Efficacy of feeding a lacteal-derived colostrum replacer or pooled maternal colostrum with a low IgG concentration for prevention of failure of passive transfer in dairy calves
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of a lacteal-derived colostrum replacer (LDCR) for the prevention of failure of passive transfer of immunity (FPT) in calves with that of pooled maternal colostrum (MC). DESIGN: Randomized field trial. ANIMALS: 568 heifer calves from 1 California dairy. PROCEDURES: Calves were randomly allocated to 1 of 2 treatment groups and fed 2 doses (200 g of IgG) of an LDCR or 3.8 L of pooled MC. From each calf, blood samples were collected before and approximately 24 hours after treatment. Serum IgG and total protein (TP) concentrations were quantified with standard methods, and the apparent efficiency of IgG absorption was calculated. RESULTS: At 24 hours after treatment, mean serum TP and IgG concentrations were significantly lower for calves fed pooled MC (TP, 4.77 g/dL; IgG, 7.50 g/L), compared with those for calves fed the LDCR (TP, 5.50 g/dL; IgG, 15.15 g/L). Calves fed the LDCR were 95% less likely to develop FPT (OR, 0.05; 95% confidence interval, 0.03 to 0.08) than were calves fed pooled MC. However, the mean IgG concentration in the pooled MC fed during the study (21.1 g/L) was substantially lower than that (64.3 g/L) determined for representative samples of pooled MC from other southwestern US dairies during a national survey. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that, on this particular dairy, calves fed an LDCR were at less risk of developing FPT than were calves fed pooled MC. The LDCR evaluated was a viable alternative for the prevention of FPT in calves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".