Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it