Assessment of Drinking Water Quality Status and its Impact on Health in Tandojam City
Bibliographic record
Abstract
Unsafe drinking water is one of the major concerns in developing countries. The southern Sindh is province of Pakistan that adjoins the Arabian Sea coast where the drinking water quality is deteriorating due to unrefined urban waste and excessive use of agro-chemicals. Present study aimed to assess the quality of ground water source and to compare it with WHO standards. Water borne diseases associated with drinking it in the vicinity Tandojam city were identified. Water from ground water source was sampled at selected locations in the study area. The samples were collected mostly from pumped waters. The samples were analyzed for physico-chemical properties in order to identify the quality problems and suggest safe source for drinking purpose. Findings revealed that the ground water quality in Muzaffrabad colony is deteriorating. The situation was much worse in Muzaffrabad colony, Jam Ghar and Mir colony with high TDS and salty taste, respectively at few sampling locations. In addition, laboratory analysis of the water quality parameters revealed the detail of variation in the groundwater. TDS, hardness, sodium (Na), chloride (Cl) and magnesium (Mg) etc. were considerably beyond the WHO permissible limits. The poor quality of the water has created different waterborne diseases like cholera, diarrhea etc. Moreover, the samples of SAU Colony and Amar Town showed that the water quality of these areas was within acceptable limits according to WHO standards and was suitable for the purpose of drinking.
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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".