Perceptions of uncertainty in forest planning: contrasting forest professionals’ perspectives with the latest research
Bibliographic record
Abstract
Many of the intrinsic facets of forest planning are surrounded by uncertainty. Decision-makers strive to improve their understanding of the sources of uncertainty and their impact on the decision-making process. However, uncertainty is rarely integrated into real-world forestry applications or into decision support tools used in forest planning problems. To identify the needs, interests, and challenges of managing uncertainty in forest planning, we interviewed forestry professionals. All the interviewees indicated the positive potential of a tool that could address some facets of uncertainty. Additionally, we conducted a review of the most recent literature on this topic to understand current hot topics and future trends that could help address real-world challenges. This study highlights the next steps to incorporate uncertainty into the decision support systems for forest planning. However, to strengthen the bond between the practical needs of forestry professionals and the theoretical approaches proposed by recent literature, more effort should be placed on defining terminology and formulating a theoretical framework for uncertainty analysis. This will provide the forestry community with a common language and typology, help increase its general understanding, and improve communication between forestry researchers, forestry professionals, and other stakeholders.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".