Seroprevalence of Lawsonia intracellularis in different swine populations in 3 provinces in Canada
Bibliographic record
Abstract
Porcine proliferative enteropathy caused by Lawsonia intracellularis is an important enteric disease in swine throughout the world. Information regarding the distribution of this pathogen in Canadian swine herds would be beneficial for the creation of control protocols. Pigs from Ontario, Quebec, and Alberta were tested by using an indirect immunofluorescence assay for antibodies to L. intracellularis. Pig seroprevalence was calculated as the proportion of pigs positive from total pigs tested in the targeted population. Seroprevalence (± standard error [s(χ̄)]) in market hogs in Ontario from farrow-finish (FF) farms and finishing (FIN) farms were significantly different at 77% (s(χ̄) = 7%) and 29% (s(χ̄) = 15%), respectively. Seroprevalence for sows and gilts in FF and farrowing and nursery (FAR + NUR) farms in Ontario were 90% (s(χ̄) = 3%) and 93% (s(χ̄) = 6%), respectively. Seroprevalence in breeding females in Quebec from FF and FAR farms was 82% (s(χ̄) = 5%) and 87% (s(χ̄) = 3%), respectively. Seroprevalence (57%, s(χ̄) = 8%) in finishing pigs in Alberta from FF farms was significantly different from that of multisite (MS) farms and FIN farms, 6% (s(χ̄) = 6%) and 9% (s(χ̄) = 5%), respectively. Lawsonia intracellularis appears to be widespread in Canada and the seroprevalence on FF farms is higher than that on FIN and MS farms, possibly due to the presence of breeding females or management differences.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".