Physical-Chemical Quality Assessment of the Drinking Water in the Spring Season in Tetova
Bibliographic record
Abstract
Water is the most essential product that is consumed by humans, which must be prevented from deterioration in quality. The quality of drinking water becomes even more important as water borne diseases spread through water. For this purpose, we assess the quality of drinking water in the city of Tetova with some physico-chemical parameters, which have a significant role in determining the potability of drinking water. The obtained results were compared with Macedonian standards as well as with those set by the WHO and the EU. In this research, parameters such as turbidity, EC, TRAE, TDS, COD, TOC, DOC, nitrates and chlorides were found to be within the permissible limits, while temperature, pH and residual chlorine in some cases were found to be below the recommended limit. Finally, the Drinking Water Quality Index (DWQI) developed by Canadian Council of Ministers of the Environment for fifteen sample points is calculated. It has been found that drinking water in the 2011 spring season was of a Good category (average value of DWQI = 87.57) and suitable for drinking. We recommend that the relevant municipal authorities make regular and proper amount disinfection of drinking water, as there is no compromise that can be made when it comes to the drinking water.
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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 | 0.001 |
| 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".