Legacy Phosphorus Across Canada: Insights from a 60-Year Dataset
Bibliographic record
Abstract
Human activities over decades of agriculture and urbanization have altered phosphorus (P) cycling, posing a threat to water quality and ecosystem function. Algal blooms have become a pervasive problem in both small and large waterbodies across Canada. Despite concerted efforts to reduce P loading to surface waters, there has yet to be a noticeable improvement in water quality. This can be attributed to the accumulation of legacy P in the landscape as a result of excessive use of synthetic fertilizers and the production of livestock manure. These legacy P can reach the waterbodies decades after implementing P management practices. Therefore, to better understand long-term P dynamics and their drivers, it is crucial to develop long-term datasets of P inputs and outputs. \nWe developed a 60-year (1961–2021), 250-meter grid resolution data of P components and P surplus across Canada. P surplus is the difference between P inputs (fertilizer inputs, livestock manure, detergent, and human waste) and non-hydrological P output (crop uptake). Our result shows the different drivers of P surplus across Canada. In Ontario and Quebec, the P surplus decreased from nutrient regulation programs in 1981 and subsequently rebounded in 2006 due to an increase in P fertilizer use. In prairie provinces, low P inputs and increasing crop yields have led to the mining of the P stores in the soils. This new, longer dataset will improve our understanding of long-term P dynamics and allow for explicit consideration of the impacts of legacy P on environmental outcomes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".