{"id":"W2772343646","doi":"10.1002/aic.16048","title":"A full‐condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":274,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Cointegration; Relation (database); Process (computing); Dynamic equilibrium; Chemical process; Feature (linguistics); Control theory (sociology); Term (time); Interpretation (philosophy); Variable (mathematics); Current (fluid); Computer science; Mathematics; Applied mathematics; Econometrics; Engineering; Control (management); Thermodynamics; Data mining; Physics; Mathematical analysis; Artificial intelligence","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":[0.0001861032,0.00009689996,0.0001621604,0.0001064118,0.0002846592,0.0002455992,0.00007053432,0.00006729605,0.000003852294],"category_scores_gemma":[0.0001037993,0.00007549024,0.00005195721,0.00009414137,0.00001548178,0.0003139319,0.000004238685,0.000175604,7.810677e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005619025,"about_ca_system_score_gemma":0.00002871211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005377854,"about_ca_topic_score_gemma":0.00007491461,"domain_scores_codex":[0.9995277,0.00001535797,0.0001355065,0.00009497206,0.0001169845,0.0001095001],"domain_scores_gemma":[0.9995156,0.00007152098,0.0001147736,0.00009659891,0.0001392879,0.00006222714],"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":[0.001395633,0.000104069,0.01895642,0.001253018,0.006943668,0.00006041546,0.002719756,0.06083969,0.7629617,0.0001236954,0.002356895,0.142285],"study_design_scores_gemma":[0.001948785,0.0001996562,0.01572349,0.0002172822,0.0009854124,0.0007831375,0.001005159,0.9634234,0.01404436,0.0003544743,0.0009714371,0.0003434225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2901281,0.0004437546,0.7084106,0.0003849522,0.0002334745,0.0001543884,0.00001510109,0.00006426232,0.0001653513],"genre_scores_gemma":[0.9773843,0.00005793165,0.02217492,0.000006919472,0.0001975345,0.00003291681,0.00001023506,0.00001348417,0.0001217389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9025837,"threshold_uncertainty_score":0.3078403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006016667371473136,"score_gpt":0.2768361899828129,"score_spread":0.2708195226113397,"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."}}