Impact of Land Use Planning and Urbanisation on the Urban Heat Island Phenomena, Case Study of Salalah, Sultanate of Oman
Bibliographic record
Abstract
Cities across much of the world are experiencing a gradual increase in urban air temperature. A significant possible cause is an extreme reduction in urban greenery areas. Thus, land use planning is crucial in determining the quality of the outdoor environment. This paper investigates the UHI, or Urban Heat Island phenomenon, and the land-use impact in Salalah, Sultanate of Oman. To this end, UHI has been outlined in this paper with its thermodynamic characteristics and nature. Many studies investigated the type of land-use impact on UHI in urban regions. Previous studies are reviewed in this paper to determine how land use, particularly in urban areas, affects ambient temperature. Overall, the findings of the reviewed studies show that land use could influence urban temperature. Accordingly, a high-temperature difference was found between rural and urban areas, which emphasized the effect of green areas on ambient temperature. This in turn showed evidence of the effectiveness of natural areas, such as the existence of water bodies and green foliage or plants, in reducing the UHI intensity and its spread. Accordingly, urban planning processes should be more effectively considered in the future as Urban Heat Island (UHI) can be mitigated mainly by means of more rationally appropriate land use planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".