Conservation management practices: Success story of the Hog Creek and Sturgeon River watersheds, Ontario, Canada
Bibliographic record
Abstract
The soil erosion from agricultural watersheds can be reduced by implementation of conservation management practices. In this study, the effectiveness of most popular agricultural best management practices (BMPs) for reducing sediment loads within Hog Creek and Sturgeon River watersheds in Ontario was investigated using measurement of the shift in the sediment rating curves from pre-BMP (1989 to 1993) to post-BMP (2004 to 2008) implementation periods. The data from the water quality monitoring program for the Hog Creek and the Sturgeon River watersheds over this decade of extensive conservation management program implementation showed significant reductions in the sediment loads of 49% for Hog Creek and 41% for the Sturgeon River. The results showed that the most widely adopted BMPs that greatly influenced the overall removal in sediment loads were stream bank fencing, no-till farming, and vegetative buffer strips. Overall, the outcome of the study recommends these promising practices to protect and improve receiving water quality. The practical novel technique presented in this study for quantification of the overall long-term water quality benefits of conservation management practices can be an integral part of an adaptive strategy for a watershed-scale BMP implementation program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".