Abandoned artisanal gold mines in the Brazilian Amazon: A legacy of mercury pollution
Bibliographic record
Abstract
The modern gold rush in the Brazilian Amazon attracted millions of people to become artisanal miners in order to escape complete social marginalization. The rudimentary nature of artisanal mining activities often generates a legacy of extensive environmental degradation,both during operations and well after mining activities have ceased. One of the most significant environmental impacts is derived from the use of mercury (Hg), which is illegal for use in gold amalgamation in Brazil, but continues to be the preferred method employed by artisanal gold miners. The general population is unaware of the capricious nature of mercury and artisanal mining activities. Moreover, individuals in positions of political or economic infiuence tend to be negatively biased towards artisanal mining and government policies do not effectively address the realities of these activities. Affected communities have consequently been ignored,and mistrust towards outside parties is high. Not surprisingly, miners are suspicious of and unlikely to employ externally derived solutions to reduce mercury emissions. This article reviews the use of mercury in artisanal mining and highlights the role miners, governments and non‐governmental organizations (NGOs) have played in communicating facts, perpetuating myths and deriving solutions for mercury pollution. This article also raises some key concerns that must be addressed to understand the behaviour of mercury in the environment and identifies solutions for problems facing communities where artisanal gold mining operations have been abandoned.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".