Consensus recommendations on calf- and herd-level passive immunity in dairy calves in the United States
Bibliographic record
Abstract
Passive immunity in calves is evaluated or quantified by measuring serum or plasma IgG or serum total protein within the first 7 d of age. While these measurements inform about circulating concentrations of this important protein, they are also a proxy for evaluating all of the additional benefits of colostral ingestion. The current individual calf standard for categorizing dairy calves with successful passive transfer or failure of passive transfer of immunity are based on serum IgG concentrations of ≥10 and <10 g/L, respectively. This cutoff was based on higher mortality rates in calves with serum IgG <10 g/L. Mortality rates have decreased since 1991, but the percentage of calves with morbidity events has not changed over the same time period. Almost 90% of calves sampled in the USDA National Animal Health Monitoring System's Dairy 2014 study had successful passive immunity based on the dichotomous standard. Based on these observations, a group of calf experts were assembled to evaluate current data and determine if changes to the passive immunity standards were necessary to reduce morbidity and possibly mortality. In addition to the USDA National Animal Health Monitoring System's Dairy 2014 study, other peer-reviewed publications and personal experience were used to identify and evaluate potential standards. Four options were evaluated based on the observed statistical differences between categories. The proposed standard includes 4 serum IgG categories: excellent, good, fair, and poor with serum IgG levels of ≥25.0, 18.0-24.9, 10.0-17.9, and <10 g/L, respectively. At the herd level, we propose an achievable standard of >40, 30, 20, and <10% of calves in the excellent, good, fair, and poor categories, respectively. Because serum IgG concentrations are not practical for on-farm implementation, we provide corresponding serum total protein and %Brix values for use on farm. With one-third of heifer calves in 2014 already meeting the goal of ≥25 g/L serum IgG at 24 h of life, this achievable standard will require more refinement of colostrum management programs on many dairy farms. Implementation of the proposed standard should further reduce the risk of both mortality and morbidity in preweaned dairy calves, improving overall calf health and welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.013 | 0.005 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".