Initiatives to combat mercury use in artisanal small-scale gold mining: A review on issues and challenges
Bibliographic record
Abstract
The Minamata Convention on mercury has received a number of criticisms and challenges that potentially hinder its progress on reducing and controlling mercury use and release by artisanal small-scale gold mining (ASSGM). The resulting weak environmental control has repercussions for the social and environmental wellbeing of countries that subsist on ASSGM in their territory. Lack of distinguishing and categorizing ASSGM, the absence of a contextual implementation plan, and no defined means of communication are some of the aspects that lead to unsuccessful initiatives, particularly on effectively introducing mercury-free technologies. Moreover, an underestimation of the active mining population implies that the problem is greater than what is perceived. ASSGM is a viable source of poverty alleviation which cannot be ignored and therefore the resulting socioeconomic and environmental challenges need to be addressed while optimizing economic benefits. In this paper a compendium of issues and challenges that need to be addressed to reduce and control mercury use and release by ASSGM are discussed. By reviewing the challenges of successful case studies, a comprehensive approach is proposed to enhance the implementation of the Minamata Convention on mercury.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".