Enumeration of coliform bacteria and characterization of Escherichia coli isolated from Staff Club swimming pool in Ile-Ife, Nigeria
Bibliographic record
Abstract
Water recreation, though increasing globally, is strongly associated with infectious diseases. Unexpectedly, artificial water recreation systems e.g. swimming pools account for 90% of these outbreaks. It is therefore essential that pool waters be regularly monitored for deviations from microbial water quality guidelines. To assess the sanitary quality of a club swimming pool in Ile-Ife, Nigeria, I used the multiple-tube fermentation technique to determine the most probable number (MPN) of coliform bacteria in 100 ml of pool water. MPN estimates ranged from 9 to 93 with geometric mean of 38. Escherichia coli was isolated from positive presumptive tubes, indicating recent faecal contamination. The isolate elicited similar biochemical reactions as reference E. coli (ATCC-25922), except that it utilized sucrose and liquefied gelatin, which probably indicates potential pathogenicity. Also, the E. coli isolate was resistant to 13 antibiotics from 9 different classes. Finally, coliform counts and detection of E. coli clearly violates international guidelines. I recommend that pool operators increase water disinfection efficiency and educate the public on the need for improved swimmer hygiene to reduce the risk of recreational water illness transmission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".