Potential for mitigating atmospheric ammonia in Canada
Bibliographic record
Abstract
Abstract Most ammonia ( NH 3 ) emissions (85%) in Canada come from agricultural sources (400 kt/yr). There are international conventions that require countries to mitigate NH 3 emissions but there are no federal or provincial guidelines in Canada stipulating emission targets or best practices for agriculture. This study examines the potential for mitigating atmospheric NH 3 using a range of approaches. Taking current farm practices into account, employing proven low‐cost measures (low‐emission slurry application and slurry storage covers) would reduce annual emissions from livestock operations by 16 kt NH 3 ‐N, while using all available low‐cost measures would reduce emissions by 79 kt NH 3 ‐N or 26% of livestock emissions. Another 36 kt/yr could be avoided by improving fertilizer practices, so that the total potential reduction would be about 29% of all agricultural emissions. Emissions from beef cattle and pig production could be reduced by 18% if consumption was cut by 50%, with greater mitigation if production for export was reduced, although the economic and social consequences need to be considered. Mitigation practices must be viewed in the context of possible pollution swapping especially in surplus nitrogen situations. Emissions must also be considered in terms of atmospheric NH 3 transport to and from the USA , therefore bi‐national agreements to jointly reduce emissions might be needed. It may be more cost‐effective in Canada to strategically reduce emissions to minimize risks to health (from particulate matter) and the environment rather than to reduce annual national emission targets.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".