Species-specific mercury speciation in billfishes and its implications for food safety monitoring and dietary advice
Bibliographic record
Abstract
Humans are exposed to toxic methylmercury mainly by consuming marine fish, in particular top predator species like billfishes or tunas. In seafood risk assessments, mercury is assumed to be mostly present as organic methylmercury in predatory fishes; yet high percentages of inorganic mercury were recently reported in marlins, suggesting markedly different methylmercury metabolism across species. We quantified total mercury and methylmercury concentrations in muscle of four billfish species from the Indian and the Pacific oceans to address this knowledge gap. We found low percentages of methylmercury in blue and black marlins (15 ± 7 %) compared to swordfish and striped marlin (89 ± 13 %), with no significant differences among ocean regions. This illustrates that billfishes exhibit species-specific methylmercury bioaccumulation patterns, likely related to unique selenium-dependent in vivo methylmercury demethylation capacities in muscle. Blue and black marlins therefore appeared generally safer for human consumption than swordfish and striped marlin regarding MeHg toxicological effects. Yet, no matter the species, the frequency of recommended weekly billfish meals decreased with increasing fish size, given that mercury naturally accumulates over time. When assessing potential risks of billfish consumption, we therefore recommend measuring methylmercury, rather than total mercury, and relying on a large number of samples to cover a broad range of fish sizes. This study calls for additional characterization of mercury speciation and bioavailability in billfishes to better understand the mechanisms driving species-specific differences of methylmercury detoxification, and to refine dietary advices associated to marine top predators consumption.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".