Caracterização hidrológica e hidrogeoquímica do Parque Estadual do Itacolomi - Ouro Preto, MG.
Bibliographic record
Abstract
This research had been developed in the Itacolomi State Park (PEI) responsible on the part of the water supply for the district of Passagem de Mariana and the city of Ouro Preto (MG). A hydrology and hydrogeochemistry characterization was accomplished for diagnosing the water conditions of this region. The characterization of the superficial hydrology means was used to evaluate the water potential and the litology influence on that potential. This characterization was based on determining the flow rates by the Guelph permeameter during one hydrological year of three distinct sub-basins selected according to the local geology. The geochemistry analysis of the superficial waters was related with the geological scenarios, flows conditions and ground geochemistry. 18 water sampling points were selected on areas with different drainage patterns. 36 ground samples were also collected at points located in representative areas from the different litology units. The ground samples were collected in several depths on each region. In the ground and water samples the concentrations of major and trace elements were determined by ICP-OES and the ground mineralogy determination were performed by X-rays diffraction. Besides, some physicist-chemistries parameters (pH, electric condutivity and dissolved solids totals) were determined for the water samples. The hydrology analysis favor to understanding the aquifer systems from the explored region by relating the litology, the geologic structure, and the geomorfologic configuration. The lineaments densities and directions, the dolinamentos presence, the altimetric differences, and the segments of the topographic levels of declivity and lithological were investigated. Using the higrological data we can note the hidrical potencial from the sub-basins verifying the data influence in the spatial and temporal variance for the hidroquimic of the drainage systems of the PEI. The results of the analisys reveal the geology influence over the ground features, the flow conditions differences and the chemical signature of the superficial waters in the region. The quimical elements from the analised water and grounds had been compared with patterns values defined by the rules CETESB (2005) and CONAMA (2005) proving the geological and pedological influences on the water quality. Outliers for several elements were detected on waters and grounds. These outliers reflex the litology indicating natural concentrations because the explored area is a preservation unit without antropic interference.
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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 | 0.000 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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; both teacher heads agree on what is shown here.
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".