Risks of Domestic Underground Water Sources in Informal Settlement in Kabwe – Zambia
Bibliographic record
Abstract
Informal settlements are a hot spot for disaster risks worldwide. They are characterised by limited provision of basic services. Water being a critical life support resource is not adequately provided. Residents usually rely on unsafe water sources of hand dug wells. Pit latrines are a major facility for sanitary purposes. Further, informal settlements high population density residing in poor housing units is a common characteristics. Risks of underground water pollution are high due to the proximity of sanitation facilities to unprotected shallow wells increasing the possibility of feacal contamination by ecoli and coli form. This paper presents a case of Makululu informal settlement in Zambia. A total of 385 respondents were identified at random while purposive sampling identified key informants. Water samples collected from 12 hand dug wells located close to pit latrines were tested for coli form and ecoli. Testing was done before and after the rainy season to analyse the relationship between pit latrines and wells as well as the relationship with rainfall distribution pattern to ascertain levels of risks. Water was tested to determine the levels of contamination based on the presence of ecoli and coli form. Laboratory results indicated that 90 percent of water consumed in Makululu informal settlement is highly contaminated by faecal coliforms.
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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 | 0.000 |
| 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".