Veterinarian perceptions on the care of surplus dairy calves
Bibliographic record
Abstract
Both male and female calves that are not required in the dairy herd sometimes receive inadequate care on dairy farms. Veterinarians work with farmers to improve animal care, and farmers often view veterinarians as trusted advisors; however, little is known about the attitudes of veterinarians on surplus calves. This study investigated the perspectives of Canadian cattle veterinarians on the care and management of surplus calves, as well as how they view their role in improving care. We conducted 10 focus groups with a total of 45 veterinarians from 8 provinces across Canada. Recorded audio files were transcribed, anonymized, and coded using thematic analysis. We found that veterinarians approached surplus calf management issues from a wide lens, with 2 major themes emerging: (1) problematic aspects of surplus calf management, including colostrum management, transportation, and euthanasia, and suggested management and structural solutions, including ways to improve the economic value of these calves, and (2) the veterinarian's role in advising dairy farmers on the care of surplus calves, including on technical issues, and more broadly working with farmers to better address public concerns. We conclude that veterinarians are concerned about the care of surplus calves on dairy farms and believe that they have an important role in developing solutions together with their farmer clientele.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".