Interactions between reactive nitrogen and the Canadian landscape: a budget approach
Bibliographic record
Abstract
The movement of excess reactive nitrogen (Nr) from anthropogenic activities to natural cosystems has been described as one of the most serious environmental threats facing modern ociety [Rockstrom et al., 2009]. One of the approaches for tracking this movement is the use of budgets that quantify fluxes [Leip et al., 2011a]. We constructed an Nr budget for Canada using measured and modeled values from the scientific literature, government databases, and data from new agri-environmental indicators, in order to produce information for policy makers and scientists to understand the major flows of nitrogen to allow abetter assessment of risks to the Canadian environment. We divided the Canadian territory south of N into areas dominated by natural ecosystems, as well as by agricultural and urban/industrial activities to evaluate Nr flows within, between and out of these units. We show that Canada is a major exporter of Nrdue to the availability of inexpensive commercial fertilizers. The large land area suitable for agriculture makes Canada a significant agricultural Nr exporter of both grain crops and livestock. Finally, Canada exports petroleum Nr mainly to the United States. Because of its location and prevailing atmospheric transport patterns, Canada is a net receptor of Nr air pollution from the United States, receiving approximately 20% of the Nr leaving the US airshed. We found that overall, terrestrial natural ecosystems as well as the atmosphere are in balance between Nr inputs and outputs when all N reactive and non-reactive fluxes are included. However, when only reactive forms are considered, almost 50% of N entering the Canadian atmosphere cannot be accounted for and is assumed to be lost to the Atlantic and Arctic oceans or to unmeasured dry deposition. However, agricultural and freshwater landscapes are showing large differences between measured inputs and outputs of Nras our data suggest that denitrification in soils and aquatic systems is larger than what models predict. Our work also shows that Canada is a major contributor to the global flowof nitrogen through commercial exports.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".