Infestation rates, seasonal distribution, and genetic diversity of ixodid ticks from livestock of various origins in two markets of Yaoundé, Cameroon
Bibliographic record
Abstract
Little is known about the impact of ticks on livestock and humans in Cameroon. This study aimed to determine the prevalence, seasonal variation, and genetic diversity of hard ticks in the country. Ticks were collected during a cross-sectional survey on domestic livestock in two markets of Yaoundé in 2019 and 2020 and identified using morphological keys, 16S ribosomal DNA, (16S rDNA), and the cytochrome c oxidase subunit 1 (Cox1) genes. The infestation rates were 39.18%, 11.53%, and 2.74% in cattle, sheep, and goats respectively. Three genera of ticks were identified, Rhipicephalus, Amblyomma, and Hyalomma comprising eleven tick species. The main species were Rhipicephalus decoloratus (30.25%), R. microplus (24.43%), and Amblyomma variegatum (12.96%). Rhipicephalus spp. (81.31%) and Amblyomma variegatum (51.54%) were abundant during the rainy season, while Hyalomma spp. (83.86%) during the dry season (p-value <0.00001). Cox1 and 16S rDNA analysis showed a high level of genetic diversity among tick species with sequences close to those observed across Africa. Phylogenetic analysis revealed that our R. microplus belong to clade A and we identified R. sanguineus s.l. as R. linnea. This study shows a high tick infestation rate in cattle, while low in small ruminants with an extensive diversity of tick species, including several known vectors of important tick-borne diseases.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".