A Scoping Review of Guidelines for Mercury Limits in Soil, Water, and Air: How Do Brazil’s Standards Compare to International and Developed Country Guidelines?
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mercury is a global problem for both human and environmental health. The Minamata Convention on Mercury entered into force in 2017, joining efforts of 151 countries, mostly in the Global South. Many of them are still in the process of incorporating these international concepts into domestic practice. This includes Brazil, which has been a signatory of the Convention since 2013. Since the country includes 70% of the Amazon, which is the region responsible for approximately 40% of the global emissions and 80% of South America′s emissions, it plays quite an important role in the Convention. For the successful implementation of the Convention, efforts must be made to bridge the gap between policymakers and academic knowledge when setting mercury limits. As a first step, this study followed the PRISMA-ScR guidelines and used references exclusively within this scope to perform a systematic search of guidelines with recommended limits for mercury in soil, water, and air. The documents retrieved by this approach were compared, bringing together insights and recommendations, especially for Brazil and the Amazonian context. While not exhaustive, the number, geographical diversity, and recency of the documents (guidelines) allow for the establishment of benchmarks through comparative analysis and the formulation of recommendations that could potentially be adopted by other Amazonian countries (or even other countries in the Global South), while respecting their specific differences.
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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 | 0.090 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.039 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".