Towards Quantifying the Coastal Vulnerability due to Natural Hazards using the InVEST Coastal Vulnerability Model
Bibliographic record
Abstract
Coastal areas and coastal communities are facing threats due to the impacts of climate change. Therefore, assessing their vulnerabilities and the potential for natural habitats to contribute to protecting coastal areas and communities is essential for effective long-term planning, sustainability, and resilient coastal management. This study modeled and mapped coastal vulnerability using the InVEST 3.9.1 model developed by the Natural Capital Project Coastal Vulnerability model to explore the role of natural habitats in mitigating coastal hazards in Southern Al Sharqiya and Al Wusta Governorates of the Sultanate of Oman. The results showed that the highest hazard classification > 2.67 represented 18% of the coastal distribution, the intermediate hazard classification ranging between 2.31 and 2.66 represented 38% of the coastal distribution, and the lowest hazard classification ranging between 1.22 and 2.30) represented 44% of the coastal distribution. These results, however, did not account for the role of natural habitats in coastal protection. In terms of the role of natural habitats in mitigating coastal hazards, the presence of natural habitats reduced the extent of the highest exposed shoreline by 14% and 8% for the highest and intermediate areas, respectively. Under the natural habitat’s scenario, the habitats could provide 59% protection for the coastal communities under the highest exposure category and 41% under the intermediate category. Under a no-habitat scenario, about 75% of the coastal communities are exposed and vulnerable to coastal hazards under the highest hazard exposure category and 25% under the intermediate category. These results demonstrate that it is critical, especially for policymakers, to enhance the protection of coastal ecosystems to achieve coastal resilience. This study buttresses the importance of coastal ecosystem assessments in ensuring coastal resilience and climate change adaptation processes for any coastal countries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".