On‐farm euthanasia practices and attitudes of commercial meat rabbit producers
Bibliographic record
Abstract
Appropriate and timely on-farm euthanasia is the responsibility of the producer, working together with their herd veterinarian. Unfortunately, validated methods for euthanasia of commercial meat rabbits are lacking and there are few educational materials available for producer training. Because euthanasia must be performed in a timely fashion to minimise suffering, it is critical to ensure that methods used are aesthetic, humane and effective. We surveyed Canadian meat rabbit producers for current on-farm euthanasia practices as well as attitudes towards the methods they employed and thoughts on novel euthanasia techniques. Surveys were distributed with a response rate of 26 per cent (n=26). Blunt force trauma was the most common euthanasia method used (54 per cent), followed by assisted manual cervical dislocation (31 per cent). Half of producers admitted to not having a euthanasia method in place for all age groups of rabbits, instead electing to let sick and injured rabbits die on their own. While some producers reported feeling highly skilled and satisfied with their current euthanasia method, 58 per cent reported concerns with their current method and 42 per cent desired alternative methods to be developed. Responses to additional questions on training and awareness of euthanasia resources indicated that veterinarians are not part of on-farm euthanasia planning for meat rabbits.
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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 | 0.003 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".