{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001046264,0.0007280729,0.0008802078,0.001037625,0.0003162002,0.0008902615,0.001433962,0.00089314,0.002366011],"category_scores_gemma":[0.003085924,0.0002569861,0.0005942737,0.0006144447,0.0004506233,0.001128538,0.0009862623,0.001211109,0.0005944615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000519484,"about_ca_system_score_gemma":0.0007459135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001220057,"about_ca_topic_score_gemma":0.0008544755,"domain_scores_codex":[0.9988157,0.0002416701,0.00008347955,0.0003696092,0.0003831119,0.0001064297],"domain_scores_gemma":[0.9988262,0.0003020272,0.0002286562,0.0001916083,0.0003923775,0.00005923268],"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":[0.0008250842,0.0002483535,0.002300351,0.0001664879,0.0001223791,0.0002026425,0.000162513,0.1507874,0.1257987,0.01874227,0.002705659,0.6979382],"study_design_scores_gemma":[0.00003510598,0.0001377804,0.0008195324,0.000006914267,0.00002518046,0.00009491143,0.0000061296,0.9760074,0.01840052,0.002779019,0.001658886,0.00002854567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01395633,0.00009543905,0.9841549,0.00005259172,0.00003453647,0.00004056964,0.00004649846,0.001029227,0.0005899317],"genre_scores_gemma":[0.4501863,0.00008951007,0.5470797,0.00009160495,0.00008989881,0.0001092466,0.0002097746,0.00008002509,0.002064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002366011,"threshold_uncertainty_score":0.00791508,"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."}}