{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001129422,0.0001075371,0.0001257839,0.00004543588,0.00009902796,0.00007719758,0.00006427246,0.00009018968,0.0000314699],"category_scores_gemma":[0.000007829744,0.00008174902,0.00003927239,0.00004465322,0.00001003313,0.0001326574,0.00001173585,0.0002718416,0.00005254396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004212676,"about_ca_system_score_gemma":0.000007323561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009781314,"about_ca_topic_score_gemma":0.000002708206,"domain_scores_codex":[0.9994006,0.00002409035,0.0001968287,0.0000862204,0.0001439486,0.0001483096],"domain_scores_gemma":[0.9996464,0.00001510359,0.00004110851,0.0001081595,0.00004973175,0.0001394794],"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":[0.00001430262,0.00001863366,0.0005005571,0.00005258013,0.00004850042,0.000003051695,0.000464069,0.07430637,0.8889142,0.00002129078,0.005042469,0.03061396],"study_design_scores_gemma":[0.0004758688,0.00001138628,0.001223098,0.00003305665,0.000009352739,0.000188711,0.0001008939,0.991442,0.001605851,0.000105242,0.004698535,0.0001060439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507066,0.000616599,0.04706401,0.0003649502,0.0001961095,0.0002026405,0.000001431206,0.0001392672,0.0007083906],"genre_scores_gemma":[0.9986307,0.00007650103,0.0006619283,0.00007497358,0.0002027673,0.00002308126,0.000001503359,0.00001856169,0.0003099997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9171356,"threshold_uncertainty_score":0.3333628,"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."}}