Global fishing patterns amplify human exposures to methylmercury
Bibliographic record
Abstract
Global pollution has exacerbated accumulation of toxicants like methylmercury (MeHg) in seafood. Human exposure to MeHg has been associated with long-term neurodevelopmental delays and impaired cardiovascular health, while many micronutrients in seafood are beneficial to health. The largest MeHg exposure source for many general populations originates from marine fish that are harvested from the global ocean and sold in the commercial seafood market. Here, we use high-resolution catch data for global fisheries and an empirically constrained spatial model for seafood MeHg to examine the spatial origins and magnitudes of MeHg extracted from the ocean. Results suggest that tropical and subtropical fisheries account for >70% of the MeHg extracted from the ocean because they are the major fishing grounds for large pelagic fishes and the natural biogeochemistry in this region facilitates seawater MeHg production. Compounding this issue, micronutrients (selenium and omega-3 fatty acids) are lowest in seafood harvested from warm, low-latitude regions and may be further depleted by future ocean warming. Our results imply that extensive harvests of large pelagic species by industrial fisheries, particularly in the tropics, drive global public health concerns related to MeHg exposure. We estimate that 84 to 99% of subsistence fishing entities globally likely exceed MeHg exposure thresholds based on typical rates of subsistence fish consumption. Results highlight the need for both stringent controls on global pollution and better accounting for human nutrition in fishing choices.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".