Severe climate change risks to food security and nutrition
Bibliographic record
Abstract
This paper discusses severe risks to food security and nutrition that are linked to ongoing and projected climate change, particularly climate and weather extremes in global warming, drought, flooding, and precipitation. We specifically consider the impacts on populations vulnerable to food insecurity and malnutrition due to lower income, lower access to nutritious food, or social discrimination. The paper defines climate-related “severe risk” in the context of food security and nutrition, using a combination of criteria, including the magnitude and likelihood of adverse consequences, the timing of the risk and the ability to reduce the risk. Severe climate change risks to food security and nutrition are those which result, with high likelihood, in pervasive and persistent food insecurity and malnutrition for millions of people, have the potential for cascading effects beyond the food systems, and against which we have limited ability to prevent or fully respond. The paper uses internationally agreed definitions of risks to food security and nutrition to describe the magnitude of adverse consequences. Moreover, the paper assesses the conditions under which climate change-induced risks to food security and nutrition could become severe based on findings in the literature using different climate change scenarios and shared socioeconomic pathways. Finally, the paper proposes adaptation options, including institutional management and governance actions, that could be taken now to prevent or reduce the severe climate risks to future human food security and nutrition.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".