Silviculture that sustains: the nexus between silviculture, frequent prescribed fire, and conservation of biodiversity in longleaf pine forests of the southeastern United States
Bibliographic record
Abstract
The longleaf pine (Pinus palustris Mill.) forest ecosystems of the US southeastern Coastal Plain, among the most biologically diverse ecosystems in North America, originally covered over 24 × 10 6 ha but now occupy less than 5% of their original extent. The key factor for sustaining their high levels of diversity is the frequent application of prescribed fire uninterrupted in time and space. Pine fuels, critical to application of fire and regulated by canopy distribution, provide the nexus between silviculture and fire management in this system. Typical silvicultural approaches for this type were, in large part, developed to maximize the establishment and growth of regeneration as well as growth and yield of timber, with much less regard to how those practices might influence the ability to sustain prescribed burning regimes or the associated biodiversity. However, many landholdings in the region now include conservation of biodiversity as a primary objective with sustained timber yield as an important but secondary goal. This review synthesizes the literature related to controls of biodiversity for longleaf pine ecosystems, and silvicultural approaches are compared in their ability to sustain natural disturbance such as fire and how closely they mimic the variation, patterns, and processes of natural disturbance regimes while allowing for regeneration.
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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 | 0.001 | 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".