Aspect influence on productivity when applying the deferment harvest method in mixed-oak hardwood forests in West Virginia
Bibliographic record
Abstract
The deferment harvest method is a new forest management treatment in central Appalachian hardwood forests. It is intended to primarily improve aesthetics by leaving select residual trees in the forest stand beyond the establishment of the regeneration cohort. However, there are concerns with residual tree quality due to the development of epicormic branches and if the presence of forest canopy influences the species composition and development of the regeneration. Topographic aspect can influence differences in productivity in both the residual and regeneration cohorts. This study examined if residual tree quality for timber value and a desirable species composition of the regeneration cohort differed by aspect (i.e., south and east). Epicormic branches were present on majority of the residual trees but did not reduce the quality nor the presumed lumber value of these trees. Forest canopy had no effect on the species composition and development of the regeneration cohort, while there were differences between the south and east aspects in species diversity and stem density of the mid-tolerant species. The regeneration cohort was dominated primarily by commercial species with both shade-intolerant and shade-tolerant species present. These results suggested that maintaining timber value of residual trees and regenerating commercial tree species is possible with the deferment harvest method.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".