Avaliação e distribuição de metais traço em sedimentos superficiais da bacia hidrográfica do Rio Japaratuba/SE
Bibliographic record
Abstract
The Japaratuba River Basin (BHRJ) is an important water resource in the state of Sergipe with a geographical area of 1,700 km 2 , equivalent to 7.5% of the state territory, and covering about 120,000 inhabitants.This study aimed to determine and evaluate the distribution of Cr, Cu, Ni, Pb, Zn, Al, Fe and Mn metals in twenty surface sediment samples collected along the basin.The extraction method was efficient with agreement for total contents, ranging from 80% (Cu) to 107% (Ni) for Lago reference material (MR) (LKSD-1 CCNRP / Canada), and between 83%.(Cu) and 118% (Pb) for Marine MRC (NCS DC 75304 (T) / CNACIS / China).In the partial contents of MR, it varied from 93% (Cu) and 107% (Pb).The total and partial concentrations of the metals presented a wide range of variation in the values obtained by the flame atomic absorption spectrometry (FAAS) technique.Significant correlations between iron and other metals indicated that this element is the main inorganic carrier in controlling the distribution of metals in the sediments of the study area.Total metal contents were normalized from iron.According to geochemical normalization, enrichment factor (EF) and geoaccumulation index (Igeo), the levels of the metals analyzed can be considered as natural in origin except at point P18.This was enriched by Cu (EF = 1.91) and Zn (EF = 1.77).The obtained regression lines can be used to define the regional geochemical basis.The application of PCA and HCA suggested similar geochemical characteristics between the points: P1, P4, P7 -P9, P11, P14 -P20 (Group I), and for the points: (P2, P3, P5, P6, P10, P12, and P13 (Group II) The partial contents of Cr, Cu, Pb and Zn were lower than TEL / PEL and TEC / PEC in all sampling points, while Ni presented higher levels of TEL at points P5, P6, P15 and P18 higher than TEL and TEC In order to assess the potential impact of contamination, we calculated the quality quotients for PEL (QPEL-VGQS) and PEC (QPEL-VGQS), which showed that the studied region is moderately impacted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".