CHANGES IN BEST MANAGEMENT PRACTICES MONITORING AND ENFORCEMENT AND THEIR IMPACT ON FORESTRY IN WEST VIRGINIA
Bibliographic record
Abstract
Changes in Best Management Practices Monitoring and Enforcement and their Impact on Forestry in West Virginia William McCormickForestry Best Management Practices are a compilation of environmental regulations established to help mitigate and offset water sedimentation and soil erosion.This study examines severe layoffs in the West Virginia Division of Forestry and the resulting cessation in BMP compliance inspections by the State of West Virginia.This study found a significant decline in notifications to the state from loggers about impending timber harvests, a decline of 14.5 percent from the average of the previous three years prior to the curtailment in inspections due to the layoffs, and significant declines in the proper establishment of forestry BMP criteria on notified timber harvests, a decline of 13 percent statewide from previous years in terms of sites passing their overall BMP inspections.Findings on non-notified harvested sites for forestry BMP compliance proved inconclusive.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".