Prevalence and impacts of animal trypanosomosis in Vogan sheep and Djallonke sheep in southern of Togo
Bibliographic record
Abstract
The aim of this study was to estimate the prevalence of animal trypanosomosis and its impact on the Packed red Cell Volume (PCV) in Vogan sheep (VS) and Djallonke sheep (DS) in two administrative regions of southern-Togo (Maritime and Plateau Regions). A total of 206 samples (104 VS and 102 DS) were analysed by microscopic observation of buffy coat, PCR and indirect ELISA. Using the three diagnostic techniques, the prevalence was 24.51% in Djallonke sheep and 20.19% in Vogan sheep with a clear predominance of Trypanosoma vivax infection. Geographical location (canton) influenced significantly the prevalence of trypanosome infections and the PCV; the highest recorded prevalence was obtained in Dagbatchi and Sevagan locations, associated with the lowest PCV. No significant difference was observed between PCV of VS and those of DS in Maritime Region. We concluded that even with a phenotype of Sahelian sheep, VS is well adapted in this area and has developed a certain degree of trypanotolerance similar to DS. However, Djallonke sheep in the Maritime Region (Vo Prefecture) might have become less trypanotolerant comparatively to those originating from the Plateau Region. These results could be used to update the epidemiological situation of trypanosomosis in this region and showed that the sheep genetic improvement strategies should take into account animal trypanosomosis.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".