Microbial and Chemical Risk Assessment of River Bukuruwa Used as Drinking Water by Farming Communities
Bibliographic record
Abstract
Water is very vital for the sustenance of life and according to World Health Organization; potable water should be free from any health risk. However due to in adequate supply of potable water, many rural dwellers have no choice but to depend on streams and rivers as source of drinking water which becomes the vehicle for the transmission of infections due to a host of microorganisms both pathogenic and non-pathogenic present in these water bodies. This study therefore quantified and assessed the microorganisms present in river Bukuruwa in Techiman in the Brong Ahafo Region of Ghana. Water was aseptically collected into sterile sampling bottles with caps and analyzed in the laboratory through standard microbiological protocols. Microbial organisms such as Feacal coliforms, E.coli and Salmonella spp. as well as physicochemical parameters such as pH, conductivity, turbidity, Sulphate (SO42-), Fluoride (F-), Phosphates (PO43-), Nitrites (NO2-) and Nitrates (NO3-) were quantified and assessed, respectively. The highest load of faecal coliforms in the river water at a point in this study was found to be 9 x 102 per 100 ml and pH values ranging from 5.31 to 6.84 with variations within the sampling points. The observational survey revealed human beings competing with farm animals for the same source of water and again farming activities were carried out very close to the river banks which may increase the chance of infections which needs serious attention by policy makers and implementers.
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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 | 0.001 |
| Research integrity | 0.001 | 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".