Climate Change Pathways and Potential Future Risks to Nutrition and Infection
Bibliographic record
Abstract
Climate change is a recognized theme of the twenty-first century, affecting both nutrition and infection by multiple pathways and mechanisms. This chapter outlines the linkages between climate change, nutrition, and infection from a systems perspective, incorporating the past associations between global health and climate as well as the projected trajectory of change over the twenty-first century. Both observed mechanisms and associations and frameworks for scenario-based assessment and model results are presented and explained. While the synthesis emphasizes the importance of taking action on all three challenges at the same time, it also identifies the need for more knowledge on the combined impacts of climate change on nutrition and infections and of nutrition and food systems on climate change. The chapter starts by describing the climate situation and scenarios of change and assessment frameworks and then presents an overview of ways in which climate is linked to nutrition and infections. Thereafter, climate change, undernutrition, and infections are each discussed in more depth. The chapter concludes by highlighting how climate change, nutrition, and infection are all intertwined in the United Nations 2030 Sustainable Development Goals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".