Using salivary anti-CarLA IgA as a tool to manage gastrointestinal parasitism in Canadian pastured sheep *
Bibliographic record
Abstract
Identifying sheep with superior immunity to gastrointestinal nematodes (GIN) is of great interest for Canadian sheep producers. Measuring the concentration of salivary immunoglobulin A (IgA) against carbohydrate larval antigen (CarLA) found on all third-stage GIN larvae has shown promise in New Zealand sheep, where salivary anti-CarLA IgA exceeding 1.0 U/ml has been associated with 20–30 % lower fecal egg counts (FEC). However, it remains unclear whether these findings translate to other sheep-producing regions, especially northern climates where GIN epidemiology and flock management differ greatly from New Zealand. Accordingly, this study investigated salivary anti-CarLA IgA testing in sheep under Canadian conditions, and was approved by the University of Guelph Animal Care Committee (#4762). In 2022, an average of 25 ewe lambs per farm were randomly selected on 18 farms in Ontario, Canada, after grazing pasture for a minimum of 60 consecutive days. Body condition, fecal consistency, FAMACHA© score, liveweight, hematocrit, FEC, and salivary anti-CarLA IgA concentration were recorded for each study animal after the grazing season. Study animals returned to pasture in 2023 and were re-sampled 4 weeks after turnout. Multivariable linear regression demonstrated that the salivary anti-CarLA IgA response in 2022 predicted the salivary anti-CarLA IgA response in 2023 (β = 0.213; p < 0.001). In addition, salivary anti-CarLA IgA in 2022 was negatively associated with FEC in 2023 (β = - 0.167; p = 0.025). These data indicate that salivary anti-CarLA IgA measurements appear to be helpful for identifying sheep with superior immune responses to GIN in Canada.* Title in ESDA Proceedings: Evaluating the use of salivary anti-CarLA IgA testing to reduce gastrointestinal parasitism in Canadian pastured sheep
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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 | 0.001 | 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".