Rethinking chronic toxicity and carcinogenicity assessment for agrochemicals project (ReCAAP): A reporting framework to support a weight of evidence safety assessment without long-term rodent bioassays
Bibliographic record
Abstract
Rodent cancer bioassays have been long-required studies for regulatory assessment of human cancer hazard and risk. These studies use hundreds of animals, are resource intensive, and certain aspects of these studies have limited human relevance. The past 10 years have seen an exponential growth of new technologies with the potential to effectively evaluate human cancer hazard and risk while reducing, refining, or replacing animal use. To streamline and facilitate uptake of new technologies, a workgroup comprised of scientists from government, academia, non-governmental organizations, and industry stakeholders developed a framework for waiver rationales of rodent cancer bioassays for consideration in agrochemical safety assessment. The workgroup used an iterative approach, incorporating regulatory agency feedback, and identifying critical information to be considered in a risk assessment-based weight of evidence determination of the need for rodent cancer bioassays. The reporting framework described herein was developed to support a chronic toxicity and carcinogenicity study waiver rationale, which includes information on use pattern(s), exposure scenario(s), pesticidal mode-of-action, physicochemical properties, metabolism, toxicokinetics, toxicological data including mechanistic data, and chemical read-across from similar registered pesticides. The framework could also be applied to endpoints other than chronic toxicity and carcinogenicity, and for chemicals other than agrochemicals.
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 | 0.554 | 0.350 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.023 | 0.010 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.029 | 0.019 |
| Open science | 0.027 | 0.022 |
| Research integrity | 0.024 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".