{"id":"W2009720286","doi":"10.1002/aic.11080","title":"Autoassociative neural networks for robust dynamic data reconciliation","year":2007,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Kalman filter; Computer science; Fractionating column; Noise (video); Artificial neural network; Dynamic data; Filter (signal processing); Process (computing); Online model; Computation; Control theory (sociology); Artificial intelligence; Control engineering; Distillation; Algorithm; Control (management); Engineering; Mathematics; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002038772,0.0005101653,0.0006617712,0.0005979491,0.0004565126,0.0007748027,0.001344516,0.0008391404,0.001335258],"category_scores_gemma":[0.004680894,0.0003937829,0.0003592229,0.0008055074,0.0006146995,0.00135313,0.001388457,0.0009098627,0.0003513319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007156501,"about_ca_system_score_gemma":0.0007265952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002397395,"about_ca_topic_score_gemma":0.002440197,"domain_scores_codex":[0.9991575,0.0002487238,0.00007245223,0.0002108698,0.0002327601,0.00007767913],"domain_scores_gemma":[0.9981609,0.0007284448,0.0002895129,0.0003497922,0.0004343556,0.00003709786],"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":[0.0002732598,0.0001556112,0.0009091215,0.00008416623,0.0001230054,0.0001000182,0.000106242,0.5478714,0.00941196,0.008050798,0.001642075,0.4312723],"study_design_scores_gemma":[0.00000880735,0.00002512568,0.0002469674,0.000006345197,0.00001067064,0.00002659912,0.000006476827,0.9923989,0.004039196,0.002492994,0.0007295912,0.000008296306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03829554,0.0005197555,0.9579851,0.000193497,0.0000871324,0.00004323358,0.00003870923,0.001410195,0.001426791],"genre_scores_gemma":[0.7826155,0.0001751439,0.2145394,0.0001858549,0.00005847414,0.0001223023,0.0001251654,0.00006610034,0.002112003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002397395,"threshold_uncertainty_score":0.01078218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566352150534206,"score_gpt":0.2649440832402891,"score_spread":0.239280561734947,"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."}}