Prevalence of Escherichia Coli O157 strain isolated from dairy cattle manure in Bogor, Indonesia
Bibliographic record
Abstract
In Bogor, Indonesia, where dairy farming is prevalent, dairy cattle manure serves as a significant reservoir for numerous bacteria, including the potentially harmful Escherichia coli. This study intends to evaluate the prevalence of E. coli O157, a particularly pathogenic strain, in 25 manure samples collected from different dairy farms in the Kebon Pedes area of Bogor. This study was conducted from June 2023 to December 2023. Using the Global Tricycle Surveillance ESBL E. coli WHO 2021 method for isolation and identification and employing the SYBR Green real-time polymerase chain reaction (qPCR) technique for E. coli O157 detection, the results revealed that out of 25 E. coli positive samples, three tested positive for E. coli O157, indicating a prevalence rate of 12% of this specific strain. The occurrence of this pathogenic variant suggests a noteworthy finding in understanding the microbial landscape of dairy cattle manure in Bogor. The presence of E. coli O157 in dairy cattle farms underscores the potential for continuous bacterial transmission to the environment, highlighting the importance of ongoing monitoring and comprehension of pathogenic strains in agricultural settings for the development of effective public health strategies. Improved manure management protocols and regular surveillance programs can be used as targeted interventions to mitigate the risks of E. coli O157 transmission from dairy farms.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".