Beyond oak regeneration: modelling mesophytic sapling density drivers along topographic, edaphic, and stand-structural gradients in mature oak-dominated forests of Appalachian Ohio
Bibliographic record
Abstract
Shade-tolerant mesophytic tree species tend to dominate the understories of present-day oak–hickory and mixed-hardwood forests in the eastern United States. We quantified the sapling density associations with abiotic and biotic variables for three important mesophytic species: red maple (Acer rubrum L.), sugar maple (Acer saccharum Marsh.), and American beech (Fagus grandifolia Ehrh.) in southeastern Ohio. In this study, we sampled 165 permanent plots in oak-dominated, topographically diverse, mature (>90 years old), second-growth forests following a time span of 21–25 years (1993–1995 and 2016–2018) between samples on the Athens and Marietta units of the Wayne National Forest. Our models showed that sugar maple was strongly associated with high pH soils and red maple was strongly associated with low pH soils. Additionally, red maple was associated with upper slope positions and older stands, while American beech was associated with lower slopes, northeasterly aspects, and northeast-facing upper slopes. Basal area of competing species, solum depth, and management unit were not significantly related to sapling density for our focal species. American beech sapling density doubled between the two sampling periods, while densities of both maple species declined by half. Our results will help scientists and managers by providing insight into potential future composition of currently oak-dominated forests in areas without active management intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".