Bibliographic record
Abstract
Computational modelling has been used to investigate the impact of vegetation on the microclimate in a campus courtyard in Qatar.Sixteen scenarios were tested during a hot day with over twenty thousand data entry points analysed for Air Temperature, Wind Speed, Mean Radiant Temperature and Predicted Mean Vote parameters.Trees provide direct shading, evapotranspiration and wind shielding which impacts thermal comfort conditions experienced locally as well as mitigating heat island effect.Vegetation has the ability to reduce excessive air temperature through sunlight interception.Its geometric configuration influences the amount of solar radiation, air temperature, humidity and wind velocity on microclimate of a given area.The study indicates that an equally distributed vegetation cover results in improved thermal comfort and a significant MRT reduction of 28.3C within the courtyard.The arrangement provides MRT improvement of 7.4C compared to the baseline scenario.The vegetation type also impacts the microclimate where trees of the same leaf density and higher trunk result in an increase of 0.2m/s in wind speed.The shaded grass areas witness a reduction of 1.1C of air temperature and 22C surface temperature compared to exposed hardscape.When replacing trees by shading structures, an increase of 3-5C MRT is witnessed along with an increase in reflected solar radiation.The study concluded that the placement of trees in a configuration that allows air movement and provision of shading as well as having an equally distributed vegetation cover are key to reduce both short-wave and long-wave radiation, avoid solar absorption and heat being trapped in the space.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".