The Framework to Mitigate the Urban Heat Island Effect for Improving Environment and Protecting Human Health
Bibliographic record
Abstract
Changes in temperature and precipitation, sea level, fisheries, agriculture, natural ecosystems, and air quality will all directly or indirectly affect human morbidity (illness) or mortality. One of the great challenges facing our current generation of scientists and engineers is how to protect urban population from health stressors associated with summertime heat. As heat-waves are likely to increase in frequency because of global climate change, the most effective interventions, measures and policies to protect the health need to be developed and evaluated. Therefore, this paper is focused on understanding the most important factors of urban heat island (UHI) formation and their effects on urban population health with an emphasis on the considerable growth of both population and rapid urbanization of Tehran, Iran. To achieve this aim, this paper explores literally a conceptual framework about the relationship between UHI and human health. Then, it suggests three important strategies to minimize the impact of UHI on human health: achieving appropriate transportation for mitigating air pollution, providing appropriate landscape, increasing the albedo of building materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".