{"id":"W2100246847","doi":"10.1109/icpc.2011.24","title":"Capturing Expert Knowledge for Automated Configuration Fault Diagnosis","year":2011,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Troubleshooting; Computer science; Automation; Expert system; Process (computing); Software engineering; Software; Configuration Management (ITSM); Fault (geology); Software bug; Software configuration management; Artificial intelligence; Machine learning; Software system; Operating system; Software construction","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002126993,0.00009922414,0.0001204475,0.00005085684,0.0001166385,0.00004823644,0.0003599764,0.00007255394,0.00004862611],"category_scores_gemma":[0.00004610951,0.00007272363,0.00006305873,0.0001340916,0.00002128232,0.0004719882,0.00005697267,0.00003391931,0.0001343612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003620667,"about_ca_system_score_gemma":0.0000428495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001697824,"about_ca_topic_score_gemma":0.00002349225,"domain_scores_codex":[0.9992229,0.00002488487,0.0002121537,0.0002648038,0.00008099496,0.0001942618],"domain_scores_gemma":[0.9992964,0.00008971171,0.00004770323,0.0003596657,0.0001480241,0.00005845304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009010999,0.001983523,0.06914657,0.0008684443,0.000244509,0.00001317371,0.1473779,0.0001682422,0.002345541,0.09980661,0.1776596,0.5002958],"study_design_scores_gemma":[0.001331431,0.0003519732,0.03456425,0.0001181148,0.00001143643,0.00001477466,0.0004344451,0.737996,0.1923123,0.001498648,0.03063018,0.0007364459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06485979,0.0002894948,0.9109244,0.0002379744,0.00162263,0.0007751547,0.000001821732,0.003211958,0.01807681],"genre_scores_gemma":[0.9695745,0.00001345534,0.02958425,0.0001134099,0.00006455699,0.000346596,0.000002077183,0.000006008223,0.0002951126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9047148,"threshold_uncertainty_score":0.2965583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04808761548663284,"score_gpt":0.2883663511378135,"score_spread":0.2402787356511807,"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."}}