Underplanting in slash reduces first-year browse damage in a central hardwood forest
Bibliographic record
Abstract
Ungulate herbivory damage presents challenges to forest management in many regions and often requires mitigation to meet objectives. Under certain conditions, logging slash can exclude ungulates and provide refuge for tree seedlings. We underplanted clusters of northern red oak ( Quercus rubra L.) and white oak ( Quercus alba L.) seedlings within slash and in adjacent openings and monitored herbivory damage and seedling growth during the first growing season following an operational cut-to-length partial timber harvest in Kentucky, USA. By planting in slash, the odds of white-tailed deer ( Odocoileus virginianus) browse damage decreased by a factor of 12. Oaks were 3 ± 1 cm taller in slash plantings than in non-slash plantings. Slash height and openness best predicted probability of deer browse damage, with tall, dense slash affording the greatest protection. Planting in operationally created slash provided at least short-term advantages to seedling growth potential in areas with high deer density. Further work is needed to determine slash decay rates and the concomitant changes in exclusion efficacy after year 1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".