Transforming the evaluation of agrochemicals
Bibliographic record
Abstract
The present agrochemical safety evaluation paradigm is long-standing and anchored in well-established testing and evaluation procedures. However, it does not meet the present-day challenges of rapidly growing populations, food insecurity, and pressures from climate change. To transform the current framework and apply modern evaluation strategies that better support sustainable agriculture, the Health and Environmental Sciences Institute (HESI) assembled a technical committee to reframe the safety evaluation of crop-protection products. The committee is composed of international experts from regulatory agencies, academia, industry and nongovernmental organizations. Their mission is to establish a framework that supports the development of fit-for-purpose agrochemical safety evaluation that is applicable to changing global, as well as local needs and regulatory decisions, and incorporates relevant evolving science. This will be accomplished through the integration of state-of-the-art scientific methods, technologies and data sources, to inform safety and risk decisions, and adapt them to evolving local and global needs. The project team will use a systems-thinking approach to develop the tools that will implement a problem formulation and exposure driven approach to create sustainable, safe and effective crop protection products, and reduce, replace and refine animal studies with fit-for-purpose assays. A new approach necessarily will integrate the most modern tools and latest advances in chemical testing methods to guarantee the robust human and environmental safety and risk assessment of agrochemicals. This article summarizes the challenges associated with the modernization of agrochemical safety evaluation, proposes a potential roadmap, and seeks input and engagement from the broader community to advance this effort. © 2022 Health and Environmental Sciences Institute (HESI). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.093 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".