Diet, nutrition, and climate: historical and contemporary connections
Bibliographic record
Abstract
This paper reviews the past global nutrition efforts, particularly those led by the Global Alliance for Improved Nutrition (GAIN), at the critical intersection of nutrition and climate change. Despite progress in tackling malnutrition and promoting sustainable food systems, significant challenges remain, especially in regions like Sub-Saharan Africa and South Asia, where micronutrient deficiencies persist. The paper underscores the urgent need to integrate nutrition into climate strategies and strengthen food system resilience. Initiatives like the Scaling Up Nutrition (SUN) movement, the Food Systems Dashboard, and GAIN's public and private sector partnerships at the local levels have contributed to transforming food systems. However, there is an urgent need for more robust policies that effectively align nutrition, climate, and equity goals. Looking ahead, we advocate for increased financial investment, improved policy frameworks, and innovations in technology and data monitoring to drive sustainable food system transformations. We further underscore the importance of addressing micronutrient deficiencies, promoting biodiversity, and developing healthier crops to support climate-smart agriculture. Achieving resilient, equitable, and sustainable food systems over the next years will depend on collaborative efforts across sectors and stakeholders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".