Scientific uses of animals: harm–benefit analysis and complementary approaches to implementing the Three Rs
Bibliographic record
Abstract
The principles of humane experimental technique, first described by Russell and Burch in 1959, focus on minimising suffering to animals used for scientific purposes. Internationally, as these principles became embedded in the various systems of oversight for the use of animals in science, attention focused on how to minimise pain, distress and lasting harm to animals while maximising the benefits to be obtained from the work. Suffering can arise from the experimental procedures, but it can also arise from the manner in which the animals are housed and cared for. Increased attention is therefore being paid to the entire lifetime experience of an animal, in order to afford it as good a quality of life as possible. Russell and Burch were also concerned that animals should not be used if alternatives to such use were available, and that animals were not wasted through poor-quality science. This concept is being revisited through new efforts to ensure that experiments are well designed and properly reported in the literature, that all results--positive, negative or neutral--are made available to ensure a complete research record, and that animal models are properly evaluated through periodic systematic reviews. These efforts should ensure that animal use is truly reduced as far as possible and that the benefits derived through the use of animals truly outweigh the harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.240 | 0.229 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".