Groundwater quality evaluation for drinking purpose using water quality index in Kathmandu Valley, Nepal
Bibliographic record
Abstract
Groundwater is a significant source of drinking water in Kathmandu Valley of Nepal. The study aims to evaluate the groundwater quality in terms of water quality index. We compared the physicochemical and microbial parameters of 159 groundwater samples. The study showed that conductivity, hardness, chloride, and nitrate were found to be significantly higher in well water and ammonia was found to have significantly higher concentrations in boring water. The Spearman’s rank correlation coefficient demonstrated a positive correlation between conductivity and hardness, turbidity and iron, total hardness and chloride, and ammonia and arsenic. The drinking water quality parameters including pH, conductivity, turbidity, chloride, hardness, iron, ammonia, total coliform, and Escherichia coli count exceeded National Drinking Water Quality Standards, 2022 by 7.55%, 22.01%, 50.94%, 1.26%, 3.77%, 69.81%, 41.51%, 93.71%, and 47.17% samples, respectively. The water quality index showed that 38.36% of groundwater samples fall under grade-E which requires proper treatment before use. Linear regression revealed that with the increase in turbidity and iron, the water quality index also increases. The principal component analysis identified hardness, iron, conductivity, and nitrate as the major variables governing groundwater quality with no significant difference between well and boring water. Results suggest an urgent need for appropriate treatment of groundwater to mitigate pollutants.
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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) | 0.000 | 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".