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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.022 | 0.001 |
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; both teacher heads agree on what is shown here.
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".