Genetic Susceptibility to Scrapie in Sheep: A Clinically Relevant Theme in Veterinary Medical Education
Bibliographic record
Abstract
RATIONALE FOR THIS STUDY: This article describes and evaluates two molecular biology practical classes based around the theme of genetic susceptibility to scrapie in sheep. These practical classes allow students to experience a range of molecular biology techniques in the context of a clinically based genetic disease. METHODOLOGY: The two molecular biology practical classes described are evaluated in terms of their perceived usefulness to study by first-year veterinary medicine students. The students' ratings are then assessed in relation to the approaches to studying (i.e., deep, strategic, and surface). These dimensions of learning are measured using the 52-item Approaches to Studying Inventory (ASI). RESULTS: The overall ratings from students in relation to both the practical classes were found to be positive. The scrapie genotyping practical was the highest-ranking laboratory-based practical in the first-year curriculum. Ratings in terms of usefulness to studies for both practical classes were found to be significantly higher for students with higher deep learning scores. CONCLUSION: The practical classes described here provide a clinically relevant scenario within which molecular biology concepts and methods can be illustrated to veterinary students. The positive correlation with deep learning is more evident for the scrapie genotyping practical than for the DNA extraction practical. This may reflects the complexity of the former, which is greater both technically and conceptually.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".