METAL CONTAMINATION OF SOILS OF THE JUNIN NATIONAL RESERVE - PERU
Bibliographic record
Abstract
Improper dumping of tailings from nearby mining companies into the rivers that flow into Lake Junín as well as improper management and discharge of the Upamayo hydroelectric dam can cause soil contamination. The aim of the study was to analyze metal pollution in the soils of the Junín National Reserve in Peru. Samples were taken at 10 points within the study area at 2 depths (0-15 and 15-30 cm) to assess the impact of metals on the soil. Concentrations of As, Cd, Cu, Hg, Pb, and Zn have exceeded the thresholds of the soil quality guidelines for the protection of the environment and human health according to Canadian regulations. Similarly, the ecological risk index revealed that the risk level for Cd, Cu, Hg, and Pb is severe and/or serious at both depths. The geoaccumulation index indicates that an accumulation of Cu, Zn, Pb, and Hg in the soils of the study area, indicating they are persistent pollutants. Furthermore, spatial distribution shows that the nearest sites to the pollutant sources were the most contaminated. Likewise, pH, texture and EC are factors influencing metal concentrations in soil. In conclusion, mining activity and other human actions have contaminated the soils near Lake Junín, impacting the environment and local communities. Although metal concentrations vary, the uniformity in their vertical distribution highlights the complexity of pollution.
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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 | 0.001 |
| 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".