Cull Dairy Cow Management in Ontario - Perspectives and Practices by Producers' and Veterinarians
Bibliographic record
Abstract
Over the last decade, the management of cull cows in the dairy industry has been a point of interest for improvement.Regardless of the reason, the removal of cows from dairy herds is referred to as culling. To address welfare concerns, the management of these animals has been a source of recent regulatory changes by government and industryorganizations. Yet, previous research indicates that cows of poor fitness continue to be leaving farms for marketing in the province. This research project aimed to assess how individuals are making culling decisions, perspectives of cullcow management strategies, familiarity with regulatory changes for cull cows, and learning preferences. The datawere collected using two surveys administered separately to Ontario farmers and bovine veterinarians in the last year. The findings highlight the variety of challenges in the management of cull cows, one being the differences in access to destinations for cull cows like shipment directly to slaughter facilities. Additionally, findings demonstratedmissed areas of communications between producers and veterinarians, and the gaps in knowledge regardingregulations and the journey of cull cows. Next steps include further consultation with farmers through focus groups to identify the best strategies for training to improve cull cow welfare and regulatory compliance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".