{"id":"W2996626254","doi":"10.1002/acs.3074","title":"Distributed monitoring of the absorption column of a post‐combustion CO<sub>2</sub> capture plant","year":2019,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Killam Trusts","keywords":"Estimator; Column (typography); Combustion; Fault (geology); State (computer science); Work (physics); Absorption (acoustics); Computer science; Distributed computing; Environmental science; Engineering; Chemistry; Mathematics; Materials science; Algorithm; Geology; Telecommunications; Mechanical engineering; Statistics","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":[0.0001998261,0.0001157747,0.0002693101,0.0001081473,0.00003071692,0.00003050196,0.0001673342,0.0000678271,0.000002503358],"category_scores_gemma":[0.00004144691,0.000089204,0.00008657576,0.00008297301,0.00004213861,0.0003948503,0.00001342399,0.0001920454,4.26527e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001008939,"about_ca_system_score_gemma":0.00005432867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005337537,"about_ca_topic_score_gemma":0.000001908296,"domain_scores_codex":[0.9988452,0.00004229244,0.0005173027,0.00008017505,0.0004149441,0.0001000795],"domain_scores_gemma":[0.9982256,0.00008339307,0.0006030908,0.00004682991,0.00100461,0.00003652068],"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":[0.0003899672,0.00002081128,0.00443587,0.00005375061,0.0001637594,0.000003403243,0.0002151341,0.3626375,0.625964,0.0000611339,0.000006464708,0.006048094],"study_design_scores_gemma":[0.007131611,0.0003724693,0.06567806,0.002945965,0.0001863233,0.0002351402,0.001358054,0.7481639,0.1730248,0.0005208571,0.00004506857,0.0003377706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7428848,0.001039988,0.2553462,0.00006419131,0.0003609565,0.0001552194,0.000102275,0.00001087087,0.00003550167],"genre_scores_gemma":[0.9995091,0.00003668597,0.0002610979,0.00001029735,0.000157509,0.000002388703,0.000006548699,0.00001324692,0.000003138021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4529393,"threshold_uncertainty_score":0.3637633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005554083269617777,"score_gpt":0.2027641917515102,"score_spread":0.1972101084818924,"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."}}