Farm-level prevalence and risk factors for detection of hepatitis E virus, porcine enteric calicivirus, and rotavirus in Canadian finisher pigs.
Bibliographic record
Abstract
Hepatitis E virus (HEV), norovirus (NoV), and rotavirus (RV) are all hypothesized to infect humans zoonotically via exposure through swine and pork. Our study objectives were to estimate Canadian farm-level prevalence of HEV, NoV [specifically porcine enteric calicivirus (PEC)], and RV in finisher pigs, and to study risk factors for farm level viral detection. Farms were recruited using the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) and FoodNet Canada on-farm sampling platforms. Six pooled groups of fecal samples were collected from participating farms, and a questionnaire capturing farm management and biosecurity practices was completed. Samples were assayed using validated real-time polymerase chain reaction (RT-PCR). We modeled predictors for farm level viral RNA detection using logistic and exact logistic regression. Seventy-two herds were sampled: 51 CIPARS herds (15 sampled twice) and 21 FoodNet Canada herds (one sampled twice). Hepatitis E virus was detected in 30/88 farms [34.1% (95% CI 25.0%, 44.5%)]; PEC in 18 [20.5% (95% CI: 13.4%, 30.0%)], and RV in 6 farms [6.8% (95% CI: 3.2%, 14.1%)]. Farm-level prevalence of viruses varied with province and sampling platform. Requiring shower-in and providing boots for visitors were significant predictors (P < 0.05) in single fixed effect mixed logistic regression analysis for detection of HEV and PEC, respectively. In contrast, all RV positive farms provided boots and coveralls, and 5 of 6 farms required shower-in. We hypothesized that these biosecurity measures delayed the mean age of RV infection, resulting in an association with RV detection in finishers. Obtaining feeder pigs from multiple sources was consistently associated with greater odds of detecting each virus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".