Evaluación fisicoquímica del agua potable en la Ciudad de Macas - Ecuador durante el segundo trimestre del 2022
Bibliographic record
Abstract
The physicochemical evaluation of drinking water is an essential process to guarantee the safety \nand quality of the water supply in any location, including the city of Macas in Ecuador. Drinking \nwater is a vital resource for the health and well-being of the population, so it is crucial to ensure \nthat it meets the established standards. In this way, the present investigation was based on the \nmonthly sampling of the second quarter of the year 2022 of the physicochemical and \nmicrobiological parameters of the drinking water of the city of Macas and compared with the \nINEN 1108:2014 DRINKING WATER standard. REQUIREMENTS, in conclusion, the \nevaluation of the physicochemical parameters of water samples from different locations in Macas \nrevealed variations in the levels of turbidity, pH, temperature and free chlorine. Although some \nsamples were considered fit for human consumption, others exceeded the maximum permissible \nlimits, mainly due to increased precipitation and the consequent transport of particles and \nsubstances. These findings highlight the importance of continuous monitoring and treatment \nprocesses to guarantee the supply of drinking water to the population.
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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 | 0.000 | 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".