Fifty years of partial harvesting in a mixed mesophytic forest: composition and productivity
Bibliographic record
Abstract
Long-term silvicultural trials contribute to sustainable forest management by providing a better scientific understanding of how forest ecosystems respond to periodic timber harvesting. In this study, species composition, diversity, and net periodic growth of tree species in a mixed mesophytic forest in the central Appalachians were evaluated after about a half century of management. Three partial cutting practices on 18 research compartments and on 3 unmanaged reference compartments were evaluated (19512001) on 280 ha. Single-tree selection, diameter-limit harvesting, and timber harvesting in 0.162-ha patches were assessed on three northern red oak site index50(SI) classes: 24, 21, and 18. ShannonWeiner's diversity index (H′) declined from the first (19511959) to last (19872001) measurements and was related to both SI (P = 0.004) and treatment (P = 0.009). Sugar maple (Acer saccharum Marsh.) and red maple (Acer rubrum L.) were the two most abundant species in recent years (19872001); in contrast, in initial inventories (19511959), northern red oak (Quercus rubra L.) and chestnut oak (Quercus prinus L.) were most abundant. Net periodic annual increment (PAI) of merchantable trees (DBH ≥12.7 cm) was related to both SI (P = 0.004) and treatment (P = 0.003). Mean PAI ranged from 4.6 m3·ha1·year1for single-tree selection to 2.5 m3·ha1·year1for unmanaged reference areas across all SI classes. The decline of oak species suggests that only intensive and specific forest management focused on maintaining oak species can obtain historical levels of diversity.
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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".