Human perceptions of forest fragmentation: Implications for natural disturbance management
Bibliographic record
Abstract
To test public perception and preference of forest fragmentation trends under current forest management practices, we solicited preferences for harvest patterns from 63 study participants before and after they were provided with educational material on the subject. In addition, we solicited preferences for harvest systems employing different retention patterns. Participants preferred harvest patterns tending away from small, dispersed harvest blocks (i.e., more fragmented) towards larger, more aggregated harvest blocks (i.e., less fragmented). This preference was more pronounced when participants were provided with information that stressed a less fragmented pattern as being ecologically beneficial. This result suggests that the public is willing to accept larger, more aggregated harvest blocks relative to the status quo, especially if provided with information that stresses benefits of that approach. However, participants clearly preferred a harvest system employing dispersed individual tree retention over other systems employing a more concentrated retention pattern. The combination of these results suggests that public acceptability of larger aggregated harvest blocks may depend on the amount of post-harvest retention involved, and that harvest systems employing dispersed individual tree retention will be preferred by the public. Key words: effects of information, environmental perception, forest fragmentation, forest management, human perception, natural disturbance
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".