{"id":"W2972581957","doi":"10.1002/cjce.23642","title":"Chemical process fault diagnosis based on enchanted machine‐learning approach","year":2019,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Smoothing; Fault (geology); Computer science; Artificial neural network; Artificial intelligence; Process (computing); Convergence (economics); Machine learning; Probabilistic logic; Probabilistic neural network; Feature (linguistics); Support vector machine; Algorithm; Data mining; Time delay neural network","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":[0.0002338039,0.0001974878,0.000283,0.0001814642,0.00003744843,0.00006484475,0.0003223677,0.0001305855,0.00006659061],"category_scores_gemma":[0.000168821,0.0001530905,0.0001371881,0.000264206,0.00002030654,0.00006935382,0.000004531185,0.0008732287,0.00001743478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002398316,"about_ca_system_score_gemma":0.00009261665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001518582,"about_ca_topic_score_gemma":0.00001421623,"domain_scores_codex":[0.9989417,0.00001721246,0.0003206046,0.0001094558,0.0002594267,0.0003515599],"domain_scores_gemma":[0.9992294,0.0001126563,0.0000613765,0.0001518663,0.00006594342,0.0003787945],"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":[0.00001293808,0.000007024405,0.0002783276,0.0001060259,0.00004837707,0.00001032052,0.0001673181,0.9520916,0.04666767,0.00003363051,0.00007900592,0.0004977895],"study_design_scores_gemma":[0.0004915067,0.00002557561,0.00001193029,0.0001295889,0.00001672912,0.00006841137,0.00002006761,0.9259607,0.07089326,0.000004569402,0.002195552,0.0001820879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951255,0.0004361962,0.00134283,0.0001803758,0.0006093945,0.0002090817,0.000007405075,0.0001236185,0.0019656],"genre_scores_gemma":[0.9995377,0.000001420123,0.00009385055,0.00006605107,0.000211225,0.00001783234,0.000003483507,0.00004944593,0.00001896501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02613086,"threshold_uncertainty_score":0.6242852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004681226612557218,"score_gpt":0.1733081829934525,"score_spread":0.1686269563808953,"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."}}