Climate Change and Nutrition in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change is a threat to Africa, one of the most vulnerable regions to climate variability and change, due to its sensitive economies, multiple stresses, low resilience, endemic poverty, weak institutions, recurrent droughts, complex emergencies, and conflicts. Climate impacts African populations, economies, and the need for emergency resources. Climate change exacerbates undernutrition and undermines efforts to reduce poverty and the resilience of vulnerable populations, decreasing their ability to cope and adapt to negative consequences of climate change and inhibiting their economic growth, particularly in sub-Saharan countries. Recent drought-triggered famine in Somalia spurred food crises in other countries, demonstrating the consequences that may come with the increased frequency of extreme weather events. This article reviews the existing research on climate change and variability; its impacts on nutrition security in Africa, focusing on sub-Saharan Africa; and adaptation and mitigation strategies to address these challenges. This article identifies research needs in nutrition and related sectors to address the impacts that climate change will have on nutrition security in Africa and adaptation and mitigation strategies over the next 10–15 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it