© Author(s) 2009. This work is distributed under the Creative Commons Attribution 3.0 License. Atmospheric Chemistry and Physics Modelling the impacts of ammonia emissions reductions on North American air quality
Bibliographic record
Abstract
Abstract. A unified regional air-quality modelling system (AURAMS) was used to investigate the effects of reduc-tions in ammonia emissions on regional air quality, with a focus on particulate-matter formation. Three simulations of one-year duration were performed for a North American do-main: (1) a base-case simulation using 2002 Canadian and US national emissions inventories augmented by a more de-tailed Canadian emissions inventory for agricultural ammo-nia; (2) a 30 % North-American-wide reduction in agricul-tural ammonia emissions; and (3) a 50 % reduction in Cana-dian beef-cattle ammonia emissions. The simulations show that a 30 % continent-wide reduction in agricultural ammonia emissions lead to reductions in median hourly PM2.5 mass of <1µg m−3 on an annual basis. The atmospheric response to these emission reductions displays marked seasonal vari-
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.494 | 0.407 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".