Passive Immunity in Ontario Dairy Calves and Investigation of Its Association with Calf Management Practices
Bibliographic record
Abstract
Adequate passive transfer of maternal immunoglobulin is important for optimal health and performance in newborn dairy calves. From June to October 2003 and January to April 2004, blood samples were collected from 961 dairy calves 0 to 8 d of age on 11 farms in southwestern Ontario. This was followed by a second study conducted from May to October 2004, in which similar samples were taken from 422 calves up to 8 d of age on 119 dairy farms throughout southern Ontario. For each sample collected, serum refractometry was used to evaluate serum total protein (TP) as a measure of passive transfer of maternal immunity. During each study, producers were asked to provide information on calf management practices, including details of colostrum feeding. Data were analyzed to assess the levels of maternal immunity present in the calves, and to investigate whether these were associated with any calf management or colostrum feeding practices used on the farms. Serum TP readings ranged from 3.5 to 9.8 g/dL. Controlling for any effects of variation between farms, we found no statistically significant difference in serum TP levels, or risk of failure of passive transfer (FPT), between heifer and bull calves. The odds of FPT in calves on farms where more than 75% of cows were usually allowed to remain with their calves for more than 3 h after calving were significantly higher than the odds of FPT in calves on farms where dams and calves were separated within 3 h of the birth. Furthermore, an increased volume of colostrum given to calves within 6 h of birth was significantly associated with a reduced risk of FPT in calves. Information from this work provides valuable insight into the efficiency of passive transfer in newborn dairy calves in southern Ontario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".