Examining animal testing for risk assessment: A WC-12 workshop report
Bibliographic record
Abstract
In toxicology and regulatory testing, the use of animal methods has been both a cornerstone and a subject of intense debate. To continue this discourse a panel and audience representing scientists from various sectors and countries convened at a workshop held during the 12th World Congress on Alternatives and Animal Use in the Life Sciences (WC-12). The ensuing discussion focused on the scientific and ethical considerations surrounding the necessity and responsibility of defending the creation of new animal data in regulatory testing. The primary aim was to foster an open dialogue between the panel members and the audience while encouraging diverse perspectives on the responsibilities and obligations of various stakeholders (including industry, regulatory bodies, technology developers, research scientists, and animal welfare NGOs) in defending the development and subsequent utilization of new animal data. This workshop summary report captures the key elements from this critical dialogue and collective introspection. It describes the intersection of scientific progress and ethical responsibility as all sectors seek to accelerate the pace of 21st century predictive toxicology and new approach methodologies (NAMs) for the protection of human health and the environment.
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 | 0.059 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".