The formative evaluation of a forestry Best Management Practices program in a municipal watershed
Bibliographic record
Abstract
Best Management Practices for water quality (BMPs) have been proven effective in reducing sedimentation from timber harvesting operations. Although most states in the country have BMP guidelines, many are non-regulatory, creating challenges for forest managers to ensure implementation. In surface watershed systems, BMP cost-sharing extension programs (BMP programs) are designed to encourage the implementation of BMPs. To assess the efficacy of a BMP program we examined the rates of BMP implementation on 45 properties harvested between 2013 and 2015: 22 harvests enrolled in a BMP program and 23 harvests not enrolled. We also compared our results to two previous studies completed in 2002 and 2011. Our results indicate BMP implementation was significantly better on properties participating in the BMP program. Also, BMP implementation scores improved for almost all categories evaluated when compared to the two previous studies. One BMP category with low implementation scores (even in 2018), was water diversion devices such as water bars. We suspect the BMP program is not sufficient to incentivize implementation given the time commitment for BMP implementation. Another factor at play here is that implementation may have been perceived as adequate to manage surface flow, but not optimal according to specifications dictated by the BMP field guide.
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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 | 0.055 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".