Identification and Prioritization of Criteria Used for Selecting Protected Areas in Forest Ecosystems Case Study: Iran's Hyrcanian Forests
Bibliographic record
Abstract
In this study, first of all 5 core criteria and 26 sub-criteria to select protected patches in Iran's natural ecosystems were derived. During the next step to elect appropriate criteria and sub-criteria to chose protected patches in Hyrcanian forest areas, these criteria being organized in a Delphi Questionnaire were presented to protection experts and finally the results extracted from Criteria-importance diagram based on two parameters of importance percentage and importance rank of under study criteria showed that there are 5 core criteria including habitat, species, social aspects, economical aspects, and managerial aspects and 18 sub-criteria including uniqueness of habitat, vulnerability of habitat, representativeness of habitat, importance of habitat, landscape, water resources, species diversity, species damage, species population, species protection-degree, compatibility, historical and cultural importance, importance in national economy, dependence for local economy, legal support, threat factors, research and monitoring and training respecting importance to be used to elect protected patches in Caspian forest ecosystems in Iran.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".