Seed-bank characteristics in mixed-mesophytic forest clearcuts and edges: Does "edge effect" extend to the seed bank?
Bibliographic record
Abstract
Little is known about the seed banks of mixed-mesophytic forest clearcuts or their associated forest edges. Seed banks were described and compared to better understand how seed density, species richness, and composition change with increasing distance from clearcuts. Thirty-two taxa were found in the seed bank of clearcuts, and 44 were found in adjacent forest edges. Annuals represented 41% of seeds in clearcuts, but only 8% in edges, while trees and shrubs represented 3% in both areas. Seed-bank density and species richness varied significantly within and between clearcuts, but clearcuts were no different in seed-bank density and richness from interior forest seed banks. Seed-bank density declined significantly with distance from clearcuts on west-facing forest edges, but demonstrated no discernable spatial pattern on south-facing edges. Overall, edge effects from clearcutting on adjacent forest seed banks were demonstrated in total seed density and in several common wind-dispersed, early-successional herbaceous species, including Erechtites hieraciifolia (L.) Raf. and Lobelia inflata L., and Vitis, a common ingestion-dispersed species. The seed-shadow edge effect may influence both current and future community characteristics and population dynamics of vegetation in mixed-mesophytic forest edges adjacent to clearcuts.Key words: seed banks, clearcutting, edge effects, mixed-mesophytic forest, West Virginia.
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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".