Condition of surplus dairy calves during marketing: A cross-sectional study
Bibliographic record
Abstract
In North America, surplus dairy calves are sold soon after birth and often marketed through a third party (e.g., live auction, livestock dealer) before entering veal or dairy beef production. Previous work has demonstrated that a percentage of calves have failed transfer of passive immunity (FTPI) and clinical signs of disease on arrival at calf-raising facilities, but little is known regarding calf condition during marketing. The objectives of this cross-sectional study were to (1) estimate the prevalence of FTPI and poor health outcomes in surplus calves on arrival at livestock dealers, and (2) investigate the association between calf-level variables (body weight, sex, source) and health outcomes. Two livestock dealers in Ohio were visited 2 to 3 times per week, with approximately 28 calves enrolled in the study per visit for a total of 1,119 calves. One blood sample per calf was obtained to evaluate FTPI by measuring serum total protein concentrations (using a cutoff <5.1 g/dL). Calves were clinically evaluated for signs of arthritis, broken ribs or tail, dehydration, depression, diarrhea, fever, navel inflammation, and respiratory disease by 2 observers; health outcomes were dichotomized using clinically relevant cut points. Descriptive statistics were used to estimate the prevalence of calves with poor health outcomes. Multivariable logistic regression models were built to investigate the effect of body weight, sex, and source on health outcomes. Nineteen percent (206/1091) of calves had FTPI. Calves purchased from a third party were less likely to have FTPI compared with those purchased directly from the dairy farm (OR: 0.5; 95% CI: 0.3 to 0.8). Upon clinical examination, 69.2% (769/1112) of calves were considered dehydrated, 26.3% (293/1112) had navel inflammation, and 7.3% (82/1118) were depressed. Males were more likely than females to have navel inflammation (OR: 1.8; 95% CI: 1.2 to 2.7). This research highlights the opportunity for continued improvements in surplus calf care, including colostrum management, for female and male dairy calves. Strategies to mitigate dehydration and navel inflammation are also encouraged before calves enter veal or dairy beef production.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".