Predicting Sea-Level Rise in Al Hamra Development, Ras Al Khaimah, UAE
Bibliographic record
Abstract
The Al Hamra development in the emirate of Ras Al Khaimah is situated along the south-eastern coast of the Arabian Gulf. The development fronts the Gulf along a 5 km. stretch of sandy beach and, as it includes 5 hotels, numerous villas and condos, represents a significant investment. Such an investment requires long-term strategies to minimize risk from sea level rise. As IPCC reports continue to be updated with new information, predictions of sea level rise have been revised upward. In order to plan for the protection of these, and other developments, accurate information needs to be supplied to those involved in planning adaptation strategies. This paper seeks to quantify and map the potential area subject to inundation up to the year 2099. Using the highest inundation scenario, a GIS map of inundation will be created. Other factors, such as high tides, storm surge and extreme wave events will see water levels increased beyond the predicted sea level scenarios indicating greater risk of flooding. This project will use LiDAR data in a GIS environment to provide the most accurate elevation data. Other layers showing buildings assist in visualizing future vulnerability to sea level rise. This coastline is heavily developed with construction underway for more resort developments. As the risk from sea level rise evolves over a long time period, planners require accurate information that can be easily updated in order to react to revised predictions. This paper represents a pilot project as future research is planned to examine the entire 65km coastline of Ras Al Khaimah
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".