{"id":"W2470162039","doi":"10.1002/cjce.22568","title":"Locality preserving based data regression and its application for soft sensor modelling","year":2016,"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":"National Natural Science Foundation of China","keywords":"Soft sensor; Locality; Robustness (evolution); Regression; Generalization; Computer science; Process (computing); Data mining; Soft computing; Regression analysis; Artificial intelligence; Machine learning; Mathematics; Statistics; Artificial neural network","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":[0.000331149,0.00008888755,0.0001308814,0.00005530243,0.00004258975,0.00002586865,0.0002557786,0.00006433435,0.000003538515],"category_scores_gemma":[0.0001443881,0.0000548978,0.00003179276,0.00005606165,0.00001163375,0.000125926,0.00001006596,0.000115497,8.646933e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009539702,"about_ca_system_score_gemma":0.00005755771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009970694,"about_ca_topic_score_gemma":0.00006067501,"domain_scores_codex":[0.9994267,0.000007818656,0.0002189365,0.00008287416,0.0000883739,0.0001753536],"domain_scores_gemma":[0.9993212,0.0001445312,0.00004397183,0.000205986,0.00006130886,0.0002230015],"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":[0.00001636427,0.000001522591,0.00001588495,0.0001340525,0.00003548231,0.000001993561,0.00003702806,0.5325708,0.4612319,0.00008844835,0.0002082742,0.005658247],"study_design_scores_gemma":[0.0002987325,0.000004831143,0.000002665092,0.000147257,0.0000135497,0.00001894838,0.000003215434,0.9472616,0.04709703,0.00003341023,0.005043236,0.00007550042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1819176,0.001110936,0.8155479,0.0008638497,0.0002390679,0.0002098521,0.00004118848,0.00004914369,0.00002039251],"genre_scores_gemma":[0.9992596,0.000002955692,0.0004745353,0.0000153272,0.0002091773,0.000006870163,0.000001746352,0.00002083125,0.000008985018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.817342,"threshold_uncertainty_score":0.2238667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02195613140058026,"score_gpt":0.2140583885406412,"score_spread":0.192102257140061,"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."}}