The Effect of Rooftop Garden on Reducing the Internal Temperature of the Rooms in Buildings
Bibliographic record
Abstract
With the rapid industrialization and urban expansion pushing cities to build skyscrapers, streets, and homes often at the expense of plant life and vegetation people have forgotten the real reasons for vegetation.For most them, trees and other vegetation serve merely as a luxury or to make cities look prettier.While providing aesthetic value is a well-known fact, the integration of plants and other wildlife can also benefit cities in other ways.It is not until recently that people realized the effect of uprooting plants, one of these effects is the Urban Heat Island Effect.To alleviate its effects, urban planners, in developed countries, have been using rooftop gardens as a way to merge vegetation in urban areas.The purpose of this research is to study the effectiveness of rooftop gardens in reducing the Urban Heat Island Effect in the climate and conditions in Jeddah, Saudi Arabia.This research uses a one sample t-test to measure the extent of the effects.Model buildings; similar to materials that make up real buildings in Jeddah; with and without a rooftop garden were used in the experiment.The internal temperature of each building was recorded at regular intervals for a period of time, and the results were then compared.The results showed that there is a difference in temperatures between the two buildings especially, in the peak temperatures.It is expected that if rooftop gardens were implemented on a large scale, will reduce energy consumption and eventually energy bill.Consequently, the rooftop garden will be a financially and an environmentally beneficial idea.
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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 | 0.001 |
| 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) | 0.002 | 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".