Projected Intensification and Expansion of Heat Stress and Related Population Exposure Over Africa Under Future Climates
Bibliographic record
Abstract
Abstract Africa lacks comprehensive studies related to the intensification and expansion of high levels of heat stress and the related population exposure. In this regards, two heat stress indices (HI and UTCI) are applied to 30 CMIP6 GCMs to assess the robust changes in heat stress over Africa during the late 21st century under the SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 forcing scenarios. The analysis includes spatial extent, frequency and population exposure of different risk levels (Safe, Caution, Extreme Caution and Danger) gradually rising as the value of the heat index increases. The projections show a recession of Safe areas and a robust expansion of the Caution and Extreme caution classes, irrespective of the seasons and scenarios. The largest change of fractional areal cover, estimated between 23.9% and 30.7% of Africa land areas, occurs for the Extreme caution category over West Africa, East Africa, Central Africa and part of East southern Africa in JJA under the SSP5‐8.5 scenario. Furthermore, regions north of 5°S are projected to face an average annual increase of the number of days with high‐end heat stress conditions intensifying as greenhouse gas forcing increases. Under the SSP5‐8.5, the change in the number of days with Extreme caution ranges between 60% and 80%, while that of days in the Danger category reaches 10%–20%. As a result, the total population exposure to the Extreme caution category is projected to increase over West Africa, East Africa, and Central Africa under both SSP2‐4.5 and SSP5‐8.5 scenarios, while for the Danger class, the exposure increase is limited to the Sahel regions under SSP5‐8.5. These exposures are due to the intensification of heat stress but also to its interaction with an increased population. Our results thus highlight the need of adaptation measures to extreme heat in Africa in order to ensure healthy population and safe and efficient working conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".