{"id":"W2140720699","doi":"10.1109/tnsm.2008.080104","title":"Efficient fault diagnosis using incremental alarm correlation and active investigation for internet and overlay networks","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Overlay network; Overlay; Fault management; Distributed computing; Scalability; Fault (geology); Fault model; The Internet; Computer network; Real-time computing; ALARM; 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":[0.001403371,0.000675043,0.0008132937,0.001329043,0.0005323587,0.001222805,0.001626315,0.0006478859,0.0005362825],"category_scores_gemma":[0.005802579,0.0003288694,0.0006107995,0.0008960774,0.0006582286,0.002717294,0.00147891,0.000795019,0.0001036821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032449,"about_ca_system_score_gemma":0.001367706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008265685,"about_ca_topic_score_gemma":0.00651012,"domain_scores_codex":[0.9986358,0.000360296,0.00009747062,0.0002367352,0.0005376255,0.0001320208],"domain_scores_gemma":[0.9970996,0.001518263,0.00042822,0.0004830405,0.0003701626,0.000100713],"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":[0.0005339203,0.0003096039,0.007802984,0.0001618458,0.0001267141,0.0003640548,0.0004488603,0.5296747,0.01416006,0.01762197,0.001923143,0.4268721],"study_design_scores_gemma":[0.0000132785,0.00003015104,0.0003905403,0.00000303539,0.00002166623,0.00006328804,0.00002524976,0.9906901,0.003001175,0.005372011,0.0003815163,0.000008079959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0480847,0.0002029789,0.9488535,0.0001684052,0.00001306626,0.00008301594,0.00004433144,0.001628643,0.0009213447],"genre_scores_gemma":[0.8089848,0.00010768,0.1902586,0.0000494914,0.00001990793,0.00005388866,0.0001199393,0.00003362212,0.0003720492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008265685,"threshold_uncertainty_score":0.01643515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186822568874934,"score_gpt":0.2234991308502759,"score_spread":0.2048168739627825,"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."}}