Temporal evolution of human mercury exposure in the Amazon
Bibliographic record
Abstract
Due to the current global attention to mercury exposure and toxicity, as well as its various consequences on ecosystems and human health, new scientometric tools help to better understand the issues involved. In this literature research, studies of the risk of human exposure to mercury in populations of the Brazilian Amazon biome in the last three decades were contemplated using scientometric techniques, bibliographic docking, authors, citations, and keywords. The analyses of the period from 1991 to 2019 enabled the selection of 130 articles. There was the identification of the main research institutions, classification and interrelations of the main thematic axes of the studies in the Amazon biome and most cited authors. The most referenced articles on this theme and the main bioindicators were ordered. The results show that most of the studies were carried out along rivers and with riverside populations. In the sample universe, there is a predominance of localities on the Tapajós and Madeira Rivers. Most researchers work only with internal partnerships, without interaction with other scientific groups. The hair matrix is the main bioindicator of Hg exposure used by the authors. For future perspective, this paper has the potential to represent a general temporal understanding of human exposure to mercury in the Amazon and its main bioindicators.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".