Effects of woody debris and ferns on herb-layer vegetation and deer herbivory in a Pennsylvania forest blowdown
Bibliographic record
Abstract
Disturbances create a variety of legacy types, which can influence subsequent forest dynamics. One method by which legacies may influence succession is by altering herbivore activities. For example, logging slash has been shown to be effective in preventing browsing by large herbivores. Generalizing from studies after logging, we hypothesized that windthrow slash (woody debris) would limit the ability of deer to reach or locate individual plants. Furthermore, another legacy of disturbances is the establishment of recalcitrant vegetation, such as dense fern layers, which subsequently preclude woody plant establishment. To test for potential influences of these 2 types of legacies, we examined the effects of slash and fern abundance on herb and woody community structure and deer herbivory levels within a Pennsylvania windthrow gap. Slash abundance was negatively correlated with woody diversity, richness, black cherry seedling densities, and total seedling densities, but this effect was probably not a consequence of browsing. Fern abundance was negatively correlated with woody richness, red maple densities, total seedling densities, herb diversity, and richness. Fern effects on the woody community appeared to be related to reductions in light availability. Our results contradict the findings of previous studies that show that slash piles serve as refugia from herbivores. We suggest the inconsistencies may be due to a lack of quantitative measures of slash within those studies.
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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 | 0.001 |
| 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".