The Vision of Toxicity Testing in the 21st Century: Moving from Discussion to Action
Bibliographic record
Abstract
Over the past year, a series on commentaries have appeared in the Toxicological Sciences Forum Series related to the 2007 National Research Council (NRC) publication, Toxicity Testing in the 21st Century: A Vision and A Strategy. The first article in the series provided an overview of the vision and was accompanied by an editorial by the three editors of Toxicological Sciences. During the past year, eight invited commentaries from the academic, industrial, and regulatory sectors have provided diverse perspectives on the vision, noted challenges to its implementation, and highlighted aspects of toxicity testing that were not addressed in the original NRC report. Here, we offer a summary of the main points raised by the commentators in tabular form, identify a number of common themes, and finish the series by providing our perspective on several key issues in charting the path forward to move from discussion to action.
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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 | 0.076 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.019 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 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".