{"id":"W2064344480","doi":"10.5430/air.v1n2p75","title":"A Bayesian Network approach to diagnosing the root cause of failure from Trouble Tickets","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian network; Scope (computer science); Hierarchy; Computer science; Root cause; Context (archaeology); Root cause analysis; Element (criminal law); Root (linguistics); Network element; Network monitoring; Distributed computing; Computer security; Computer network; Artificial intelligence; Reliability engineering; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003194235,0.0009525152,0.001122832,0.003700229,0.000707982,0.001647004,0.00188938,0.001951925,0.002805374],"category_scores_gemma":[0.0130798,0.000797072,0.0008887982,0.002046321,0.001109382,0.002429693,0.0008782374,0.001532932,0.0004371625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001762874,"about_ca_system_score_gemma":0.001609966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01681345,"about_ca_topic_score_gemma":0.01254116,"domain_scores_codex":[0.9982755,0.0008255612,0.0001009804,0.0003448018,0.0003427315,0.0001103606],"domain_scores_gemma":[0.9931316,0.005659508,0.0004135704,0.0001298402,0.0005326848,0.0001328764],"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":[0.0002218411,0.0001374339,0.005678928,0.0002410161,0.0002510967,0.0004189424,0.0002611013,0.8212925,0.001645267,0.05855098,0.002508884,0.108792],"study_design_scores_gemma":[0.00002059873,0.00002650181,0.0008703127,0.00003116803,0.00006844001,0.0001083169,0.00003239949,0.9545799,0.000318125,0.04256809,0.001350279,0.00002580173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01100535,0.0005922682,0.985061,0.0006214143,0.00003012684,0.00006610081,0.000267583,0.0002536993,0.002102458],"genre_scores_gemma":[0.4874358,0.002395726,0.5026639,0.0003105536,0.0003450958,0.0003180466,0.001057813,0.00009568785,0.005377455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01681345,"threshold_uncertainty_score":0.03343117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1474488304530515,"score_gpt":0.3792363958667171,"score_spread":0.2317875654136656,"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."}}