High immune responder cows have lower treatment risk and reduced milk loss during highly pathogenic avian influenza H5N1 outbreaks
Bibliographic record
Abstract
The spillover of highly pathogenic avian influenza (HPAI) hemagglutinin type 5 and neuraminidase type 1 (H5N1) into dairy cattle marks the emergence of a new threat to herd health and production efficiency for North American dairy cattle. To address this challenge, this study evaluated whether genetic selection for improved immune response, using Immunity+ GEBVs, can increase resilience to HPAI H5N1 in commercial dairy herds. Treatment records (n = 15,386) and daily milk yield data (n = 650) were obtained from herd management software from 6 large commercial dairy herds in the United States with confirmed HPAI H5N1 outbreaks. The association between Immunity+ GEBV classes and the likelihood of requiring supportive treatment was assessed using binomial logistic regression, while linear models were used to assess the effects of Immunity+ GEBV class on the length of the milk fluctuation period following treatment and total milk loss during this period. Cows with high Immunity+ GEBVs were 24% less likely to require supportive treatment compared with those with low Immunity+ GEBVs (odds ratio = 0.76; 95% CI: 0.64 to 0.9). High immunity cows also experienced significantly lower milk loss (484.46 kg; 95% CI: 440.61 to 528.30 kg) during the post-treatment fluctuation period compared with low immunity cows (618.10 kg; 95% CI: 561.08 to 675.12 kg). Overall, these findings support the value of selecting for enhanced immune response to improve animal health, reduce production losses, and strengthen economic resilience during disease challenges.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".