The Relationship Between Milk Production and Antibody Response to Ovalbumin During the Peripartum Period
Bibliographic record
Abstract
Suboptimal innate and immune mechanisms of host resistance during the peripartum period may contribute to increased incidence of mastitis. To evaluate associations between antibody response to ovalbumin and milk production during the peripartum period, 136 Holstein cows and heifers from three herds with known antibody response profiles, were evaluated for projected 305-d milk, protein, and fat yield. Using a previously described index (Wagter et al., 2000), cows were quantitatively classified based on their profile of antibody response to ovalbumin into high, average, or low antibody response groups. The single-effect antibody response group contributed significantly to variation in fat and protein yield, but not milk yield. The interaction between antibody response and parity significantly contributed to the variation in milk, fat, and protein yields; therefore the effects of group were reported on a within-parity basis. Among first-parity cows, low responders had a higher fat and protein yield than high or average antibody responder animals. Among older cows (parity 3 or greater) milk yield was significantly higher for those in the high antibody response group compared with average and low response groups. However, no significant differences in fat or protein yields were observed between high and low antibody response groups. These results suggest the possibility to select cows for enhanced immune response with no adverse effects on yield. That first-parity cows with low antibody response produce more fat and protein may be offset by the fact that mastitis occurrence was highest in this group in two out of three herds investigated. Selection for high immune response may prove beneficial to herd life by maintaining optimal yield, yet minimizing occurrence of disease.
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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 | 0.000 |
| 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".