Assessing the Impact of BMPs on Water Quality and Quantity in a Flat Agricultural Watershed in Southern Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-point source pollution poses a continuous threat to the quality of Great Lakes waters. To abate this problem, the Great Lakes Agricultural Stewardship Initiative (GLASI) was initiated in Ontario, Canada, with the primary aim of reducing phosphorus pollution. Therefore, a case-study analysis of the Wigle Creek watershed, one of the six priority watersheds under the GLASI program, was undertaken to evaluate the effectiveness of various existing and potential future Best Management Practices (BMPs) and to identify BMPs that might aid in mitigating the watershed’s contribution to phosphorus loads reaching Lake Erie. Given the watershed’s very flat topography, hydrological/nutrient modeling proved an extremely challenging exercise. The Soil and Water Assessment Tool (SWAT) model was used in this evaluation. Several digital elevation model (DEM) options were considered to accurately describe the watershed and represent flow conditions. A 30 m resolution DEM, implementing a modified burning in of streams based on ground truthing, was finally employed to develop the SWAT model’s drainage framework. The model was first calibrated for flow, sediment, and phosphorus loads. The calibrated model was used to evaluate the ability of potential BMPs (minimum tillage, no-till, retiring croplands into pasture, retiring croplands into forest, winter wheat cover crop, and vegetative filter strips) to reduce phosphorus loads compared to implemented practice. Converting all croplands into pasture or forest significantly decreased P loads reaching Lake Erie. Comparatively, a winter wheat cover crop had minimal effect on reducing phosphorus loading.
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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 | 0.001 | 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 it