{"id":"W4415363631","doi":"10.1002/cjce.70117","title":"A novel <scp>VMD</scp> ‐ <scp>LassoNet</scp> ‐ <scp>iTransformer</scp> framework with enhanced feature fusion for dynamic <scp>NOx</scp> forecasting in flexible utility boilers","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Boiler (water heating); Artificial neural network; Feature selection; Thresholding; Transformer; NOx; Feature extraction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009456303,0.0009942382,0.001193486,0.001019488,0.0002973505,0.0002510943,0.001281185,0.0008814083,0.000005801498],"category_scores_gemma":[0.00750422,0.0008972601,0.0004389992,0.001870945,0.0002182285,0.0006522298,0.00009815017,0.003362676,0.000004004686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001436973,"about_ca_system_score_gemma":0.000720133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002542569,"about_ca_topic_score_gemma":0.0008517285,"domain_scores_codex":[0.9953444,0.00003977282,0.00123028,0.0006377181,0.0006460119,0.002101796],"domain_scores_gemma":[0.9935123,0.003952119,0.0003716116,0.0007093412,0.0004243749,0.001030229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002010904,0.0001082396,0.0003915876,0.001690262,0.0005130359,0.0001474929,0.004755868,0.2238287,0.7485172,0.0005388582,0.01267622,0.006812507],"study_design_scores_gemma":[0.002430289,0.0002198626,0.0006983285,0.004013212,0.0002464425,0.0003150768,0.001399126,0.2398885,0.7229812,0.002427118,0.02515811,0.0002227277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.520654,0.002164042,0.4728386,0.0001321042,0.0006454488,0.0009253521,0.0001242298,0.0004209206,0.002095251],"genre_scores_gemma":[0.9264519,0.0001242745,0.0715793,0.0002224133,0.0003170382,0.0001505926,0.00004322662,0.0002903588,0.00082091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4057979,"threshold_uncertainty_score":0.9993478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00827347252726865,"score_gpt":0.2183202495009421,"score_spread":0.2100467769736735,"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."}}