The Role of Vegetation in Controlling Air Temperature Resulting from Urban Heat Island
Bibliographic record
Abstract
Urban Heat Island (UHI) is a phenomenon exhibited by many worldwide cities. Cities, which exhibit UHI, possess higher air temperature as compared with air temperature in the surrounding areas. However, existing UHI profiles are those occurring in subtropical areas which are, of course, very much different from those in tropical cities. Therefore, the objectives of this study are to describe the UHI’s profile and the role of tree vegetation in controlling and reducing air temperature in a tropical region’s urban areas and, particularly, in DKI Jakarta. In this study, we carried out a spatial analysis of land cover and the distribution of air temperature. In this regard, we based our analysis of the potency of tree vegetation in reducing air temperature in UHI’s profile on the distribution of air temperature in various types of land cover which extended from north to south and from east to west. The ranges of air temperature in land cover in the form of built-up areas were 29.2-39.5 ⁰C, non-tree vegetation 28.6-35.6 ⁰C, and tree vegetation 27.0-35.7 ⁰C. Accordingly, tree vegetation has the highest potential to reduce air temperature and to overcome the phenomenon of UHI.
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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 | 0.000 | 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 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".