Teaching Tip: The Development of an Interactive Computer-Based Training Program for Timely and Humane On-Farm Pig Euthanasia
Bibliographic record
Abstract
With extensive knowledge and training in the prevention, management, and treatment of disease conditions in animals, veterinarians play a critical role in ensuring good welfare on swine farms by training caretakers on the importance of timely euthanasia. To assist veterinarians and other industry professionals in training new and seasoned caretakers, an interactive computer-based training program was created. It consists of three modules, each containing five case studies, which cover three distinct production stages (breeding stock, piglets, and wean to grower-finisher pigs). Case study development was derived from five specific euthanasia criteria defined in the 2015 Common Swine Industry Audit, a nationally recognized auditing program used in the US. Case studies provide information regarding treatment history, clinical signs, and condition severity of the pig and prompt learners to make management decisions regarding pig treatment and care. Once a decision is made, feedback is provided so learners understand the appropriateness of their decision compared to current industry guidelines. In addition to training farm personnel, this program may also be a valuable resource if incorporated into veterinary, graduate, and continuing education curricula. This innovative tool represents the first interactive euthanasia-specific training program in the US swine industry and offers the potential to improve timely and humane on-farm pig euthanasia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".