{"id":"W4399366048","doi":"10.1016/j.ces.2024.120324","title":"Semi-supervised learning for predicting multivariate attributes of process units from small labeled and large unlabeled data sets with application to detect properties of crude feed distillation unit","year":2024,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Project 211; International Cooperation and Exchange Programme; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Distillation; Refinery; Multivariate statistics; Process (computing); Data mining; Machine learning; Artificial intelligence; Supervised learning; Engineering; Artificial neural network; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002834035,0.001122399,0.001702527,0.0008340581,0.0008572989,0.0008484178,0.001883838,0.001422236,0.0006731976],"category_scores_gemma":[0.005448241,0.0006309989,0.001243983,0.0008789515,0.0009364038,0.001509168,0.00135249,0.001580136,0.0004027886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006769626,"about_ca_system_score_gemma":0.001443797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005430007,"about_ca_topic_score_gemma":0.008489922,"domain_scores_codex":[0.9985265,0.0005683778,0.0001252375,0.0004093918,0.0002753414,0.00009519231],"domain_scores_gemma":[0.9922758,0.005218752,0.0004657637,0.0008075868,0.001092832,0.0001391816],"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":[0.0008656933,0.001170855,0.0080125,0.0002585544,0.0003141739,0.0002330073,0.0002697789,0.5184461,0.01237217,0.003362525,0.00413715,0.4505575],"study_design_scores_gemma":[0.000007745633,0.00002235569,0.0002911676,0.000001752533,0.000005797323,0.000007358912,0.000007204825,0.9979382,0.0008414459,0.000806105,0.00006745519,0.000003357442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1196153,0.0004438197,0.8759497,0.0002134464,0.00006652639,0.0001270683,0.0004532598,0.002475409,0.000655439],"genre_scores_gemma":[0.7478226,0.0001581123,0.2473081,0.0001648779,0.0001050507,0.0003567784,0.001840013,0.0001084296,0.002135894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005430007,"threshold_uncertainty_score":0.01498801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193223971288434,"score_gpt":0.2353138964058051,"score_spread":0.2133816566929207,"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."}}