Good Practices in Animal Research: A Web-Based Platform for Training in Laboratory Rodent Experimental Procedures
Bibliographic record
Abstract
The advancement of technology has revolutionized education, particularly through video-based learning. In response, the Good Practices in Animal Research (BPEA, "Boas Práticas em Experimentação Animal" in Portuguese) platform was established as a contemporary educational tool for training in laboratory rodent experimental techniques. Designed to replace traditional animal-centered teaching methods, BPEA provided scientifically accurate video content tailored for veterinary medicine students. Mastering animal handling skills is crucial for veterinary students, and BPEA addressed this by offering video demonstrations of experimental procedures, allowing visualization and confidence-building before live animal interaction. The platform's video library covered diverse procedures, such as substance administration and blood collection, accompanied by protocols, images, and diagrams for enhanced learning. The intuitive menu facilitated easy navigation, enabling students to access content aligned with their needs. Website traffic analysis demonstrated widespread usage, with users from Portuguese-speaking countries being prominent. Integration of BPEA into the Laboratory Animal Science course at the University of São Paulo garnered positive student feedback, highlighting its value as a supplementary resource for bridging theoretical and practical learning. While BPEA showed promise in promoting ethical teaching practices and reducing animal stress, it could not entirely replace hands-on training. A balanced approach between video-based learning and live demonstrations is necessary for a comprehensive learning experience. In conclusion, BPEA was a valuable resource contributing to laboratory animal science education, aligning with ethical standards and benefiting students, researchers, and animal care professionals. Continuous improvements based on feedback make the platform a dynamic tool for future advancements in laboratory animal science education.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.021 |
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".