Animal Handling as an Integrated Component of Animal and Veterinary Science Programs at the University of Queensland
Bibliographic record
Abstract
Students in animal science and veterinary science at the University of Queensland (UQ) have similar introductory courses in animal handling in year 1 of their degree programs. Veterinary students take animal-handling instruction in farm and companion animals, whereas animal science students are instructed in handling farm animals, horses, and rodents. Veterinary students are introduced to rodents, and animal science students to dogs and cats, in subsequent years of the curriculum. Both cohorts receive additional training, with clinical emphasis for veterinary students in years 3, 4, and 5 of their five-year curriculum. The introductory course is well received by students; both student cohorts appreciate the opportunity provided and the effort that goes into the animal-handling classes. Undergraduates realize that acquiring animal-handling skills will increase their proficiency in their subsequent careers; veterinary graduates recognize that their handling prowess will give their clients confidence in their abilities. Most clients cannot judge the competence of a veterinarian's diagnosis or treatment but will judge their ability based on their handling skills. Ongoing practice allows students to become competent in animal handling.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".