{"id":"W4416403088","doi":"10.1002/cjce.70177","title":"Dynamic <scp>NOx</scp> emission prediction for a <scp>660 MW</scp> super‐critical coal‐fired boiler using integrated time series network with adaptive filtering and deep learning","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Jiangsu University of Science and Technology; Jiangsu University","keywords":"Deep learning; NOx; Time series; Boiler (water heating); Convolutional neural network; Generalization; Artificial neural network; Combustion","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005261055,0.0001977727,0.0002510606,0.00006837759,0.0003324178,0.000100927,0.000172677,0.0001233704,0.000007202581],"category_scores_gemma":[0.001126797,0.0001564214,0.00006053592,0.0002518509,0.0001930465,0.0002481176,0.0000504913,0.0005642765,9.197e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004891263,"about_ca_system_score_gemma":0.00009484109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004235166,"about_ca_topic_score_gemma":0.00007172708,"domain_scores_codex":[0.9987791,0.00004480207,0.0003182041,0.000181076,0.0001792148,0.0004975795],"domain_scores_gemma":[0.9988214,0.0005922797,0.00008852106,0.00009343874,0.00004675989,0.0003575909],"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":[0.00003386911,0.00001357165,0.008859564,0.0001114673,0.0001448404,0.00004072832,0.002487054,0.7888725,0.1959029,0.00005584625,0.0003983797,0.003079298],"study_design_scores_gemma":[0.0003667582,0.0001581628,0.001478225,0.0009808386,0.0001045529,0.0002307699,0.0005948374,0.979062,0.01479805,0.0001326058,0.002012772,0.00008046535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547194,0.0002898684,0.04441877,0.0001000248,0.0001997177,0.0001167255,0.000008056874,0.00003381184,0.0001136252],"genre_scores_gemma":[0.9889365,0.000004040861,0.01062903,0.00001903173,0.0001475881,0.000005478025,0.000004945832,0.00003035781,0.0002230314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1901895,"threshold_uncertainty_score":0.6378679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007818526707619528,"score_gpt":0.2008349311123445,"score_spread":0.1930164044047249,"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."}}