Spatial Conservation Prioritization of Persian Squirrel Based on Habitat Suitability and Climate-Induced Forest Mortality Risk
Bibliographic record
Abstract
The Zagros forests in Iran are currently experiencing an exacerbation of climate-induced mortality, placing the Persian squirrel, a keystone species reliant on these ecosystems, in jeopardy. Addressing this imminent threat, our research employed a spatial prioritization methodology, integrating assessments of habitat suitability and mortality risk. Utilizing a weighted ensemble approach, incorporating the strengths of diverse models and expert rules, we discerned that approximately 62% of surveyed forests are at risk, with 7% classified as high risk and 17% as very high risk. Notably, 83% of the forests exhibited varying degrees of habitat suitability, with 11% and 12% demonstrating high and very high suitability, respectively. Employing a conservation prioritization framework, we systematically categorized habitats into priority classes, with 8%, 17%, 29%, and 46% assigned to very high, high, moderate, and low conservation priority classes, respectively. Significantly, areas classified as very high priority demand immediate restoration efforts due to ongoing mortality, while other priority classes underscore the importance of protection and prevention in unaffected habitats. Acknowledging the irreversible nature of current climatic conditions in the Zagros forests, our strategic emphasis aligns with conservation triage principles, prioritizing the preservation of intact habitats yet to succumb to irreversible mortality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".