Cache a Killer: Cache Valley virus seropositivity and associated farm management risk factors in sheep in Ontario, Canada
Bibliographic record
Abstract
Cache Valley virus (CVV) disease is a mosquito-borne zoonosis endemic to North America. CVV disease is reported most often in sheep, causing lethal congenital deformities. There are limited data on CVV in Ontario, which is the largest sheep producing province in Canada. This study aimed to determine CVV seroprevalence in Ontario sheep flocks and investigate farm management factors associated with CVV exposure. A cross-sectional study was performed including 364 mature ewes across 18 farms selected from the five largest sheep districts in the province. A questionnaire was administered at each farm to determine farm management practices pertinent to the flock and ewes specifically sampled. Mixed multivariable logistic regression with a random effect for farm was conducted to assess associations between CVV seropositivity (outcome variable) and farm management risk factors (predictor variables). CVV seroprevalence was 33.2% in individual ewes (95% CI: 28.4%-38.1%) as determined by a virus neutralization assay with a titre > 4. Sixteen of the eighteen flocks (88.9%) had at least one CVV seropositive ewe. Increased age, smaller flock size, and sheep housing near wetlands, lakes, or ponds were found to be significantly associated with higher odds of CVV seropositivity. These findings are valuable in guiding breeding practices and housing during mosquito season to minimize infection and, ultimately, CVV disease in the flock.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".