Changes in anthropogenic nitrogen and phosphorus inputs to the St. Lawrence sub‐basin over 110 years and impacts on riverine export
Bibliographic record
Abstract
Abstract Human activities have increased the flow of nitrogen (N) and phosphorus (P) over much of the Earth, leading to increased agricultural production, but also the degradation of air, soil, and water quality. Here we quantify the sources of anthropogenic N and P inputs to 76 watersheds of the St. Lawrence Basin (SLB) throughout the 20th century using NANI/NAPI (net anthropogenic N/P input to watersheds), a mass balance modeling approach, and estimate the fraction of these inputs exported to adjacent rivers. Our results show that since 1901, NANI and NAPI increased 4.5‐fold and 3.8‐fold, respectively, with a peak in 1991 mainly due to high atmospheric N deposition and P fertilizer application. However, the relative increase over the course of the last century was much higher in certain watersheds, particularly those where there was greater urbanization. Ranges in NANI and NAPI vary greatly among watersheds (110 to 9351 kg N km −2 yr −1 and 0.16 to 1938 kg P km −2 yr −1 , respectively in 2011) and are strongly related to riverine fluxes ( R 2 = 0.87 and 0.71 for N and P, respectively). Our results suggest that 22% of NANI (ranging from 11% to 68% across watersheds) and 17% of NAPI (ranging from 3% to 173%) are exported to rivers. Predominant sources of inputs vary spatially and through time largely due to changes in farming practices. By tracking the main sources of inputs to specific watersheds and through time, our work provides insights for N and P management. Reduction strategies will likely need to be watershed specific, although through time, our results clearly show the large‐scale impact of targeted legislation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".