Landowner Acceptability of Silvicultural Treatments to Restore an Open Forest Landscape
Bibliographic record
Abstract
This study examined the acceptability of different silvicultural treatments to restore pine barrens, an open, fire-dependent forest landscape type globally imperiled across the northern Great Lakes region of the United States and Canada. In an online survey, we asked family-forest owners (N = 466) in Northeastern Wisconsin about the acceptability of pine barrens restoration treatments through ratings of both verbal descriptions and visual scenarios. An informational statement about pine barrens restoration purposes and goals preceded ratings for half the sample. Across the entire sample, acceptability ratings for eleven verbally-described treatments generally declined as treatments became more intensive, creating greater openness on the landscape. Information recipients found two groups of treatments identified by factor analysis (selective openings, fire) more acceptable than non-recipients, and cluster analysis identified four respondent subgroups, each with varying levels of acceptability. The respondents also rated the acceptability of visual scenarios, with treatment attribute combinations portraying a range of likely restoration alternatives. While we generally found correspondence between verbal and visual acceptability ratings across the entire sample, the groups distinguished by their verbal acceptability ratings did not substantially differ in how they rated the acceptability of the visual scenarios. Implications are discussed for designing and communicating the purpose and value of restoration treatments to stakeholder groups.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".