Water quality assessment of Villa Victoria and Madin reservoirs: a case study
Bibliographic record
Abstract
The presence of contaminants, such as metals, in water storage systems leads to a risk of adverse effects on human health and aquatic organisms since these substances are difficult to eliminate and are often recycled through physicochemical and biological processes. In recent years, there has been severe hydrological stress in the Madin and Villa Victoria reservoirs, in addition to an increase in industrial and agricultural activities around these systems, so the release of contaminants into the water is becoming more frequent. The objective of this research was to determine the environmental risk associated with the contaminants present in the Madin and Villa Victoria reservoirs, as well as the degree of toxicity of the water, allowing us to obtain a deeper understanding of the water quality of these reservoirs over time. For this, the quality index was established using the physicochemical parameters of interest for the development of aquatic organisms reported from 2013 to 2020 based on the methodology of the Canadian Council of the Ministry of the Environment. In Madin Dam, the water quality was poor and marginal depending on the site, while for Villa Victoria, the index is between marginal and acceptable. It was also observed that water toxicity in species such as Danio rerio, Allium cepa, and Vibrio fischeri, varies based on the amount of metals found in the water samples; therefore, the presence of contaminants in the water can cause potential adverse effects on the aquatic organisms that inhabit these reservoirs, as a risk quotient greater than one was found in both reservoirs.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".