Synergic Effect of Gold Mining and Damming on Mercury Contamination in Fish
Bibliographic record
Abstract
Since the late 1980s, several studies have shown that human populations in the Amazon basin are exposed to high mercury levels in their fish diet. Gold mining, which releases the metal during the amalgamation process and erodes soils naturally rich in mercury, is regarded as the main contamination source. Here, we present the results of a comparative study of mercury distribution in the water and fish of two adjacent rivers in French Guiana, with and without gold mining activities. As a consequence of a marked difference in suspended particulate matter between the two systems, total mercury concentrations in unfiltered water samples were higher in the mined river (25.4-34.9 ng L(-1)) as compared to the reference one (2.1-5.4 ng L(-1)). Surprisingly, no significant differences were observed in mercury concentrations between 13 common fish species at upstream sites. In sharp contrast, mercury concentration of fish caught downstream a hydroelectric reservoir, where the two rivers flow, was up to 8-fold higher than that upstream. Mercury speciation measurements allowed one to relate these differences in fish to the water distribution of monomethylmercury, the mercury chemical species that biomagnifies along aquatic foodwebs. Indeed, mean dissolved monomethylmercury concentrations were low and similar in both rivers (0.03-0.06 ng L(-1)), while they were 10 times higher (up to 0.56 ng L(-1)) in the water outflowing the hydroelectric dam. Dissolved monomethylmercury determinations along a water column profile suggest that methylation of inorganic mercury occurs in the deep anoxic part in reservoir. We conclude that mercury mobilization related to gold mining is not solely sufficient to account for high concentrations in fish and that environmental conditions that favor mercury methylation, such as anoxia, are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".