Reduction of microbial contamination using household techniques in rural area of India
Bibliographic record
Abstract
Groundwater is the major source of domestic water for people residing in Amingaon – a rural area in North Guwahati, Assam, India. However, it contains an excessive amount of dissolved iron (Fe) (11.3 mg/l); hence, the people resort to the use of indigenous household groundwater filter (IHGF) units. A survey of different variants of IHGF units within a radius of 5–6 km from the campus of Indian Institute of Technology Guwahati reveals that the filter medium – that is, river sand, gravel, wooden charcoal and mesh – are arranged in different combinations layerwise and bounded in plastic buckets, reinforced cement concrete rings and tin (Sn) containers. An IHGF unit is selected for performance monitoring. The iron concentration in the filtered water is within the permissible limits (0.3 mg/l), but when the water is assessed for pathogenic contamination using the hydrogen sulfide (H2S) strip test, it is found to be contaminated with pathogens, making it unfit for drinking. Efforts have been made to reduce the faecal microbial contamination present in the filtered groundwater using household treatment options such as additional filtration using folded cloth, use of Ocimum tenuiflorum (tulsi) and copper (Cu) utensils and solar disinfection (Sodis). Sodis is found to be the most effective in reduction of faecal microbial contamination present in the filtered 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".