A guide to conserve amphibian species in Iran
Bibliographic record
Abstract
Globally, amphibians are one of the most threatened vertebrate groups and are hypersensitive to human-imposed habitat changes. Here we explore ways to conserve amphibians in two of the least-known biodiversity hotspots on earth, the Caucasus and Irano–Anatolian regions. We used two techniques: (ⅰ) combining species richness, endemism, and endangerness indices and (ⅱ) a species distribution model (SDM) to identify high-priority areas for Iran’s seven endemic and/or threatened amphibian species. The identified amphibian high-priority areas were then targeted to assess the levels of protection granted by the network of conservation areas (CAs) in Iran. We also computed the species-specific extent of occurrence (EOO) and the area of occupancy (AOO) to detect conservation gaps for the targeted species. Our results indicate that amphibian high-priority areas in Iran are mostly distributed across the Hyrcanian forest in the north and Zagros Mountains in the west. The gap analysis revealed that based on the most optimistic metric, 40% of amphibian hotspots are covered by CAs in Iran. However, the species-specific gap analysis showed that Iran’s CAs perform poorly at representing the EOO of all of the endemic and/or threatened amphibian species. These results suggest that expansion of CAs in Iran is essential for amphibian conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.045 |
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".