Determinação de metais potencialmente tóxicos em amostras de água e sedimentos nas bacias dos rios Cuiabá e São Lourenço - MT
Bibliographic record
Abstract
Contamination of water resources is directly linked to anthropic activities. Some of these activities can release metals in the environment which, once in the water, cause great concern since they are not biodegradable, can accumulate in the biota and so reach critical levels causing adverse effects to human health and to the environment. The watersheds of Cuiabá and São Lourenço rivers are of great importance since their water is applied to multiple uses, involving the main municipalities of Mato Grosso State. Moreover they are the main affluent of Paraguay river which is the most important contributor to the Pantanal of Mato Grosso. Thus, this study aimed to determine the concentration of Cu, Cr, Cd, Mn, Fe, Pb and Zn in surface water and riverbed sediment of Cuiabá and São Lourenço watersheds. Metals were analysed in water using flame atomic absorption spectrometry and inductive couple plasma emission spectrometry and in sediment using flame atomic absorption spectrometry. The metal concentrations in water were compared to the maximum allowed values established in the Resolution CONAMA n. 357/2005 for class II river waters while for sediments the values established by the Canadian Council of Ministers of the Environment (CCME) were used for comparison. The hierarchical cluster analysis revealed the existence of four groups regarding water quality and two groups of sediment. Discriminant analysis (DA) was applied to evaluate the grouping quality showing a correct classification of 80% of water and 79% of sediment samples. A análise de agrupamento hierárquica (AHA) revelou a existência de quatro agrupamentos para as amostras de água e dois agrupamentos para as amostras de sedimento. Moreover, DA in stepwise forward mode showed that it was possible to reduce the number of evaluated variables keeping the same efficiency in the groups classification. Exceedence curves showed that the majority of metal concentrations were below the limits established in the legislation. However, especial attention should be given to Pb and Cr that were detected in concentrations above these limits in water as well as in sediment samples in some sampling points, probably related to anthropic activities such as domestic and industrial effluents discharge.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".