Prevalence of Cryptosporidia, Eimeria, Giardia, and Strongyloides in pre-weaned calves on smallholder dairy farms in Mukurwe-ini district, Kenya
Bibliographic record
Abstract
AIM: Gastrointestinal diseases are among the leading causes of calf morbidity and mortality in Kenya and elsewhere. This study was undertaken to determine the prevalence of Cryptosporidia, Eimeria, Giardia, and Strongyloides in calves on smallholder dairy farms (SDF) in Mukurwe-ini District, Nyeri County, Kenya. These infections have been associated with economic losses by decreased growth rates, decreased productivity, and increased susceptibility to other diseases. MATERIALS AND METHODS: An observational study was conducted on 109 farms in Mukurwe-ini District, Nyeri County, Kenya, where 220 calf fecal samples (each calf at 4 and 6 weeks of age) from 110 calves (1 set of twins) were collected and analyzed for Cryptosporidia, Eimeria, Giardia, and helminth parasites. RESULTS: Eimeria oocysts, Cryptosporidia oocysts, and Strongyloides eggs were detected in the fecal samples examined, but no Giardia cysts were found. The overall period prevalence of Eimeria, Cryptosporidia, and Strongyloides was 42.7% (47/110), 13.6% (15/110), and 5.4% (6/110), respectively. The prevalence at 4 weeks of age for Eimeria, Cryptosporidia, and Strongyloides was 30.0% (33/110), 8.2% (9/110), and 3.7% (4/109), respectively, while the prevalence at 6 weeks of age was 20.2% (22/109), 6.5% (7/107), and 2.7% (3/110), respectively. There was, however, no significant difference in the prevalence at 4 and 6 weeks (p>0.05). CONCLUSION: Findings from this study show that Eimeria, Cryptosporidia, and Strongyloides, are prevalent in the study area and indicate the need to adopt optimal management practices to control infections in calves.
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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 | 0.000 |
| 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".