Overstory and understory relationships in longleaf pine plantations 14 years after thinning and woody control
Bibliographic record
Abstract
To develop silvicultural strategies for restoring longleaf pine ( Pinus palustris Mill.) savannas, mortality and growth of overstory pines and midstory hardwoods and abundance and species richness of herbs were studied for 14 years after pine thinning and nonpine woody control. Pine cover in thinned stands was about half of that in nonthinned stands through year 5, but it lagged by only 8% and 3% in years 9 and 14, respectively, because of vigorous crown responses. Despite a cumulative mortality of 64% of hardwood stems from prescribed fires in years 0, 4, and 9, hardwood basal area in thinned stands (2.1 m2·ha–1) was three times that in nonthinned stands (0.7 m2·ha–1) in year 14. Thinning was associated with 13%–22% more cover and six to eight more species of herbs in years 3–8 but only 6% more cover and two more species in year 14 because of accelerated growth of pine cover and hardwood basal area. However, similar increases in cover and richness of herb species in the woody control treatment were retained through year 14 because it had sustained reductions in hardwood and shrub abundance. Silvicultural strategies that substantially delay encroachment by pines, hardwoods, and shrubs will be those most effective at retaining herb species in longleaf pine savannas, including planting pines at wide spacing, periodic thinning and woody control, and frequent burning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".