A field trial evaluation of the prophylactic efficacy of amitraz-impregnated collars against canine babesiosis (Babesia canis rossi) in South Africa
Bibliographic record
Abstract
South African canine babesiosis caused by Babesia canis rossi is a common clinical disease in dogs in South Africa and remains a significant cause of domestic dog mortality. To determine whether tick-repellent, 9% amitraz-impregnated tick collars (Preventic-Virbac) could prevent tick-borne exposure to B. canis rossi, 50 dogs were assigned to two groups. Group 1 (20 dogs), polymerase chain reaction (PCR)--and reverse line blot (RLB)-negative for B. canis rossi, were fitted with amitraz collars and blood samples collected monthly, over a 6-month period, and analysed for B. canis rossi. Group 2 (30 dogs) included 5 dogs selected on a month-by-month basis from a population of dogs from the same geographical area as the group 1 dogs, but with no history of previous tick control, which were blood-sampled together with the treatment group and analysed for B. canis rossi by PCR and RLB, to serve as the control group. Eight of the 30 control dogs (26.6%) were PCR/RLB positive for B. canis rossi, indicating high pathogen exposure during the trial period. All twenty of the treatment group dogs remained negative for B. canis rossi throughout the 6 months of the trial. These results suggest that the use of amitraz-impregnated collars had a significant effect on reducing infection with B. canis rossi.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".