The consumption of shark meat in the Amazon region and its implications for human health and the marine ecosystem
Bibliographic record
Abstract
Here, we evaluated the levels of As, Hg, Pb, and Cd in shark meat sold along the Amazon Coast of Brazil and used nitrogen stable isotope values to determine trophic position and to assess element biomagnification. From market samples, a total of 13 species were identified via molecular analysis, including those listed as endangered and vulnerable by the IUCN Red List. Arsenic was present in significantly higher concentrations than all other elements, followed by Hg, with the highest mean concentrations recorded in M. higmani (As: 19.46 ± 8.79 μg/g ww) and C. acronotus (Hg: 1.12 ± 0.68 μg/g ww). Lead and Cd were recorded at much lower levels in all species. The EWI of individual elements were above PTWI for all species when considering Hg, seven species for inorganic arsenic (iAs), and one species for Pb. The weekly consumption of 10 species should be reduced to less than 416.39 g, which is equivalent to the daily estimated fish consumption rate in the region. The mean (±SD) δ15N values of species ranged from 10.7 ± 0.51‰ in M. higmani to 14.2 ± 0.59‰ in C. porosus, indicating feeding over >1 trophic level. Arsenic was negatively correlated with δ15N values, while Hg was positively correlated indicating biodilution and biomagnification, respectively. Our results indicate that the sale and consumption of shark meat will expose consumers to potentially harmful levels of iAs and Hg, as well as contributing to the population decline of species including those that are currently categorized as threatened.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".