Revealing Impacts of Trees on Modeling Microclimate Behavior in Spaces between Buildings through Simulation Monitoring
Bibliographic record
Abstract
Urban trees have been recognized as having different impacts on the microclimate and thermal comfort. Therefore, this study conducted on-site measurement and simulation to explore and clarify how trees impact the microclimate, thermal comfort, and façade temperature. A campus site was selected as the test field and two models—one with and one without trees—were built with the ENVI-met. Meanwhile, one microclimate station and four sensors were installed to simulate and validate the microclimate. Twelve blocks with different tree conditions were also selected to further investigate the specific impacts of trees. The results showed that, firstly, the transpiration and sheltering effect of trees that dominates on sunny days can decrease air temperature and the predicted mean vote. Secondly, trees’ effects on airflow, including on the wind channel and blocking effect, are dominant on cloudy days. Trees inside the group often exhibit the wind-blocking effect, while trees with a downwind determinant at the windward group edge usually exhibit the wind channel effect. Thirdly, high canopy coverage enhances trees’ sheltering effect on solar radiation. The study also provides design recommendations for campus building and trees that account for how trees help improve the microclimate, enhance comfort, and reduce energy consumption.
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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 | 0.001 | 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".