Short communication: Variation of total immunoglobulin G and β-lactoglobulin concentrations in colostrum and milk from Canadian Holsteins classified as high, average, or low immune responders
Bibliographic record
Abstract
The objective of this study was to evaluate IgG and β-lactoglobulin (β-LG) concentrations in colostrum and milk of Canadian Holsteins (n=108) classified as high (H), average (A), or low (L) for antibody-mediated (AMIR) or cell-mediated immune responses (CMIR) based on estimated breeding values. It was hypothesized that H-AMIR and H-CMIR cows produce colostrum (first milking) and milk (d 5 postcalving) with higher concentrations of IgG and β-LG. Data for IgG and β-LG in colostrum and milk were analyzed independently using mixed linear models. Least squares means were compared using Tukey's test. Cows classified as H-AMIR had higher IgG and β-LG concentrations in colostrum compared with A- and L-AMIR cows; 84% of H-AMIR, 69% of A-AMIR, and 68% of L-AMIR cows had over 5,000 mg/dL IgG in colostrum. No differences in IgG and β-LG concentrations in colostrum were noted among cows ranked on CMIR or in milk of cows ranked on AMIR. β-Lactoglobulin and IgG concentrations were positively correlated in colostrum. Breeding cows for H-AMIR status may reduce failure of passive transfer of IgG in their calves; β-LG may play a role in bovine immune defenses. Colostrum from H-AMIR cows may serve as a more economical feedstock source for manufacturing natural health products.
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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 | 0.001 | 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) | 0.002 | 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".