Patterns of outdoor exposure to heat in three South Asian cities
Why this work is in the frame
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Bibliographic record
Abstract
Low socio-economic status has been widely recognized as a significant factor in enhancing a person's vulnerability to climate change including vulnerability to changes in temperature. Yet, little is known about exposure to heat within cities in developing countries, and even less about exposure within informal neighbourhoods in those countries. This paper presents an assessment of exposure to outdoor heat in the South Asian cities Delhi, Dhaka, and Faisalabad. The temporal evolution of exposure to heat is evaluated, as well as intra-urban differences, using meteorological measurements from mobile and stationary devices (April-September 2016). Exposure to heat is compared between low-income and other neighbourhoods in these cities. Results are expressed in terms of air temperature and in terms of the thermal indices Heat Index (HI), Wet Bulb Globe Temperature (WBGT) and Universal Thermal Climate Index (UTCI) at walking level. Conditions classified as dangerous to very dangerous, and likely to impede productivity, are observed almost every day of the measurement period during daytime, even when air temperature drops after the onset of the monsoon. It is recommended to cast heat warnings in terms of thermal indices instead of just temperature. Our results nuance the idea that people living in informal neighbourhoods are consistently more exposed to heat than people living in more prosperous neighbourhoods. During night-time, exposure does tend to be enhanced in densely-built informal neighbourhoods, but not if the low-income neighbourhoods are more open, or if they are embedded in green/blue areas.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it