Environmental and Economic Evaluation of Cow-Calf Wintering Site Runoff Control as a Beneficial Management Practice to Improve Surface Water Quality
Bibliographic record
Abstract
Abstract A runoff diversion-collection system was evaluated as a beneficial management practice (BMP) for minimizing the impact of wintering site runoff on the water quality of an adjacent reach in the headwaters sub-basin of the Haynes Creek Watershed in central Alberta. Significant (α = 0.05) post-BMP mean annual load reductions of 13, 47, and 17% for dissolved, particulate, and total phosphorus, respectively, were realized. Mean annual loads of nitrate nitrogen and total Kjeldahl nitrogen were also reduced by 83 and 22%, respectively. The sum of total loads of Kjeldahl nitrogen and nitrate-plus-nitrite nitrogen, a surrogate measure of total nitrogen, also showed a reduction of 29%. However, mean annual load of total residue increased by 15%. Despite significance at the wintering site, these water quality changes were not statistically detected at the outlet of the subbasin. In spite of the measureable improvement in downstream water quality, adoption of the studied BMP represented a net economic cost to the producer under the current management conditions at the site. Furthermore, there appeared a need for concurrent implementation of BMPs in the headwaters sub-basin to be able to register any detectable changes in water quality at its outlet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".