Designing Thermally Pleasant Open Areas: The Influence of Microclimatic Conditions on Comfort and Adaptation in Midwest Brazil
Bibliographic record
Abstract
The increasing temperatures in urban areas can negatively affect the health and comfort of its dwellers. Thus, this study assessed thermal comfort conditions in a public square, located in hot-humid Midwest Brazil, based on the relationship between microclimate conditions, thermal sensations and adaptation. Data used were collected through micrometeorological measurements and questionnaire surveys, which were performed simultaneously in field campaigns during hot and cold seasons. The aim was to propose design guidelines for open areas according to local thermal preferences. An updated regionally-calibrated Physiological Equivalent Temperature (PET) assessment scale is also proposed. Neutral temperatures were estimated on a seasonal basis and critical discomfort hours on a monthly basis. The results reaffirm that psychological and behavioral factors influence the individuals’ assessment of the outdoor thermal environment and therefore should be considered as design criteria. To improve the microclimate of urban open areas in Campo Grande, shading must be provided primarily by trees and resting areas should be protected from wind exposure. The use of water features for evaporative cooling purposes is not recommended, however, drinking fountains should be available in public squares. Such results can be used by landscape architects and urban planners to deliver thermally comfortable open spaces, encouraging greater use and increased length of stay in these areas.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".