{"id":"W4413364312","doi":"10.1002/cjce.70054","title":"Dynamic <scp>NOx</scp> emission prediction in coal‐fired power plants based on joint multi‐head attention <scp>CNN</scp> ‐ <scp>GRU</scp> hybrid model","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Guangdong Key Laboratory of Efficient and Clean Energy Utilization, South China University of Technology","keywords":"NOx; Joint (building); Coal; Power (physics); Head (geology); Computer science; Chemistry; Automotive engineering; Engineering; Waste management; Biology; Physics; Structural engineering; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001849675,0.0005521649,0.0003354259,0.0001969072,0.0001800143,0.0003508143,0.0006102976,0.0004386088,0.0006332799],"category_scores_gemma":[0.0002499715,0.0002006302,0.0004550475,0.0001691098,0.0002080592,0.0003203871,0.0002743749,0.0003899007,0.00009660131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005999543,"about_ca_system_score_gemma":0.0005652975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03498732,"about_ca_topic_score_gemma":0.02556241,"domain_scores_codex":[0.9999461,0.000006606374,0.000002358005,0.00001891795,0.00001193923,0.00001400076],"domain_scores_gemma":[0.9999239,0.00002494934,0.000010659,0.000006893252,0.00002621282,0.000007412156],"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":[0.00003756193,0.00002678426,0.001585969,0.00001206815,0.0000254797,0.00004530312,0.000008037049,0.9863653,0.00304049,0.0002018947,0.0001428466,0.008508337],"study_design_scores_gemma":[5.128848e-7,0.000004119839,0.0001549925,3.355529e-7,0.000001543524,0.000001286132,7.380409e-7,0.9995518,0.0002451069,0.00002837784,0.00001035362,7.484553e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8295239,0.0004137126,0.1630934,0.0002510283,0.00006795,0.00003042592,0.0001864361,0.0007718665,0.005661389],"genre_scores_gemma":[0.9966741,0.00003023695,0.002511342,0.00001200721,0.000003367359,0.000008196605,0.00004076968,0.000005285907,0.0007148174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03498732,"threshold_uncertainty_score":0.06956738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0143191639436758,"score_gpt":0.2179788814141768,"score_spread":0.203659717470501,"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."}}