Public and forest landowner attitudes towards longleaf pine ecosystem restoration using prescribed fire
Bibliographic record
Abstract
Once dominant across the United States (US) Southeastern Coastal Plain, the longleaf pine (Pinus palustris Mill.) ecosystem covers a fraction of its historic geographic range. Restoration efforts have largely occurred on public lands, while most private forests feature alternative pine species. A better understanding of public interest in ecological restoration is critical to sustained efforts and successes. This research examines both forest landowner and general public interest in longleaf pine restoration. Results contribute to research on the social dimensions of ecological restoration, much of which has focused on small-scale projects rather than landscape-scale initiatives. In addition, this study addresses the lack of knowledge regarding factors driving attitudes towards ecological restoration other than demographic and psychometric variables. We employed a telephone survey of 2700 participants across eight states in the southeastern US in the historical range of longleaf pine. A majority of respondents supported restoration as a general goal and were supportive of the use of prescribed fire as a restoration practice. Place attachment, knowledge about longleaf pine, and age were among the significant predictors of restoration support. Findings have implications for future research focusing on sociocultural influences of restoration projects, as well as expanded public support for restoration of fire-maintained ecosystems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".