Excluding deer browse increases stump sprouting success and height growth following regeneration harvests
Bibliographic record
Abstract
Slash walls are a novel strategy that could help maintain species on sites where ungulate browse limits tree regeneration. We established five slash walls in southern New England, USA to examine the influence of pre-harvest tree metrics and deer exclusion on stump sprout height and survival at 160 sample points ( n = 1509 trees). For all species groups, dominant sprouts were taller inside the walls at the end of the first and second growing seasons. After 2 years, mean sprout heights were ∼2.5 times higher for Quercus rubra (1.8 vs. 0.7 m) and >6 times higher for Acer saccharum (2.0 vs. 0.3 m) inside the walls. For some species, the proportion of stumps with a live sprout after 1 year was higher inside the slash walls (56% vs. 28% for Q. rubra and 77% vs. 55% for Carya ovata). By contrast, sprouting success was uniformly high for Acer rubrum (78%) and Liriodendron tulipifera (87%). Differences in sprout survival inside versus outside the walls increased during the second year for Q. rubra, C. ovata, and A. saccharum. Where maintaining Q. rubra is a management objective, excluding deer will increase both the growth of stump sprouts and the proportion of stumps with a live sprout.
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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 | 0.000 | 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".