Can uneven-aged management improve the economic performance of longleaf pine?
Bibliographic record
Abstract
Longleaf pine (Pinus palustris Mill.) is a keystone tree species in the Coastal Plain of the southern United States. To reverse habitat loss and restore critically important forest ecosystem services in this region dominated by private landownership, longleaf pine’s economic performance must be addressed. Uneven-aged forest management has been suggested as a viable alternative for longleaf pine, but evidence of its economic performance under uneven-aged versus even-aged management is lacking. Here, we compare the economic viability of three competing longleaf pine management scenarios — thinned even-aged, unthinned even-aged (conservation and non-conservation land objectives), and uneven-aged — considering timber and nontimber benefits. We find that managing existing uneven-aged longleaf pine forests with a 10-year cutting cycle is economically preferred to even-aged management for land conservation ($1643.9·ha−1 versus $1548.8 to $1641.6·ha−1). However, these estimates exclude costs associated with switching to uneven-aged management ($174.3 to $694.9·ha−1), which are considerable. Annual subsidies of between $5 and $22·ha−1 for 50 years would be required to offset costs of conversion to uneven-aged management. For establishment of new longleaf pine stands, an uneven-aged scenario would be the economically preferred management approach, providing higher economic gains ($176.9·ha−1) than unthinned, high-density even-aged management when the primary objective is timber production.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".