Proposition of groundwater quality index (GWQI) for application in areas with mining potential
Bibliographic record
Abstract
In some regions of the world, groundwater represents the main alternative supply. However, this resource has a direct link with local geology, and the dissolution of elements can make it naturally unsuitable for its intended uses - human consumption, irrigation and animal watering, for example. Therefore, it is necessary to control and monitor these waters in order to guarantee the standards recommended by current legislation, just as it is essential to pass on information about the quality of the water offered to the population for the transparency and reliability of the process. Most of the research carried out on water quality indices focused on surface waters, while studies on groundwater were little explored. In this context, the current research proposed the development of a Groundwater Quality Index (GWQI) for application in areas with mining potential, based on data from the groundwater monitoring network of the “Instituto Mineiro de Gestão das Águas” (IGAM) between the years 2018 and 2022. Secondly, it is intended to classify such points using the proposed index, as well as compare the results obtained from the GWQI with the results resulting from the application of the CCME WQI developed by the Canadian Council of Ministers of the Environment, which allows flexibility in the selection of parameters, matrices and water quality references. Additionally, cluster analysis and principal component analysis (PCA) were performed. The development of GWQI considered 10 parameters – arsenic, lead, mercury, nitrate, uranium, hardness, iron, manganese and zinc – which may arise from the dissolution of components of local geology. For each of these parameters, curves were constructed, weights were assigned and the wells were classified by GWQI and CCME WQI. Furthermore, to apply cluster analysis and PCA, the Rstudio software was used, with k-means being the clustering method chosen. Considering a database with 795 records of analyzes carried out in the Guarani, Norte de Minas, PANM and Velhas Networks, it was observed that the results obtained with the application of GWQI were mostly more conservative than those obtained with the application of CCME WQI, a fact associated with to the weighting of parameters in GWQI. The cluster analysis resulted in three clusters: cluster 1 – terrible GWQI quality, cluster 2 – regular GWQI quality and cluster 3 – terrible GWQI quality. Finally, the PCA considered five main components that were able to explain 79.9% of the total variance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".