Predicting water quality trends resulting from forest cover change in an agriculturally dominated river basin in Eastern Ontario, Canada
Bibliographic record
Abstract
Abstract Agricultural extensification and forest cover loss can significantly impact aquatic ecosystems. This study considered the conversion of forests to agriculture (and vice versa) in an agriculturally dominated watershed in Eastern Ontario, Canada. A series of de- and reforestation scenarios were developed, and water quantity/quality simulations were executed using the Soil and Water Assessment Tool (SWAT) using 30 years of real-world weather observations. Results indicated that streamflow and sediment loads were not sensitive to forest loss, while continuing the recent rate of deforestation of 0.8% (0.2% of the watershed area) per year would, by 2032, increase annual loads of nitrate by 5.6%, total nitrogen by 1.5%, and total phosphorus by 6.8%. Additionally, the same land-use scenarios were simulated with the inclusion of vegetated filter strips (VFS) and grassed waterways. Some reforestation scenarios were sufficient to reduce total nitrogen concentrations below water quality guidelines, particularly under the combined effect of VFSs along all river reaches. However, meeting water quality guidelines for total phosphorus concentrations requires additional management practices beyond those simulated here.
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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 | 0.007 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".