{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009794924,0.0001090675,0.0001638871,0.00003913506,0.00003750178,0.00004040747,0.0001230083,0.00007035731,0.000005874653],"category_scores_gemma":[0.00009663572,0.00007950509,0.0000449274,0.00009835385,0.00002314394,0.00004277483,0.000006254585,0.0003377291,0.000003050425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079371,"about_ca_system_score_gemma":0.00003942995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007931341,"about_ca_topic_score_gemma":0.00004217556,"domain_scores_codex":[0.9994236,0.000008833613,0.0002273322,0.00006833337,0.0001098949,0.0001619949],"domain_scores_gemma":[0.9993798,0.00003752907,0.00004299896,0.00007774707,0.00003876043,0.0004232075],"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":[0.00001474953,0.000002128636,0.00004255349,0.0001631764,0.00004784253,0.00002003782,0.0005322974,0.6144317,0.3823771,0.0001355203,0.0003601978,0.001872698],"study_design_scores_gemma":[0.0002944646,0.00001667175,0.00008083054,0.00008352315,0.00002282059,0.0001903986,0.00002244374,0.9775784,0.01247999,0.00001318802,0.009087595,0.0001296665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978942,0.001258079,0.0167229,0.002294972,0.0003363073,0.0002399564,0.000007151132,0.00009639238,0.0001022161],"genre_scores_gemma":[0.9993793,0.000001994533,0.0002181947,0.00005728085,0.000313844,0.000004467715,0.000001031574,0.00002012005,0.00000377325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3698972,"threshold_uncertainty_score":0.3242124,"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."}}