Addition of gut active carbohydrates to colostrum replacer does not improve passive transfer of immunoglobulin G in Holstein dairy calves
Bibliographic record
Abstract
The primary objective of this study was to investigate the effects of supplementing a commercial colostrum replacer (CR) with gut active carbohydrates (GAC) on passive transfer of IgG in commercial dairy calves. A secondary objective was to evaluate the effect of treatment on preweaning health and growth. A total of 240 newborn Holstein dairy calves on a commercial dairy farm were enrolled in this study. Newborn heifer and bull calves were weighed and then randomly assigned to either the treated group [GAC: 30g of GAC mixed into 1.5 doses (150g of IgG) of commercial colostrum replacer; n=119] or the control group [CON: 1.5 doses (150g of IgG) of CR; n=121]. The assigned CR treatment was fed within 3.5h of birth using an esophageal tube feeder. Venous blood samples were collected at 0 and 24h of age and used to measure serum IgG (mg/mL) and serum total protein (g/dL) concentrations and to estimate the apparent efficiency of absorption of IgG (%). The 129 heifers calves enrolled (CON=60; GAC=69) were also followed until weaning to assess the effect of GAC addition on preweaning health and growth. Multivariable linear regression showed that the addition of GAC to CR did not influence passive transfer of IgG, as measured by apparent efficiency of absorption at 24h of age (CON=54.0 vs. GAC=54.3%), serum IgG (CON=20.3 vs. GAC=20.2mg/mL), and serum total protein (CON=5.69 vs. GAC=5.68g/dL). Although study sample sizes were not originally derived to evaluate health outcomes, treatment had no effect on weight gain or incidence of health events (diarrhea, pneumonia, mortality) for heifer calves between birth and 7 wk of age.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".