A Successful Heat Wave Prevention in Ahmedabad Calls for Segregated Health Record: Highlights from Existing Heat Action Plan
Bibliographic record
Abstract
South Asia is one of the hot-spots of extreme heat events and associated health risks. As heat waves continue to get harsher due to climate change, South Asia's exposure to them is probably going to increase. After a heatwave in 2010, Ahmedabad implemented South Asia’s first heat action plan (HAP). The Ahmedabad HAP can serve as a model for other cities across South Asian nations interested in intervention strategies against excessive heat. In recent years, 2020 and onwards, Ahmedabad’s healthcare system faces an extreme COVID-19 crisis which resulted in severe negligence of heat wave-influenced mortality and morbidity cases. Though the city continued to disseminate the necessary information for public heat preparedness from the existing heat action plan, there was no record made separately for COVID-19 and heat stress-related mortality/morbidity by the health department. Thus, due to a lack of heat-related health records, we were unable to track the HAP intervention effect in 2022.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".