Heavy metals in the São Mateus Stream Basin, Peixe River Basin, Paraiba do Sul River Basin, Brazil
Bibliographic record
Abstract
Large-scale enterprises with high potential to pollute need to be licensed, properly supervised and monitored during and after their operations to avoid and/or mitigate impacts in their areas of influence. The São Mateus Stream Basin (SMSB), located in rural area of Juiz de Fora (MG), is impacted by several activities, highlighting a deactivated landfill and an industrial park. This study monitored the concentration of heavy metals in the waters of the main tributaries of the SMSB. Strategic points were selected in each sub-basin, before the mouth and meeting of the Bocaina, Salvaterra and São Mateus Streams, measured monthly between January and December 2014 using the Metalyser probe, and applying the Contamination Index (CI). The CI results showed that the enterprises located in this basin, especially the Park Sul and Salvaterra Landfill in the Bocaina and Salvaterra Streams, respectively, are negatively impacting the quality of these waters. Metals such as Hg, Cu, Pb and Zn were the ones that most violated CONAMA Resolution 357/2005, directing management in order to control the sources of these metals, which are cumulative in organisms and damage the whole trophic chain. The inhabitants of this rural area are not served by any water concessionaire and make use of springs and wells below the level of these streams.
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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 | 0.000 | 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".