{"id":"W2006831848","doi":"10.1002/cem.1020","title":"An adaptive regression adjusted monitoring and fault isolation scheme","year":2006,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fault detection and isolation; Multivariate statistics; Fault (geology); Constant false alarm rate; Computer science; ALARM; Dimension (graph theory); Scheme (mathematics); Regression; Isolation (microbiology); Chart; Regression analysis; False alarm; Data mining; Statistics; Artificial intelligence; Mathematics; Machine learning; Engineering","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":[0.0002124718,0.00009325428,0.0001769108,0.0005568262,0.00003736484,0.00004923011,0.00006480938,0.00009416798,0.000003583718],"category_scores_gemma":[0.00005668059,0.00007851078,0.0000443211,0.0007420699,0.000008135145,0.0003261665,0.000005072682,0.0002009732,0.000001694446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008829743,"about_ca_system_score_gemma":0.000008012477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008026627,"about_ca_topic_score_gemma":8.446103e-7,"domain_scores_codex":[0.9992343,0.00001762485,0.0003243247,0.00006046129,0.0002545939,0.0001086588],"domain_scores_gemma":[0.9994982,0.00003997177,0.0001518644,0.00007319713,0.0001608406,0.00007585905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002206426,0.0001259632,0.0330634,0.000133153,0.0001225454,0.00004890891,0.0002922481,0.03421615,0.8127508,0.00009341462,0.001504373,0.1174284],"study_design_scores_gemma":[0.003545156,0.0007388219,0.07937504,0.0003441077,0.00007582668,0.0003885292,0.001161344,0.7260423,0.1800857,0.0001878575,0.007535364,0.0005198854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816421,0.00254056,0.0142885,0.00002310873,0.0007013445,0.00004795143,9.881866e-7,0.00005653651,0.0006988836],"genre_scores_gemma":[0.9968133,0.00008584729,0.002382637,0.00000251293,0.0006353233,8.571976e-7,4.791028e-7,0.00001574675,0.0000632736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6918262,"threshold_uncertainty_score":0.3201577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464410538461961,"score_gpt":0.2405204069987054,"score_spread":0.2258763016140858,"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."}}