Monthly ammonia emissions from fertilizers in 12 Canadian Ecoregions
Bibliographic record
Abstract
Emissions of ammonia (NH3) from agriculture have been associated with transboundary atmospheric pollutant transport and potential human health problems. Specifically, NH3 gas reacts in the atmosphere to form fine particles (PM2.5) that are subject to long range transport and are considered to be associated with elevated risk of all-cause, lung-cancer and cardiopulmonary mortality. Agriculture is a major source of atmospheric NH3, and, of this, NH3 from fertilizers is perhaps the most easily managed. Recent shifts in nitrogen (N) fertilizer materials and improved placement of urea and related fertilizers have resulted in marked changes in emissions. This paper describes a model developed to predict month-by-month emissions of fertilizer NH3, supported by surveys of farmers and fertilizer industry personnel that update information on fertilizer use. Compared with previous estimates by Environment Canada, the fraction of fertilizer N emitted as NH3 is estimated to be 50% lower in the vast prairie regions (a very large reduction in total NH3), and about 30% lower in eastern Canada. The estimate for 2006 is 1.0 × 108 kg NH3 emitted directly from fertilizer application, 73% of this in the prairie region, and much of this in May. Overall, this indicates 6% of the applied fertilizer N is lost as NH3 gas. Clearly, emission estimates are strongly dependent on up-to-date information about farm practices.Key words: Model, urea, volatilization
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".