{"id":"W19297562","doi":"10.1056/nejme0902377","title":"Log analysis and event correlation using variable temporal event correlator (VTEC)","year":2010,"lang":"en","type":"article","venue":"USENIX Large Installation Systems Administration Conference","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Event (particle physics); Real-time computing; VTEC; Variable (mathematics); Automation; Interface (matter); Scope (computer science); Cloud computing; Volume (thermodynamics); Extensibility; Process (computing); Data mining; Operating system; 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":[0.003314147,0.001030133,0.0008592162,0.004120549,0.0006867694,0.002085476,0.001405349,0.0006207286,0.004798871],"category_scores_gemma":[0.0155212,0.0005918265,0.0005853653,0.003311763,0.0007876082,0.002073525,0.001644322,0.001437204,0.001142732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006890955,"about_ca_system_score_gemma":0.002674101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004325396,"about_ca_topic_score_gemma":0.00357679,"domain_scores_codex":[0.9964836,0.0007685979,0.0002902698,0.0006776862,0.001556025,0.0002237337],"domain_scores_gemma":[0.9879739,0.00623863,0.001327943,0.00221176,0.001840384,0.0004073727],"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":[0.001273576,0.0005459454,0.04261041,0.0006008482,0.0003606446,0.001289621,0.0009831057,0.05835433,0.03663535,0.03587364,0.03791088,0.7835616],"study_design_scores_gemma":[0.0001187249,0.0002687263,0.008839219,0.00008500017,0.00008428314,0.0009698252,0.0001555632,0.8882833,0.05641992,0.01487924,0.02974771,0.0001484516],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.016896,0.0001595124,0.9485437,0.0001664847,0.0001410542,0.0002154438,0.0007706476,0.03021084,0.002896421],"genre_scores_gemma":[0.3230231,0.0002574064,0.6694281,0.0002435163,0.00015851,0.0004527837,0.001705395,0.001600514,0.003130823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004798871,"threshold_uncertainty_score":0.0175271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174247901311357,"score_gpt":0.2733039614340599,"score_spread":0.2558791713029242,"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."}}