{"id":"W2041938145","doi":"10.1109/noms.2012.6211882","title":"Interactive learning of alert signatures in High Performance Cluster system logs","year":2012,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Computer science; Anomaly detection; Signature (topology); Visualization; Feature (linguistics); Data mining; Cluster (spacecraft); Simple (philosophy); Machine learning; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007305901,0.0001256802,0.0002410981,0.0001407354,0.00005125494,0.00002181571,0.000425301,0.0001008398,0.00001897372],"category_scores_gemma":[0.00003700356,0.0000895023,0.0000467623,0.0003144162,0.00003425022,0.001208934,0.0002085677,0.0002669432,0.00006004747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00010736,"about_ca_system_score_gemma":0.00002961372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001434694,"about_ca_topic_score_gemma":0.000003744997,"domain_scores_codex":[0.9987805,0.0001219712,0.0003425883,0.0002097983,0.0002251202,0.0003200105],"domain_scores_gemma":[0.9992135,0.0001731964,0.0001344625,0.000337272,0.00008306281,0.00005852337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005099447,0.0001585314,0.9655781,0.0006512703,0.00002775261,0.000001671172,0.00553064,0.00852366,0.0003124934,0.005189105,0.000341279,0.01363451],"study_design_scores_gemma":[0.0009287727,0.0002487577,0.7070546,0.00057167,0.000007503759,0.00002701214,0.0009746474,0.2735032,0.01535444,0.00001950044,0.0009013083,0.0004085881],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554605,0.0001407393,0.03893393,0.00005457846,0.0008516354,0.0001852048,2.580418e-7,0.0001498566,0.004223329],"genre_scores_gemma":[0.9968652,0.000007700079,0.002639238,0.00005295845,0.00007244323,0.00001697035,6.544993e-7,0.000006163241,0.0003386794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2649795,"threshold_uncertainty_score":0.3649798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006903645971703883,"score_gpt":0.2260938502631949,"score_spread":0.219190204291491,"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."}}