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Record W7128501863 · doi:10.64903/1480-6800-26.2.210

Impact of Land Use Planning and Urbanisation on the Urban Heat Island Phenomena, Case Study of Salalah, Sultanate of Oman

2023· article· W7128501863 on OpenAlexvenueno aff
Hussam Ashour, Yasser Arab, A. A. Hassan, Peem Nuaklong, Boonsap Witchayangkoon

Bibliographic record

VenueArab world geographer · 2023
Typearticle
Language
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsUrban heat islandUrbanizationUrban planningUrban climateLand useLand-use planningNatural (archaeology)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.263
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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