Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives
Bibliographic record
Abstract
Abstract The increasing frequency and intensity of forest fires, driven by climate change and human activities, pose a significant threat to vital forest ecosystems, particularly where fire is not a natural element in the regeneration cycle. This study aims to identify the indicators influencing forest fire vulnerability and compare maps of forest fire susceptibility that are based on the Intergovernmental Panel on Climate Change tripartite model, with a focus on the vulnerable Hyrcanian forest region in Golestan Province, northern Iran, where forest fires have caused considerable economic losses. On the basis of expert opinions and a literature review, we used geographic information systems, remote sensing and machine learning techniques to select and weigh 30 biophysical, environmental and socioeconomic indicators that affect forest fire vulnerability in the study area. These indicators were rigorously normalized, weighted and amalgamated into a comprehensive forest fire vulnerability index to analyze forest exposure, sensitivity and adaptive capacity. We thus identified and mapped areas with very high forest fire exposure, high sensitivity and low adaptive capacity for urgent targeted intervention and strategic planning to mitigate the impacts of forest fires. The results also revealed a set of critical indicators that contribute more significantly to forest fire vulnerability (e.g., precipitation, elevation and factors related to biodiversity, human activity and economic reliance on forest resources). Our results provide insights that can inform policy-making, community engagement and environmental management strategies to mitigate the vulnerabilities associated with forest fires in the Hyrcanian forest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".