Pig Welfare Assessment: Development of a Protocol and Its Use by Veterinary Undergraduates
Bibliographic record
Abstract
A new approach to teaching welfare assessment is described and has been used with two cohorts of first-year veterinary undergraduates (totaling 515 students). The welfare assessment protocol was devised and trialed using pigs as an exemplar, but its principles are applicable to other species. A robust learning scheme was created, comprising didactic teaching, interactive seminars, practical hands-on training, and computer-based learning. Practical training included a formative virtual assessment of clinical signs of health and welfare using Questionmark Perception, which improved the students' performance significantly. Validation studies are being carried out to establish if acceptable levels of inter-observer variability can be achieved by students conducting on-farm assessments of pig welfare during their extramural studies program. The resulting assessments of welfare will be analyzed in a cross-sectional epidemiological study to identify risk factors for good and poor welfare, and the results will be fed back to participating farmers. This new approach enables veterinary students to learn key transferable skills in the early stages of their education and provides a strong grounding in a holistic approach to animal welfare.
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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 | 0.072 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".