Understanding the key characteristics and challenges of pine barrens restoration: insights from a Delphi survey of forest land managers and researchers
Bibliographic record
Abstract
Pine barrens are open‐canopy ecological communities once prevalent on sandy soils across the northern Great Lakes Region of the United States and Canada, though fire suppression and plantation forestry have now reduced them to a few isolated areas. Efforts to restore pine barrens are underway on some public lands, but lack of knowledge on the social and ecological issues and challenges that affect these projects impedes fuller progress. As a precursor to designing a public preference survey for pine barrens restoration, we sought input from those with expert knowledge about pine barrens. Using a three‐round modified Delphi survey, forest land managers and researchers identified the key characteristics of pine barrens and important current and future management challenges. Key characteristics were related to fire, landscape structure, plant and animal species, soils, and social themes. Current and future challenges were related to landscape, invasive species, social, economic, climate change, and science themes. Four social issues (education, fire acceptance, fire risk, aesthetics) were rated among the top current challenges but none of them maintained prominence as future challenges. Potential explanations for this shift are that the experts felt these social concerns would be resolved in time or that other issues, such as development pressures and budgets for carrying out restoration, would become greater future challenges. Our approach can be used by managers and researchers to better understand the ecosystems they seek to restore and to communicate with public stakeholders about restoration efforts.
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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".