{"id":"W3044875715","doi":"10.1016/j.ifacol.2020.12.877","title":"Clustering of Redundant Parameters for Fault Isolation with Gaussian Residuals","year":2020,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Deutscher Akademischer Austauschdienst","keywords":"Cluster analysis; Fault detection and isolation; False alarm; Gaussian; Fault (geology); Residual; Computer science; Isolation (microbiology); Cluster (spacecraft); Mathematics; Algorithm; Mathematical optimization; Statistics; Artificial intelligence; Physics","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":[0.00007844167,0.0001543114,0.0002683847,0.00004828691,0.00004230239,0.00002427413,0.00008881745,0.00007259694,0.00001585304],"category_scores_gemma":[0.00004107146,0.0001315035,0.0000742989,0.0001586226,0.00002120844,0.00009390265,0.000008548133,0.0000951451,0.000008133918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003582352,"about_ca_system_score_gemma":0.00001303359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003343043,"about_ca_topic_score_gemma":0.00009821009,"domain_scores_codex":[0.9991698,0.00001857528,0.0002938277,0.0001797256,0.000144416,0.0001936466],"domain_scores_gemma":[0.9996078,0.00005204033,0.00006783838,0.0001336993,0.00003797262,0.0001006405],"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":[0.0007124507,0.00003310471,0.0001813145,0.0008286507,0.000315812,0.000008194957,0.003561082,0.5277592,0.4490827,0.00005245013,0.00007207769,0.01739299],"study_design_scores_gemma":[0.001299642,0.0003408838,0.0001527861,0.00008433348,0.0000368389,0.000008204717,0.0007525071,0.9840272,0.01154137,0.000004258563,0.001552124,0.0001998096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6654949,0.0003658007,0.327253,0.002758409,0.0004729668,0.001167043,0.0001694963,0.0006755638,0.001642927],"genre_scores_gemma":[0.8948802,0.00001057757,0.1046054,0.0001592884,0.0001476164,0.00004080493,0.00002289691,0.00004210103,0.00009107649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4562681,"threshold_uncertainty_score":0.5362557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01635744081823537,"score_gpt":0.2282685171081569,"score_spread":0.2119110762899216,"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."}}