Climate change amplifies neurotoxic methylmercury threat to Asian fish consumers
Bibliographic record
Abstract
Climate change is intricately influencing the accumulation of neurotoxic methylmercury (MeHg) in human food webs, potentially leading to uneven exposure risks across regions. Here, we reveal that climate change will elevate MeHg risks in China, with implications for regional inequalities in Asia through a climate-mercury-food-health nexus. Using a compiled fish mercury dataset from 13,000 samples and machine learning, we find that freshwater wild fish-an essential component of the Asian diet-is an underappreciated MeHg source. Specifically, MeHg concentrations in freshwater wild fish are 2.9 to 6.2 times higher than in freshwater farmed fish and 1.7 times higher than in marine wild fish. Individual climate factors influence MeHg accumulation differently, while their combined effects significantly increase MeHg concentrations in freshwater wild fish. Under SSP2-4.5 and SSP5-8.5 by 2031 to 2060, national average MeHg concentrations in freshwater wild fish are projected to increase by about 60%, adding a maximum annual economic loss of US$18 million (2022 USD) from intelligence quotient decrements in Chinese newborns. This loss may vary regionally within China and among Asian countries, disproportionately affecting less developed areas. Coordinating climate action with mercury emission reduction strategies could mitigate these overlooked regional risks, reduce regional inequalities in food safety, and ultimately contribute to sustainable development.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".