Animal use control in teaching and research.
Bibliographic record
Abstract
The animal experimentation regulation is based on the ethical concern regarding not to cause animal suffering, expressed by social movements in favour of the animal protection, legislation on this subject, Animal Use Ethics Committees, amongst others. The objective of this review was to study the control of animal use in teaching and research in some countries, randomly selected, comparing them to the situation in Brazil and the State of Parana. The control process is performed by different institutions in different countries: the President and Parliament in South Africa; autonomous institution in Canada; Department of Agriculture in United States; Ministry of Education, Culture, Sports, Science and Technology in Japan; Federal Ministry of Food, Agriculture and Consumer Protection in Germany; Ministry of Agriculture in France and Sweden; Ministry of Health, Welfare and Sport in Netherlands; Home Office in United Kingdom; Federal Council in Switzerland; and National Health and Medical Research Council in Australia. The control is responsibility of the Ministry of Science and Technology in Brazil, under two Federal Laws, a Decree and a Resolution; in the State of Parana, there is the State Animal Protection Code. Knowing the work of institutions responsible for the control of the animal experimentation in different countries may help the improvement of this process in Brazil. Such control is urgent due to the need for animal welfare protection and the increasing ethical concern from society in general.
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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 | 0.099 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".