Water Quality Analysis in a Micro-Watershed for the Community of Loma Alta Norte
Bibliographic record
Abstract
The study in the micro-basin of Santa Lucía, in the community of Loma Alta Norte, has aimed to determine whether the water has met the appropriate conditions to guarantee the health of the inhabitants.The research has focused on analyzing the water quality at different points, by non-probabilistic sampling, and on validating the results of the parameters in the National Technical Standard for the Quality of Drinking Water and the Technical Standards for Wastewater Discharges.The methodology has included physicochemical and microbiological analyses, using statistical tools such as t tests and ANOVA to compare the data obtained, employing experimental design.The results of the physicochemical and microbiological tests carried out in the laboratory, together with the ANOVA analysis, have indicated that the water quality parameters, such as temperature, turbidity and TDS, show significant differences between the stations.However, these differences are not alarming and do not suggest threatening contamination in the water.Furthermore, parameters such as pH, nitrates and phosphates have been kept within the permissible ranges, all evaluated in accordance with the "National Technical Standard for Drinking Water Quality" and the "Technical Standards for Wastewater Discharges".The calculation of the water quality index has indicated that all the stations analyzed in the micro-basin are within an acceptable range, with a use criterion classified as "Excellent Quality".This shows that the water in the Santa Lucía micro-basin, in the community of Loma Alta Norte, meets optimal quality according to the standards established by the NSF.Consequently, it has been reflected that water is suitable for human consumption and does not present significant risks of contamination that may affect the environment.
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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 | 0.000 |
| 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 | 0.000 |
| 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".