funders":"","keywords":"Computer science; Context (archaeology); World Wide Web; Meaning (existential); Source lines of code; Task (project management); Code (set theory); Web log analysis software; Software development; Software; Data science; Information retrieval; The Internet; Web server; Programming language; Web API; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005024848,0.00008482191,0.0001067674,0.00006359971,0.0001859681,0.00007020144,0.0003268109,0.00004577048,0.00001022346],"category_scores_gemma":[0.00002796939,0.0000611493,0.00002765268,0.000214664,0.00002259999,0.0002474747,0.0001630957,0.00004728208,0.0001175452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001535913,"about_ca_system_score_gemma":0.00009873482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008196346,"about_ca_topic_score_gemma":0.00001438289,"domain_scores_codex":[0.9992565,0.00003494499,0.0001855217,0.0002195293,0.0001148809,0.0001886226],"domain_scores_gemma":[0.9994962,0.00007339287,0.00003580921,0.0002918359,0.00004714238,0.00005559636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001488373,0.0005218099,0.2455331,0.0008763708,0.0001311211,0.000006411303,0.01737158,0.003210166,0.00246309,0.5050927,0.005900872,0.2188779],"study_design_scores_gemma":[0.0006381885,0.00006957982,0.008635378,0.0001690797,0.000006321606,0.00003322834,0.0001972076,0.9249719,0.01025417,0.008268279,0.0460875,0.0006691003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0531849,0.00002942112,0.9374838,0.00006968528,0.0006161226,0.00006033983,2.490855e-8,0.0002194679,0.008336227],"genre_scores_gemma":[0.9053104,0.000001124359,0.0942536,0.00006494329,0.00007947592,0.000002095915,2.24169e-7,0.000003643815,0.0002845023],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9217618,"threshold_uncertainty_score":0.2493596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1502089638642091,"score_gpt":0.295623458088261,"score_spread":0.1454144942240519,"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."}}