Improving passive transfer of immunoglobulins in calves. II: Interaction between feeding method and volume of colostrum fed
Bibliographic record
Abstract
The objective of this study was to investigate the effect of method of feeding (nipple bottle vs. esophageal tube feeder) on passive transfer of immunoglobulin (Ig) G when either a large or small volume of colostrum was fed. Newborn bull calves were removed from the dam before suckling and randomly assigned to 1 of 4 colostrum replacer (CR) treatment groups: 1.5 L (100 g of IgG) of CR fed using a nipple bottle (group 1; n = 24); 1.5 L (100 g of IgG) of CR fed using an esophageal tube feeder (group 2; n = 24); 3.0 L (200 g of IgG) of CR fed using a nipple bottle (group 3; n = 24), or 3.0 L (200 g of IgG) of CR fed using an esophageal tube feeder (group 4; n = 25). Blood samples collected at 24 h of age showed that serum IgG levels were significantly greater in calves fed large (3 L) volumes of CR compared with calves fed small (1.5 L) volumes of CR, regardless of feeding method. These differences were attributed to the larger mass of IgG ingested by calves fed 3 L of CR (200 g of IgG) compared with calves fed 1.5 L of CR (100 g of IgG). For calves fed small (1.5 L) volumes of colostrum, serum total protein (TP, g/dL), serum IgG (IgG, mg/mL), acceptable passive transfer rates (APT, %), and apparent efficiency of absorption of IgG (AEA, %) were significantly greater for calves fed with a bottle (TP = 5.3 g/dL; IgG = 12.5 mg/mL; APT = 100%; AEA = 51.1%) compared with calves fed with an esophageal tube feeder (TP = 5.0 g/dL; IgG = 9.8 mg/mL; APT = 41.7%; AEA = 40.5%). However, for calves fed large (3 L) volumes of colostrum, there was no difference in passive transfer indices for calves fed with a bottle (TP = 5.8 g/dL; IgG = 19.7 mg/mL; APT = 100%; AEA = 41.1%) compared with calves fed with an esophageal tube feeder (TP = 5.9 g/dL; IgG = 18.7 mg/mL; APT = 100%; AEA = 39.0%).
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".