The Influence of Trees and Water Features on Human Health and Thermal Comfort in Hot Arid Climate at The Microclimate Level
Bibliographic record
Abstract
The primary objective of the study is to determine if plants and water can lower particulate matter (PM) and carbon dioxide (CO2) levels in microclimates while simultaneously enhancing user thermal comfort. There are several implications of urban air pollution on human health, ranging from eye discomfort to fatality. The previous studies investigated the human thermal comfort in term of temperature and humidity. The purpose of this study is to evaluate thermal comfort and air pollution in two microclimates of a Tucson, Arizona college building. The trees and water features have increased the relative humidity by 110 percent over baseline levels. Consequently, the temperature fell by 19%. This significant microclimate improvement will put the majority of outdoor areas inside the thermal comfort zone for humans. The trees and water had a considerable influence on PM levels, decreasing PM2.5 by 50 percent and PM10 by 55 percent. In this research, however, the C3 type Calvin cycle caused a 4.8% increase in CO2. The trees may lower CO2 in other senior with a higher CO2 content, hence decreasing the possibility that the C3 cycle will be initiated. The ability to minimize air pollutants while simultaneously enhancing temperature conditions would result in a microclimate that is conducive to a variety of activities.
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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) | 0.001 | 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".