{"id":"W4411404064","doi":"10.1016/j.engappai.2025.111359","title":"Intelligent ensemble architecture for capturing transient nitrogen oxides emission spikes in real-world driving conditions","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Environment and Climate Change Canada; Emissions Reduction Alberta","keywords":"Computer science; Architecture; Transient (computer programming); Nitrogen; Artificial intelligence; Programming language; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003231346,0.0001531649,0.0001859851,0.0002339643,0.0001357627,0.00002266253,0.000262247,0.00006030912,0.00002803362],"category_scores_gemma":[0.0001344863,0.0001664932,0.00008747279,0.0007376267,0.00008517199,0.00006336766,0.00006744057,0.0001714184,0.00001047144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001670795,"about_ca_system_score_gemma":0.00001682295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004473941,"about_ca_topic_score_gemma":0.0003184321,"domain_scores_codex":[0.9987166,0.00001618127,0.0005370808,0.0003181731,0.0001389325,0.0002730592],"domain_scores_gemma":[0.9991601,0.0003951624,0.00008248215,0.0002741108,0.00002254904,0.00006561534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001560039,0.000147892,0.005768461,0.0001172859,0.00001176059,5.147753e-7,0.001131304,0.7072809,0.1705948,0.01355964,0.00005252781,0.1013193],"study_design_scores_gemma":[0.00003680194,0.00003253095,0.002274182,0.0003320275,0.00002751982,0.000001186184,0.0006730892,0.08931886,0.8800679,0.02329092,0.00364897,0.0002959628],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2319289,0.00003666032,0.7666358,0.000167423,0.00008151959,0.0004930388,0.000008469252,0.00007557386,0.0005725422],"genre_scores_gemma":[0.9767326,0.0000152327,0.02266577,0.000008770186,0.00004872425,0.0003673306,0.00001107609,0.00001535174,0.0001351492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7448037,"threshold_uncertainty_score":0.6789396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341292756416135,"score_gpt":0.2971105545879695,"score_spread":0.2736976270238082,"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."}}