Implementing toxicity testing in the 21st century: challenges and opportunities
Bibliographic record
Abstract
The publication of the US National Academy of Sciences report Toxicity Testing in the Twenty-First Century: A Vision and a Strategy (TT21C) has led to the development of new scientific techniques to modernise regulatory toxicity testing. From 2009 to 2010, a series of five international symposia were held to examine challenges, opportunities and policy issues associated with TT21C. Seven key themes emerged based on these meetings; that the TT21C vision and strategy: 1) is not self-implementing; 2) demands new toxicology techniques; 3) has a number of scientific knowledge gaps that need to be filled; 4) requires evaluation of the new tests to determine relevance, reliability, validity and regulatory acceptance by government agencies; 5) can be implemented under TSCA and the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) as currently written; 6) requires multi-stakeholder input and commitment; 7) should harmonise acceptance of test data and methods on an international level.
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 | 0.183 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".