Heavy Metal Pollution Assessment in Lake Rinconada in the Southern Andes, Peru
Bibliographic record
Abstract
The study was conducted in Lake Rinconada, a glacial lake affected by artisanal and small-scale gold mining activities in the southern Andes in Peru. The objectives of the study were to investigate the spatial distribution of heavy metals (As, Cu, Hg, Pb and Zn) in water and sediments and to assess the degree of metal pollution and ecological risk using the geoaccumulation and potential ecological risk indexes. The concentrations of As and Hg in sediments from Lake Rinconada exceeded the Canadian sediment quality regulations, whereas the concentrations of As, Hg and Pb in water and sediments from the mining-affected tributary, Lunar de Oro River exceeded the Peruvian and Canadian guidelines for water and sediments quality respectively. According to the geoaccumulation and potential ecological risk indexes, Lake Rinconada is extremely polluted by As and Hg, and the pollution is mostly concentrated in the northern part of the lake, where the mining-affected Lunar de Oro River flows into the lake. Concentrations of Pb are also high in the northern part of the lake, suggesting that the nearby gold mining town is a source of pollution. The results of this study allows to report that Lake Rinconada is completely deteriorated.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".