Mercury Pollution in Amapá, Brazil: Mercury Amalgamation in Artisanal and Small-Scale Gold Mining or Land-Cover and Land-Use Changes?
Bibliographic record
Abstract
Abstract Mercury (Hg) poses a public health burden in the Amazon and worldwide. Although usually attributed to Hg used in artisanal and small-scale gold mining (ASGM), the primary source of elevated Hg in Amazonian aquatic ecosystems is contested since there have not been tools to differentiate between Hg from ASGM and Hg from other sources such as increased soil erosion associated with land-cover and land-use change. To directly assess Hg contamination from ASGM, stable Hg isotope analyses were applied to sediment cores, surface sediments, and soils from two aquatic ecosystems in Amapá, Brazil, one downstream of ASGM activities and one isolated from ASGM. Downstream of the ASGM sites, the Hg isotope data is consistent with elevated Hg coming dominantly from increased erosion of soils and not from Hg used during gold extraction. Although these two sources represent different pathways of contamination to downstream ecosystems, ASGM may contribute to both land-cover and land-use change and local contamination of soils. Accordingly, these findings demonstrate that in some regions of the Amazon effective Hg mitigation strategies need to address land-use practices in addition to ASGM.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".