Edge influence on composition and structure of a <i>Pinus palustris</i> woodland following catastrophic wind disturbance
Bibliographic record
Abstract
Forest edges are an important legacy of natural and anthropogenic disturbances. Edges of intact forest fragments are influenced by adjacent non-forested ecosystems, resulting in compositional and structural differences at the edge and into the intact forest. Edge influence (EI) is the altered biotic and abiotic interactions that occur along the edge-to-interior gradient in disturbed forests. Few studies have analyzed natural disturbance created edges, particularly in woodland structures, which contain fewer trees per hectare and are typically less light-limited than forests. The goal of our study was to examine the EI of a tornado-created edge in a Pinus palustris Mill. (longleaf pine) woodland in Alabama. In 2011, an EF-3 tornado impacted a restored P. palustris woodland, resulting in a distinct edge. We installed transects perpendicular to the edge to quantify biotic and abiotic response variables and calculate the distance of EI. Reduced structural forest complexity and basal area (negative EI) were evident 70 m into the interior woodland. Ground flora richness and diversity experienced a positive EI, with higher richness and diversity at the edge. Results of this study add to our understanding of EI on woodland composition and structure and naturally created edges and may help guide natural disturbance based silvicultural systems.
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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".