{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004374818,0.0008766154,0.0004375414,0.01174584,0.0009423982,0.005852703,0.001542483,0.001330834,0.002225862],"category_scores_gemma":[0.03609473,0.0007191847,0.0005708826,0.005627516,0.001441077,0.01435148,0.003648612,0.001659125,0.0007694245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001995778,"about_ca_system_score_gemma":0.002503519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009576887,"about_ca_topic_score_gemma":0.01003968,"domain_scores_codex":[0.9954348,0.001437495,0.0005740871,0.0009299905,0.001370225,0.0002532951],"domain_scores_gemma":[0.9532417,0.03319544,0.004710121,0.004129428,0.004171225,0.0005521921],"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":[0.0002287093,0.0006150394,0.139578,0.001701337,0.0001071961,0.00330473,0.05756115,0.01196781,0.006768371,0.04289183,0.008253363,0.7270223],"study_design_scores_gemma":[0.00008714716,0.0004652767,0.1755408,0.004078916,0.0003697929,0.005005816,0.08658251,0.2369843,0.02076828,0.1901558,0.279633,0.0003283415],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3774751,0.002645188,0.5627005,0.004688028,0.00006694147,0.0009274818,0.005994909,0.003987329,0.04151447],"genre_scores_gemma":[0.7593194,0.001785309,0.2281279,0.0003121709,0.00003662812,0.000378805,0.006463351,0.0002818763,0.00329453],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01174584,"threshold_uncertainty_score":0.0231365,"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."}}