Potential Changes of Annual-Averaged Nutrient Export in the South Saskatchewan River Basin under Climate and Land-Use Change Scenarios
Bibliographic record
Abstract
Climate and land-use changes modify the physical functioning of river basins and, in particular, influence the transport of nutrients from land to water. In large-scale basins, where a variety of climates, topographies, soil types and land uses co-exist to form a highly heterogeneous environment, a more complex nutrient dynamic is imposed by climate and land-use changes. This is the case of the South Saskatchewan River (SSR) that, along with the North Saskatchewan River, forms one of the largest river systems in western Canada. The SPAtially Referenced Regression On Watershed (SPARROW) model is therefore implemented to assess water quality in the basin, in order to describe spatial and temporal patterns and identify those factors and processes that affect water quality. Forty-five climate and land-use change scenarios comprehended by five General Circulation Models (GCMs) and three Representative Concentration Pathways (RCPs) were incorporated into the model to explain how total nitrogen (TN) and total phosphorus (TP) export could vary across the basin in 30, 60 and 90 years from now. According to model results, annual averages of TN and TP export in the SSR are going to increase in the range 0.9–1.28 kg km − 2 year − 1 and 0.12–0.17 kg km − 2 year − 1 , respectively, by the end of the century, due to climate and land-use changes. Higher increases of TP compared to TN are expected since TP and TN are going to increase ∼36% and ∼21%, respectively, by the end of the century. This research will support management plans in order to mitigate nutrient export under future changes of climate and land use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".