Determination criteria and indicators for sustainable forest management based on the views of experts and local people (Case study: Asalem Forests, north of Iran)
Bibliographic record
Abstract
This paper aimed at identifying and providing the appropriate criteria and indicators for sustainable forest management in northern Iran, and the comparison between the views of local people and experts and priorities of each of these two groups of criteria and indicators. Four processes including Montreal, Helsinki, Near East FAO and CIFOR criteria and indicators were used as basis among the existing international procedures, Through Multi-criteria decision-making methods a series of criteria and indicators for sustainable forest management was selected. Criteria and indicators of the first stage were separately judged by teams of experts and local people. Finally, through the process of network analysis, creating a model and developing the relationships between the criteria and indicators to calculate the final weight and prioritize them were conducted. The results included a series of 11 criteria and 65 indicators. According to experts, the criteria for the conservation of biodiversity and the protective functions of forests had the highest weights (0.1011 and 0.8944, respectively) and criteria for effective local management of conservation and access to resources had the lowest weight (0.07998). According to the local people, the criteria for the socio-economic functions and the maintenance of the productive capacity of forests had the highest weights (0.9501 and 0.9069, respectively) and criteria for conservation of biodiversity had the lowest weight (0.07994). The results indicated that successful management of forests requires joint decision-making between experts and local people, which in turn depends on the relationship and mutual understanding between the two groups.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".