Mercury transport and human exposure from global marine fisheries
Bibliographic record
Abstract
Human activities have increased the global circulation of mercury, a potent neurotoxin. Mercury can be converted into methylmercury, which biomagnifies along aquatic food chains and leads to high exposure in fish-eating populations. Here we quantify temporal trends in the ocean-to-land transport of total mercury and methylmercury from fisheries and we estimate potential human mercury intake through fish consumption in 175 countries. Mercury export from the ocean increased over time as a function of fishing pressure, especially on upper-trophic-level organisms. In 2014, over 13 metric tonnes of mercury were exported from the ocean. Asian countries were important contributors of mercury export in the last decades and the western Pacific Ocean was identified as the main source. Estimates of per capita mercury exposure through fish consumption showed that populations in 38% of the 175 countries assessed, mainly insular and developing nations, were exposed to doses of methylmercury above governmental thresholds. Our study shows temporal trends and spatial patterns of Hg transport by fisheries. Given the high mercury intake through seafood consumption observed in several understudied yet vulnerable coastal communities, we recommend a comprehensive assessment of the health exposure risk of those populations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".