Scenarios to Investigate the Effect ofWetland Position in a Watershed on Nutrient Loadings
Bibliographic record
Abstract
Lake Winnipeg is Canada’s 6th largest freshwater lake and is subject to an increasing rate of eutrophication as a result of nonpoint source pollution from numerous sources including farms and municipal wastewaters. We examine scenarios designed to compare the relative effects of wetland restoration and position on modelled nutrient loadings to Lake Winnipeg from a pilot watershed in the Lake’s Basin, the La Salle River watershed. Scenarios were examined using the Soil and Water Assessment Tool (SWAT). SWAT is a well-known watershed scale hydrologic model designed to assess non-point source pollution loadings to contributing streams across a wide range of scales. Modelled results suggested that increasing wetland cover to historic levels decreased yearly nutrient loadings by 9-21% for both TN and TP. When 25% of historic wetland areas were restored at subwatershed outlets, equivalent or better nutrient reductions were attained. But placing all wetland area at the watershed outlet did not result in as substantial nutrient reductions. There was a larger range of uncertainty when wetlands were modelled across all subwatersheds than when the entire wetland area was modelled at the outlet. These results may indicate that wetland position is as important as wetland amount in terms of nutrient reductions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".