{"id":"W2086975327","doi":"10.1109/tnsm.2011.120811.100033","title":"System Monitoring with Metric-Correlation Models","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Metric (unit); Heteroscedasticity; Ordinary least squares; Linear regression; Residual; Data mining; Process (computing); Linear model; Correlation; Variance (accounting); Wilcoxon signed-rank test; Rank (graph theory); Software; Machine learning; Algorithm; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004591729,0.001507421,0.001244415,0.001556191,0.0004304397,0.001637552,0.002266788,0.001454148,0.00229735],"category_scores_gemma":[0.02385396,0.0008652762,0.001200841,0.001930193,0.001064416,0.002951879,0.001640952,0.00210953,0.0006242528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00168621,"about_ca_system_score_gemma":0.001516608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01021279,"about_ca_topic_score_gemma":0.007080816,"domain_scores_codex":[0.9957097,0.001974044,0.0002509667,0.0006991965,0.0009863223,0.000379826],"domain_scores_gemma":[0.9870155,0.007795673,0.002233172,0.001334595,0.001386618,0.0002343961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003732359,0.00003156262,0.0017024,0.00002254465,0.00003201762,0.00004973781,0.00004328312,0.9681466,0.0001972701,0.02129182,0.0004256884,0.008019735],"study_design_scores_gemma":[0.000002597083,0.000005731572,0.000122721,0.00000149986,0.000003379613,0.000007718399,0.000001837681,0.9951978,0.00004544565,0.004460488,0.000147842,0.000002903146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02699091,0.0001992558,0.9678223,0.000415922,0.00004090239,0.00007977625,0.0003400793,0.001301117,0.002809687],"genre_scores_gemma":[0.879894,0.0003905807,0.1130505,0.000171049,0.000101601,0.0003899063,0.0005841011,0.0002549255,0.005163324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01021279,"threshold_uncertainty_score":0.02428371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210269319168555,"score_gpt":0.2006628953315815,"score_spread":0.178560202139896,"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."}}