Bioaccumulation of mercury in direct-developing frogs: The aftermath of illegal gold mining in a National Park
Bibliographic record
Abstract
The use of mercury in mining gold is an illegal but still common practice in developing countries and is the world’s largest source of mercury pollution. The mercury released into the environment bioaccumulates in organism tissues due to its chemical properties and can adversely alter wildlife's neurological and reproductive systems. Frogs are susceptible to mercury contamination from gold mining because of their high skin permeability and association with aquatic environments. However, the effect of mercury pollution on direct-developing frogs is poorly known, particularly in tropical highlands. To understand the impact of mercury due to gold mining contamination on biodiversity of Tropical Andes, we assessed the bioaccumulation of mercury on direct-developing frogs of genus Pristimantis in a montane forest. We assessed bioaccumulation by comparing muscle tissue samples of frogs and sediments of streams in an area previously affected by illegal gold mining inside the Farallones de Cali National Park. Even though gold mining has not been conducted in the area for several years, we found mercury in muscle samples of direct-developing species of genus Pristimantis and alarming mercury concentrations in the sediment samples that exceed risk thresholds according international guidelines of the WHO (1.0749 μg.g-1) and countries such as Canada, USA and Brazil (0.35 μg.g-1). Our results suggest that the use of heavy metals in the gold mining can affect non-aquatic species causing bioaccumulation of heavy metals, which can be an important threat to wildlife populations, the stability of the ecosystem, and public health. Keywords: Andean forests, mercury pollution, muscle tissue, streams pollution, sediments, total 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".