Agricultural Best Management Practice Abundance and Location does not Influence Stream Ecosystem Function or Water Quality in the Summer Season
Bibliographic record
Abstract
Best management practices (BMPs) are tools commonly used to mitigate negative impacts of agriculture on water quality; however, the relationship between BMPs and aquatic ecological function is unknown. Our research goal was to determine the association between both stream ecosystem metabolism and water quality, and the abundance and location of four different BMPs in agricultural catchments. Dissolved oxygen was measured over a two-week period in mid-June and used to estimate ecosystem metabolism of 13 headwater streams representing a gradient of BMP implementation in Southern Ontario, Canada. Stepwise regression analyses were used to associate stream metabolism and water quality with metrics describing the abundance and location of BMPs within each catchment. Studied streams exhibited rates of metabolism comparable to catchments from other agricultural regions. However, metrics of BMP implementation were not associated with either stream metabolism or water quality. Our results suggest that BMPs in the studied agricultural catchments are not improving water quality or mitigating water quality impacts on stream metabolism during the summer season. We propose that seasonality of catchment hydrology and time lag effects associated with past agricultural land use may be masking the mitigation benefits of BMPs on stream ecosystem conditions during the summer season.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".