A Quantitative Approach to Classifying Holstein Cows Based on Antibody Responsiveness and Its Relationship to Peripartum Mastitis Occurrence
Bibliographic record
Abstract
A quantitative approach was developed to classify Holstein cows and heifers based on phenotypic variation of serum antibody response and to determine associations with peripartum mastitis. Using an index, 136 cows and heifers were classified into high (Group 1), average (Group 2), or low (Group 3) antibody groups following immunization with ovalbumin at wk -8, -3, and 0 relative to parturition. The ranking of groups based on the quantitative index of serum antibody response to ovalbumin were similar for sera and whey antibody such that Group 1 > Group 2 > Group 3. Animals were also vaccinated with Escherichia coli J5 (Rhône Mérieux, Lenexa, KS) at wk -8 and -3 relative to parturition. The ranking of groups for E. coli J5 was similar to that observed for serum and whey antibody to ovalbumin. Serum and whey IgG1 and IgG2 concentrations were measured at wk 0, 3, and 6 but differences between groups were not significant. There was no occurrence of mastitis for Group 1 animals in two of the herds. In contrast, Group 1 animals from the third herd had the highest occurrence of mastitis; however, these cases all occurred in first-parity heifers. According to pooled data across all herds, Group 3 animals had the highest occurrence of mastitis. Heritability estimates of serum antibody response to ovalbumin varied between 0.32 to 0.64 depending on week relative to parturition. Heritability estimates of serum antibody response to E. coli J5 also varied between 0.13 to 0.88 depending upon week relative to parturition. These results indicate that high peripartum antibody may be beneficial in some herds.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".