Forest aesthetic indicators in sustainable forest management standards
Bibliographic record
Abstract
Sustainable forest management (SFM) standards have been criticized for their lack of aesthetic indicators, which some consider to be an important social component of forestry. To provide a basis for the inclusion of aesthetic indicators in SFM frameworks, we used Delphi techniques to survey the beliefs and opinions of SFM and aesthetic experts. The three major reasons provided for the lack of aesthetic indicators were a lack of aesthetic training among those designing criteria and indicators, a bias against aesthetics, which are often considered to be highly subjective, and the general omission of people with knowledge of aesthetics during the development of SFM standards. Based on the responses, we present 10 possible aesthetic indicators appropriate for international SFM standards, including eight quantitative and two qualitative indicators. We also provide 18 other potential aesthetic indicators, which can be applied at various scales of SFM, ranging from local to national. These results should provide guidance to groups developing and revising criteria and indicators of sustainable forest management at various scales, from local to international.
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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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".