Reversing legacy effects in the understory of an oak-dominated forest
Bibliographic record
Abstract
Current forests developed under conditions different from original forests, with higher deer densities, reduced fire frequency, denser canopies, and smaller canopy gaps. These alterations have led to understories dominated by species simultaneously browse tolerant, shade tolerant, and fire sensitive leading to difficulties in the regeneration of oak species (Quercus spp.) in some areas. We evaluated how three key processes — understory fire, canopy gaps, and browsing — influenced tree species in east central West Virginia. We were particularly interested in the response of oak species because they are the dominant overstory species and of maple species (Acer spp.), black birch (Betula lenta L.), and yellow-poplar (Liriodendron tulipifera L.) as these are likely to replace the current forest. Single-process effects were significant and significant interactions among processes were numerous. In general, fire caused significant reductions of seedlings and saplings of red maple (Acer rubrum L.) and striped maple (Acer pensylvanicum L.) and increased the seedlings of black birch and yellow-poplar. Canopy gaps increased the abundance of black birch and yellow-poplar seedlings and saplings. Gaps and fire together caused an increase in the relative abundance of yellow-poplar. Excluding browsers and creating canopy gaps together nearly doubled oak sapling importance values versus either treatment alone; however, oak importance values remained low. Given the significant interactions of browse control with the other two processes, browse control should be considered when planning oak regeneration treatments such as canopy gaps or prescribed fire.
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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) | 0.000 | 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".