Spatial and Temporal Climate Change Vulnerability Assessment in the West Bank, Palestine
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
Climate change is widely recognized as an inevitable phenomenon, with the Mediterranean region expected to experience some of the most severe impacts. Countries in this region, including Palestine, are already observing significant effects on key sectors such as agriculture, water resources, industry, and health. Consequently, there is a need for multidimensional analyses of vulnerability. This study applied a Climate Change Vulnerability (CCV) index to assess spatial and temporal changes in vulnerability across different governorates in the West Bank, Palestine. Climate change vulnerability maps for the West Bank were developed using Geographic Information System (GIS) tools and Analytical Hierarchy Process (AHP) matrices, incorporating various indicators across categories such as Health, Socio-demographic, Agriculture, Service, Housing, and Economic components. The findings indicate that socio-demographic factors contribute significantly to the West Bank’s overall vulnerability to climate change. Although the overall vulnerability has decreased over time, the developed maps reveal that 76% of the West Bank’s population resides in areas classified as highly vulnerable to climate change impacts. In contrast, 10% of the population lives in areas classified as low to very low in terms of vulnerability, including the governorates of Tubas, Salfit, Qalqiliya, and Jericho and Al-Aghwar. These results are invaluable for policymakers, offering guidance on selecting appropriate mitigation and adaptation measures, particularly in highly vulnerable areas, to reduce the impacts of climate change across the region.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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