Effect of Feeding One or Two Doses of a Colostrum-Derived Commercial Colostrum Replacer on Passive Transfer of Immunoglobulin Gin Newborn Dairy Calves
Bibliographic record
Abstract
Colostrum is the single most important management factor determining calf health and survival. Calves experiencing successful passive transfer of colostral immunoglobulins (serum IgG > 10.0 mg/ml) have improved preweaning health and survival, improved growth rate and feed efficiency, reduced age at onset of puberty, reduced age at first calving, and improved first and second lactation milk production. Unfortunately, many producers continue to suffer significant losses related to poor colostrum management. Factors affecting the success of a colostrum management program include 1) Quality of colostrum fed, 2) quantity of colostrum fed, 3) quickness of providing the first colostrum feeding and 4) cleanliness of colostrum (i.e. bacterial contamination). Use of commercial colostrum replacement products may offer multiple advantages, including consistent quality, convenience, and cleanliness. However, it is important to evaluate the ability of commercially available colostrum replacement (CR) products to achieve successful passive transfer of immunoglobulin G (lgG) in calves managed under field conditions. The objective of this study was to describe passive transfer of lgG in newborn calves fed one or two doses ( 100 gm IgG or 200 g IgG) of a commercial colostrum-derived colostrum replacement (CR: Land O'Lakes Colostrum Replacement®, Land O'Lakes Animal Milk Products, St. Paul, MN) or 4 quarts of maternal colostrum (MC).
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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) | 0.001 | 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 | 0.001 | 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".