Improving passive transfer of immunoglobulins in calves. I: Dose effect of feeding a commercial colostrum replacer
Bibliographic record
Abstract
The objective of this study was to describe the effects of feeding 1 or 2 doses of a commercially available colostrum-derived colostrum replacer (CR) on passive transfer of immunoglobulins (Ig) in newborn dairy calves, including IgG, IgG1, IgG2, IgA, and IgM. Newborn calves were removed from the dam before suckling and randomly assigned to 1 of 3 treatment groups: group 1 were fed 1 package (100 g of IgG) of CR product, group 2 was fed 2 packages (200 g of IgG) of the same CR product, and group 3 was fed 3.8 L of maternal colostrum. All colostrum treatments were fed using an esophageal tube feeder within 2 h of birth. Blood samples collected before colostrum feeding and at 24 h of age were tested for serum total protein and Ig concentrations. Mean 24-h serum total protein (TP) and IgG concentrations were significantly lower for calves in group 1 (n = 24; TP = 4.9 g/dL, IgG = 9.6 mg/mL) compared with calves in groups 2 or 3. There was no difference in 24-h serum TP or IgG concentrations between calves in group 2 (n = 23; TP = 5.5 g/dL, IgG = 19.0 mg/mL) and calves in group 3 (n = 22; TP = 5.7 g/dL, IgG = 20.7 mg/mL). Fifty-four, 100, and 91% of calves in groups 1, 2 and 3 achieved acceptable passive transfer (24-h serum IgG > or =10 mg/mL), respectively. Statistically significant but numerically small differences existed between calves in groups 2 and 3 for some 24-h serum Ig classes and subclasses (mean serum concentrations of IgG2, IgA, IgM) and for the relative percentages of Ig classes and subclasses (IgA, IgM, and IgG as a percentage of total Ig; IgG1 and IgG2 as a percentage of total IgG).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".