{"id":"W1592751551","doi":"","title":"Multiple model based soft sensor development with irregular/missing process output measurement","year":2011,"lang":"en","type":"article","venue":"International Symposium on Advanced Control of Industrial Processes","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Soft sensor; Missing data; Computer science; Process (computing); Expectation–maximization algorithm; Nonlinear system; Maximization; Scheme (mathematics); Data modeling; Algorithm; Data mining; Mathematical optimization; Maximum likelihood; Machine learning; Mathematics; Statistics","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":[0.0008003101,0.000670156,0.0008950274,0.000264947,0.0002573161,0.0007041388,0.001045522,0.0009242944,0.0007329552],"category_scores_gemma":[0.002276844,0.0004481863,0.0006783482,0.0002917372,0.0007243189,0.001799247,0.001185359,0.001110899,0.0002455871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004658927,"about_ca_system_score_gemma":0.0005438837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005584089,"about_ca_topic_score_gemma":0.0006263649,"domain_scores_codex":[0.9993159,0.0001667994,0.00003598312,0.0001468906,0.0002962391,0.00003827071],"domain_scores_gemma":[0.999018,0.0004466574,0.000177388,0.0001721313,0.0001500401,0.00003576295],"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":[0.0002836245,0.0001022903,0.002159563,0.0003052929,0.00007657585,0.0003932543,0.0003046038,0.7873617,0.04989864,0.01650148,0.0004672171,0.1421458],"study_design_scores_gemma":[0.000004131815,0.00004980711,0.0001300608,0.000003423077,0.00000482833,0.00004828161,0.000009362943,0.9884833,0.009665394,0.001283925,0.0003095089,0.00000801136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01333064,0.00004890446,0.9860696,0.00004705617,0.00001325054,0.0000178537,0.000008994765,0.0001173787,0.0003463369],"genre_scores_gemma":[0.7500995,0.0001983132,0.2464899,0.00007272154,0.00002223647,0.0001050307,0.00007535007,0.00004684745,0.002889965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001045522,"threshold_uncertainty_score":0.004232526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03759039958810651,"score_gpt":0.228010185761943,"score_spread":0.1904197861738365,"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."}}