{"id":"W2135812044","doi":"10.1002/cjce.21795","title":"Fault detection and diagnosis for distillation column using two‐tier model","year":2013,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distillation; Fractionating column; Fault detection and isolation; Nonlinear system; Fault (geology); Process (computing); Stripping (fiber); Linear model; Computer science; Engineering; Chemistry; Chromatography; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000101258,0.00008935812,0.00013024,0.00008261626,0.00005790587,0.00008280565,0.00006702498,0.00005826847,0.000006329466],"category_scores_gemma":[0.00007544873,0.00007625165,0.00005402716,0.00007605382,0.00001639097,0.0001299618,0.000002679991,0.0001464636,8.913259e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001967354,"about_ca_system_score_gemma":0.00003189073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009631996,"about_ca_topic_score_gemma":0.0003833753,"domain_scores_codex":[0.9994905,0.000004348502,0.0002093429,0.00004860912,0.00007081991,0.0001763692],"domain_scores_gemma":[0.9995596,0.00005352551,0.00003742512,0.0000597557,0.00007367649,0.0002159576],"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":[0.000001997685,6.198339e-7,0.00003326488,0.00002605895,0.00002774926,4.644275e-7,0.0001011438,0.8539883,0.1426204,0.00002400079,0.00006464485,0.003111347],"study_design_scores_gemma":[0.0002636796,0.000008321054,0.00003656576,0.00003420351,0.00002158492,0.00004565449,0.000009129978,0.9775895,0.02133096,0.00009972508,0.0004721663,0.00008844918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378697,0.0003931766,0.06110315,0.00007963918,0.0003220378,0.0001731937,0.000004663452,0.0000250483,0.0000294024],"genre_scores_gemma":[0.9992036,0.000001978169,0.0005199108,0.00001643715,0.0002032373,0.00002393721,3.261651e-7,0.00002336745,0.000007226616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1236012,"threshold_uncertainty_score":0.3109452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00925473925855949,"score_gpt":0.1905942057914352,"score_spread":0.1813394665328757,"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."}}