{"id":"W1997175427","doi":"10.1002/aic.14270","title":"Mixture semisupervised principal component regression model and soft sensor application","year":2013,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Alberta Innovates - Technology Futures","keywords":"Soft sensor; Principal component analysis; Probabilistic logic; Regression; Component (thermodynamics); Computer science; Process (computing); Data mining; Pattern recognition (psychology); Principal component regression; Regression analysis; Artificial intelligence; Mathematics; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001691741,0.001017142,0.001278605,0.0007248279,0.0003052825,0.0008639588,0.001519468,0.001262902,0.001315945],"category_scores_gemma":[0.005321627,0.0006253324,0.0008807807,0.001080776,0.001063832,0.001474719,0.001091581,0.001486699,0.0006688607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005109164,"about_ca_system_score_gemma":0.0006355455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002519653,"about_ca_topic_score_gemma":0.001966954,"domain_scores_codex":[0.9984062,0.0007759635,0.00005815209,0.0003337409,0.0003571101,0.00006877098],"domain_scores_gemma":[0.9973002,0.001536205,0.0003731163,0.0002638604,0.0004758894,0.00005073058],"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":[0.0001391272,0.00006510518,0.0007154389,0.0001461353,0.00008570525,0.0001262103,0.00008589574,0.9140557,0.004546683,0.01667631,0.001426724,0.06193099],"study_design_scores_gemma":[0.000001314578,0.000007088571,0.00005220217,0.00000130267,0.000002453139,0.00001348546,0.000001855409,0.9975553,0.0002990624,0.001923822,0.0001387452,0.000003278218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007547367,0.0002064049,0.9912072,0.000142002,0.00001355558,0.00001836511,0.00004112368,0.0002531389,0.000570958],"genre_scores_gemma":[0.6654142,0.0007154561,0.3270157,0.0001423913,0.0001342722,0.0002332263,0.0004225481,0.0001569913,0.005765133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002519653,"threshold_uncertainty_score":0.008946896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007492659252881674,"score_gpt":0.2108283867937354,"score_spread":0.2033357275408537,"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."}}