{"id":"W4401733573","doi":"10.1021/acs.iecr.4c00424","title":"Fault Detection in Industrial Wastewater Treatment Processes Using Manifold Learning and Support Vector Data Description","year":2024,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; Guangxi Key Laboratory of Clean Pulp and Papermaking and Pollution Control; Natural Science Foundation of Jiangsu Province","keywords":"Fault detection and isolation; Computer science; Benchmark (surveying); Support vector machine; Nonlinear dimensionality reduction; Data mining; Nonlinear system; Curse of dimensionality; Dimensionality reduction; Feature vector; Sensitivity (control systems); Process (computing); Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007706656,0.0002577542,0.0002618643,0.0002725923,0.00008877056,0.0004255201,0.0001930016,0.0004516942,0.00005932372],"category_scores_gemma":[0.0003493331,0.0002568875,0.00003246825,0.0008108057,0.00002457204,0.0003816377,0.0000892686,0.001237227,0.00001461594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007192097,"about_ca_system_score_gemma":0.0001636322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002515808,"about_ca_topic_score_gemma":0.00002957842,"domain_scores_codex":[0.9981549,0.00005399778,0.0003755899,0.000480436,0.0003851253,0.0005499832],"domain_scores_gemma":[0.9993744,0.0001508444,0.00001732874,0.0002644879,0.00004463357,0.0001482975],"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":[0.00004958128,0.00001689927,0.0001334077,0.0003975709,0.00007817765,0.000079534,0.0001475602,0.02565799,0.9638805,0.000001010634,0.0000842852,0.009473439],"study_design_scores_gemma":[0.0009869928,0.0000844677,0.000007465369,0.0003465771,0.00002074948,0.00009863413,0.0002169557,0.5711659,0.3855928,0.00000122517,0.04122885,0.0002493453],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970546,0.000709211,0.00006271174,0.00003017731,0.0008985184,0.0004103737,0.00003272733,0.0005404941,0.0002611503],"genre_scores_gemma":[0.9973714,0.00009213803,0.000005703362,3.262988e-7,0.001614082,0.00007809994,0.00005351814,0.00007141649,0.0007133231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5782877,"threshold_uncertainty_score":0.9999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1427591403923616,"score_gpt":0.3301442373547896,"score_spread":0.187385096962428,"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."}}