Four Decades of Efforts to Reduce or Eliminate Mercury Pollution in Artisanal Gold Mining
Bibliographic record
Abstract
Throughout the past four decades, most projects related to mercury in Artisanal Gold Mining (AGM) have been dedicated to monitoring the environmental and health impacts of the activity without actually proposing effective solutions to tackle the issue. Recently, the UN and a few NGOs have been dedicated to bringing solutions to artisanal gold miners, but the outcomes remain modest, given the funds expended and the considerable effort invested by interventionists. This commentary paper critiques some of the interventions observed in the last four decades and suggests some technical strategies to approach artisanal miners to reduce mercury losses. It is stressed that mercury elimination is a consequence of good engagement with miners that creates opportunities to show them how to produce more gold with cleaner methods. We recommend that academics educate a new generation of engineers working with AGM to adopt a more practical approach, ensuring they understand the needs, motivations, and skills of artisanal miners before proposing solutions.
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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 | 0.000 | 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 | 0.000 |
| 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".