Benchmarking passive transfer of immunity and growth in dairy calves
Bibliographic record
Abstract
Poor health and growth in young dairy calves can have lasting effects on their development and future production. This study benchmarked calf-rearing outcomes in a cohort of Canadian dairy farms, reported these findings back to producers and their veterinarians, and documented the results. A total of 18 Holstein dairy farms were recruited, all in British Columbia. Blood samples were collected from calves aged 1 to 7 d. We estimated serum total protein levels using digital refractometry, and failure of passive transfer (FPT) was defined as values below 5.2 g/dL. We estimated average daily gain (ADG) for preweaned heifers (1 to 70 d old) using heart-girth tape measurements, and analyzed early (≤35 d) and late (>35 d) growth separately. At first assessment, the average farm FPT rate was 16%. Overall, ADG was 0.68 kg/d, with early and late growth rates of 0.51 and 0.90 kg/d, respectively. Following delivery of the benchmark reports, all participants volunteered to undergo a second assessment. The majority (83%) made at least 1 change in their colostrum-management or milk-feeding practices, including increased colostrum at first feeding, reduced time to first colostrum, and increased initial and maximum daily milk allowances. The farms that made these changes experienced improved outcomes. On the 11 farms that made changes to improve colostrum feeding, the rate of FPT declined from 21 ± 10% before benchmarking to 11 ± 10% after making the changes. On the 10 farms that made changes to improve calf growth, ADG improved from 0.66 ± 0.09 kg/d before benchmarking to 0.72 ± 0.08 kg/d after making the management changes. Increases in ADG were greatest in the early milk-feeding period, averaging 0.13 kg/d higher than pre-benchmarking values for calves ≤35 d of age. Benchmarking specific outcomes associated with calf rearing can motivate producer engagement in calf care, leading to improved outcomes for calves on farms that apply relevant management changes.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".