Use of native seed mixtures to improve erosion control and wildlife habitat on log landings following timber harvest in the Upper Elk Watershed of West Virginia
Bibliographic record
Abstract
Foresters in West Virginia follow BMP guidelines by reseeding retired log landings with inexpensive grasses that quickly provide erosion control. However, these grasses typically are not native nor do they provide high quality forage for wildlife. I developed 3 native seed mixtures for log landing reclamation that would maintain sediment control, as well as enhance wildlife habitat. These mixtures included an erosion control mixture, a wildlife mixture, and a wildflower mixture. I assessed sediment control, biomass production, vegetation structure, forage quality, and small mammal usage of my native mixtures and a commonly used, nonnative traditional mixture in 2005 and 2006. No statistical analysis of sediments was conducted among mixtures due to small sample size (n = 6). There were no differences among mixtures in biomass production. The wildlife mixture was highest in crude protein, height and % cover among native seed mixtures. Small mammal relative abundance and species richness did not differ among mixtures. I used compromise programming analysis to find the best seed mixture for reclaiming log landings based on land management objectives. Objectives used for analysis included those of a private landowner interested in hunting, a private landowner interested in aesthetics, a timber company, and a wildlife manager. Among native mixtures, the wildlife mixture was best for all land management objectives. However, the non-native traditional mixture was the best of all 4 seed mixtures analyzed. These results suggest that although nonnative traditional mixtures produce adequate physical structure to control sediment and enhance wildlife habitat, native seed mixtures are capable of serving a similar function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".