A survey of practices and attitudes around cull cow management by bovine veterinarians in Ontario, Canada
Bibliographic record
Abstract
The removal (culling) of dairy cows from the farm is a regular and required management practice. The main objectives of this study were to describe the involvement of bovine veterinarians in Ontario, Canada, in dairy cull cow management decisions, their expectations of current producer practices, and their perspectives on welfare issues and best management practices for cull dairy cows. Between February and May 2021, a province-wide survey was conducted among all members of the Ontario Association of Bovine Practitioners (OABP), with questions addressing veterinarians' background and demographics, veterinary clinic characteristics, cull cow management, down cow management, and learning preferences. The response rate for the survey was 25.1%, with 45 responses meeting all eligibility criteria. Dairy veterinarians would like to have greater involvement in cull cow management; when asked for desired involvement in culling decisions, 57.5% of veterinarians would like to be involved in culling decisions most of the time or always. Most (70.0%) veterinarians believed that cull cow welfare has improved over the last decade. However, most respondents also identified at least one area of management (farm of origin, transportation, auction, slaughter) as currently being a high risk to cull cow welfare. Given the frequency of their visits, trust by dairy producers, and knowledge, herd veterinarians are among those in the best position to improve the care of cull dairy cows. Information generated from this survey can be used to inform the development of improved decision-making tools for culling cows, and this, along with increased veterinarians' involvement in cull cow management, could improve the economic and welfare outcomes of culling decisions for dairy producers.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".