Spatio‐temporal geochemistry of mercury in waters of the Tapajós and Amazon rivers, Brazil
Bibliographic record
Abstract
Spatial and temporal variations of mercury (Hg) concentrations were monitored in the surface waters from the lower portion of the Tapajós River, the Arapiuns River, its principal tributary, and the Amazon River at its confluence with the Tapajós. In the rivers, Hg concentrations in the water column are governed by the concentration of suspended particles. Hg in the filtered water showed very little seasonal variation with low concentrations in the lower Tapajós (<1.8 ng L−1), Arapiuns (<0.8 ng L−1), and Amazon (<2.8 ng L−1). Concentrations of fine particulate Hg (0.6–29.7 ng L−1) represent 40%–90% of the total volumetric concentration of Hg. In relation to their oxyhydroxide contents, suspended particles are not richer in Hg than nearby soils where oxyhydroxides control the accumulation of Hg. The study shows that the dominant stock of Hg in the aquatic ecosystems of this region is derived from erosion of natural soils in the catchment rather than from anthropogenic pollution. The input of natural Hg coming from soils into the aquatic ecosystems may have increased over historical levels in the region. This increase of total Hg in aquatic ecosystems could potentially account for high levels of methylmercury recently reported in fish and humans in the lower Tapajós River area, but the link between the different processes that promote high levels of methylmercury exposure for the human community remains to be proved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".