Using Earthworms to Assess Hg Distribution and Bioavailability in Gold Mining Soils
Bibliographic record
Abstract
Between 1980 and 2000, the municipality of Cachoeira do Piriá, located in Pará State, Brazil, experienced an intense gold rush with approximately 5,000 artisanal miners discharging more than four tonnes of mercury into soils, air and aquatic systems. Mercury is dispersed across an area of approximately 2,100 ha and concentrations in soils and sediments frequently exceed 1,000 μg.kg−1. The metallic mercury discharged by miners into the environment has the potential to be transformed into a highly toxic form of mercury, methylmercury. A 28-day bioassay with the earthworm Eisenia fetida was used to assess mercury bioavailability in mine tailings, soils, and sediments. Experiments indicated that the highest Hg concentration in earthworms was associated with low-Hg-organic-rich soils collected from densely vegetated areas despite higher mercury concentrations in organic-poor tailings. This indicates that reaction with organic acids is an important pathway for mercury incorporation into food chains. The quick, inexpensive, and simple bioassay also provided a means to evaluate remedial measures (i.e. by capping “hotspots” with local soils). Earthworm experiments indicate that covering “environmental hotspots” (sites with high Hg bioavailability) with local clay-rich sediments is very effective in terms of preventing uptake of mercury from tailings, while organic-rich sediments are relatively ineffective.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".