{"id":"W3216607771","doi":"10.1016/j.jprocont.2021.11.001","title":"Adversarial smoothing tri-regression for robust semi-supervised industrial soft sensor","year":2021,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"State Key Laboratory of Industrial Control Technology; China National Funds for Distinguished Young Scientists; Zhejiang University; National Natural Science Foundation of China","keywords":"Smoothing; Soft sensor; Regularization (linguistics); Machine learning; Artificial intelligence; Regression; Computer science; Smoothness; Mathematics; Pattern recognition (psychology); Data mining; Statistics; Process (computing)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004489413,0.0002029542,0.0005710777,0.0001358478,0.0001050566,0.00011364,0.0001648208,0.0002482558,0.00005328096],"category_scores_gemma":[0.0005617359,0.0001661669,0.0003005841,0.0001926897,0.00001379563,0.0003141856,0.00000686782,0.0004606094,0.00000419722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009965614,"about_ca_system_score_gemma":0.0002066601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000275296,"about_ca_topic_score_gemma":0.00000583062,"domain_scores_codex":[0.9984,0.00006648805,0.0007371343,0.0001443062,0.0003678281,0.0002842292],"domain_scores_gemma":[0.99871,0.000233077,0.0002704196,0.0001219101,0.0005145998,0.00015003],"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":[0.005372402,0.0002596155,0.0008714782,0.0008705878,0.001711759,0.0003564825,0.002149574,0.5613313,0.2864177,0.00004214182,0.01399491,0.126622],"study_design_scores_gemma":[0.03765728,0.0002848046,0.00003405204,0.000563638,0.0003244166,0.0003676725,0.001383713,0.9104214,0.01954482,0.00007668299,0.02891295,0.0004285608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3442022,0.004405823,0.6318879,0.001895384,0.01512798,0.001267616,0.00009026696,0.0003596912,0.0007631687],"genre_scores_gemma":[0.9959121,0.00001580189,0.0004134868,0.0001105537,0.00332292,0.00002307626,0.000002515448,0.0000432857,0.0001562356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6517099,"threshold_uncertainty_score":0.677609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271950539412964,"score_gpt":0.2438712133421851,"score_spread":0.2211517079480554,"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."}}