Transforming the Evaluation of Agrochemicals: A Conceptual Model
Bibliographic record
Abstract
Globally, regulatory authorities face the challenge of integrating advances in science and technology into existing frameworks for agrochemical risk assessment. Addressing this challenge is critical to meeting the demands of food safety and quality for a growing population. To support this shift, the Health and Environmental Sciences Institute (HESI) convened a multi-stakeholder committee of international scientists. Through a problem formulation-led strategy, the committee developed the Transforming the Evaluation of Agrochemicals (TEA) conceptual model to guide the adoption of new methods, best practices, and technologies into regulatory practice for agrochemical safety. The core of the model incorporates the well-established tiered approach routinely used for identifying and characterizing hazards and assessing exposures; however, the model strategically identifies three sequential elements: exposure-led, adaptability, and inclusion of new science. The central core is then surrounded by layers with additional elements, namely: fit-for-purpose over time, adapt to global need, adapt to local need, create incentives, data sharing and transparency, and build trust. Collectively, these ten elements and their intersections result in a novel, TEA conceptual model with elements that have not been simultaneously implemented in any regulatory data package to date. In providing guiding principles, two examples of regulatory applications, and a concise summary of how this model supports an opportunity to go beyond next generation risk assessments focused primarily on alternative approaches to animal testing, we demonstrate the utility of the TEA conceptual model as a tool and mechanism supporting a structured and systematic application towards the intended transformation of agrochemical evaluations.
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 | 0.026 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".