Mercury Challenges in Mexico: Regulatory, Trade and Environmental Impacts
Bibliographic record
Abstract
Primary artisanal mercury (Hg) mining in Mexico continues to proliferate unabated, while official Hg exports have declined in recent years amid speculation of a rising black market trade. In this paper, an assessment of primary Hg mining in Mexico was conducted, with a focus on four sites in Querétaro State. Atmospheric Hg concentrations were measured at two of those sites. In addition, trade data was examined, including Hg exports from Mexico and imports by countries that have a large artisanal gold mining (AGM) sector. Results showed that while annual Hg production in Mexico has ramped up in recent years, official Hg exports reduced from 307 tonnes in 2015 to 63 tonnes in 2019. Since 2010, mercury exports to Colombia, Peru and Bolivia have represented 77% of Mexico’s total Hg trade. As the large majority of Hg trade with these countries is apparently destined for the AGM sector, which is contrary to Article 3 of the Minamata Convention, there is evidence that increased international scrutiny has led to an increase in unregulated international transfers. Atmospheric Hg concentrations at the mines show dangerously high levels, raising concern over the risk of significant health impacts to miners and other community members.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".