Influence of Physico-Chemical Parameters on the Seasonal Dynamic of <i>Salmonella </i>spp Isolated from Urban Streams in Yaounde (Cameroon)
Bibliographic record
Abstract
As water resources in urban areas are becoming increasingly degraded, due in large part to poor sanitation, a study has been was conducted to examine the influence of physicochemical parameters and seasonal variation on the distribution of the enterobacteria Escherichia coli and Salmonella spp that have been isolated from the urban streams in the city of Yaounde. Bi-monthly water samples were collected from nine rivers during 12 months (April 2010 to March 2011). The isolation of bacterial germs was done according to the classical method. Physicochemical parameters were analyzed according to Standard methods. Salmonella spp was detected all over the studied year with a high prevalence of 49.4%. This prevalence varies from one season to another. Escherichia coli ranged between 2.5 x 103 to 67.1 x 103UFC/100ml with highest prevalence observed during the long dry season. Physicochemical parameters revealed neutral to slightly alkaline waters (pH6.7 – 8.8), with low mineralization (EC= 126 – 743 µS/cm). Dissolved oxygen was generally less than 4 mg/l. Physicochemical parameters also showed temporal homogeneity in most of the variables (pH, EC, TDS, total hardness, alkalinity, Na, K, Mg, Ca). None of the physicochemical environmental variables analyzed had any specific influence on the presence of Escherichia coli or Salmonella. Furthermore, there was no significant correlation between the presence of Escherichia coli and Salmonella spp, as the sources of Salmonella spp contamination are probably different from those of E. coli. The observed pollution of rivers is related to the large anthropogenic activities in particular, the multiplicity of small farming closed to markets and houses, and husbandry activities along the streams which are important sources of organic matter. These rivers constitute a pool of Salmonella that can be easily disseminated in to different ecological systems and therefore represent a serious health risk for people who may come into direct or indirect contact with this pathogen.
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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 | 0.000 |
| 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".