Human mercury exposure and adverse health effects in the Amazon: a review
Bibliographic record
Abstract
This paper examines issues of human mercury (Hg) exposure and adverse health effects throughout the Amazon region. An extensive review was conducted using bibliographic indexes as well as secondary sources. There are several sources of Hg (mining, deforestation, reservoirs), and exposure takes place through inhalation or from fish consumption. There is a wide range of exposure, with mean hair-Hg levels above 15 microg/g in several Amazonian communities, placing them among the highest reported levels in the world today. Dietary Hg intake has been estimated in the vicinity of 1-2 microg/kg/day, considerably higher than the USEPA RfD of 0.1 microg/kg/day or the World Health Organization recommendation of 0.23 microg/kg/day. Neurobehavioral deficits and, in some cases, clinical signs have been reported both for adults and children in relation to Hg exposure in several Amazonian countries. There is also some evidence of cytogenetic damage, immune alterations, and cardiovascular toxicity. Since fish provide a highly nutritious food source, there is an urgent need to find realistic and feasible solutions that will reduce exposure and toxic risk, while maintaining healthy traditional dietary habits and preserving this unique biodiversity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".