Short communication: Variation in production parameters among Canadian Holstein cows classified as high, average, and low immune responders
Bibliographic record
Abstract
Dairy cattle evaluated for immune responses and identified as high responders are known to have a lower occurrence of economically important diseases, including mastitis, metritis, ketosis, and retained placenta. These high immune responders have also been shown to make more antibody following vaccination and to have improved milk and colostrum quality. Therefore, breeding for improved immune response is expected to have several benefits in the dairy industry. However, a concern of such an approach to improve animal health is the potential cost of lost production due to an allocation of host resources to mount a robust immune response. The objective of this study was to evaluate early- and late-lactation production parameters in cattle classified as having high, average, or low estimated breeding values (EBV) for cell-mediated (CMIR), antibody-mediated (AMIR), and overall immune responses. A total of 561 cows from 6 herds were phenotyped for immune response and ranked based on EBV for CMIR and AMIR. A linear animal model was used to evaluate differences in milk, fat, and protein yields among immune response groups, and a regression analysis was conducted based on immune response EBV. Overall, no difference in production parameters was found based on immune response rank; however, some positive relationships with immune response EBV were found, suggesting that breeding for enhanced immune responsiveness as a prophylactic approach to improve animal health would not come at the cost of lost production.
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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 | 0.001 |
| 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".