Physical, chemical and microbiological characterization of processed drinking water in central Nepal: current state study
Bibliographic record
Abstract
Abstract This study was undertaken to analyse processed bottled water for drinking water quality. Altogether 50 water samples of different brands were randomly collected from public places in Kathmandu valley. The samples were analysed for physical (turbidity, pH and electrical conductivity), chemical (iron, manganese, arsenic, cadmium, chromium, lead, ammonia, fluoride, chloride, sulphate, copper, total hardness, calcium, mercury and aluminum) and microbiological (fecal coliform and total coliform) parameters. The results revealed that >300 CFU/100 mL of Escherichia coli (E. coli) (fecal coliform) and total coliform (TC) bacteria were counted in 76 and 92% samples, respectively. The bacterial population was beyond the limit of the Department of Food Technology and Quality Control (DFTQC) (0 CFU/100 mL of water). Chemical parameters analysed for fluoride (0.5–1.5 mg/L) and ammonia (1.5 mg/L) exceeded the DFTQC values. The range of fluoride estimated was 0.001–2.37 mg/L and the maximum concentration of ammonia was 4.66 mg/L. Most of the processed water crossed the threshold standard of E. coli and TC bacteria and may pose a risk if used for drinking purposes. Therefore, to minimize the public health risk of contaminated water, scientific methods and standards of purification should be followed during the process, production, storage, and delivery of processed 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".