{"id":"W3010196813","doi":"10.1002/cjce.23739","title":"Mixture robust L1 probabilistic principal component regression and soft sensor application","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; State Key Laboratory of Robotics and System; National Natural Science Foundation of China","keywords":"Probabilistic logic; Outlier; Principal component analysis; Principal component regression; Laplace distribution; Multivariate statistics; Computer science; Robust regression; Component (thermodynamics); Artificial intelligence; Statistical model; Gaussian process; Multivariate normal distribution; Gaussian; Algorithm; Mathematics; Laplace transform; Pattern recognition (psychology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001358537,0.0009574381,0.0007680894,0.0007124725,0.0002967106,0.0008403751,0.0009192965,0.0007896658,0.001137773],"category_scores_gemma":[0.003639732,0.0003996449,0.0008601681,0.0007922827,0.0007866754,0.001117545,0.0009776787,0.001024008,0.0004579622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005907172,"about_ca_system_score_gemma":0.0006436203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002897612,"about_ca_topic_score_gemma":0.001428699,"domain_scores_codex":[0.9988341,0.0004199881,0.00004173459,0.0002356102,0.0004096842,0.00005881374],"domain_scores_gemma":[0.9989052,0.0006308255,0.00015569,0.00007587363,0.0002098808,0.00002256913],"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":[0.00008862542,0.00003767288,0.0006600283,0.0001305489,0.00004862946,0.0001105444,0.00008720648,0.8741734,0.008968547,0.01960718,0.0008167376,0.09527079],"study_design_scores_gemma":[0.000001355986,0.00001257918,0.00008452182,0.000001883297,0.000002442277,0.00001236655,0.000002918416,0.9972963,0.0008616268,0.001510064,0.0002086424,0.000005416584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004716938,0.0001327879,0.9942029,0.0000845783,0.00001061519,0.00000923038,0.00001264312,0.0002017373,0.0006285422],"genre_scores_gemma":[0.8070045,0.0005820447,0.1882186,0.0001213064,0.00007450939,0.00009115318,0.000143289,0.0001555509,0.003608986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002897612,"threshold_uncertainty_score":0.007184744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008609392829422838,"score_gpt":0.1756502853640464,"score_spread":0.1670408925346236,"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."}}