Integrating emerging technologies into chemical safety assessment: progress since the 2012 report of the expert panel on the integrated testing of pesticides
Bibliographic record
Abstract
Governments need to categorise tens of thousands of data-poor chemicals in order to better inform human health risk assessment. Pesticide active ingredients have contributed considerably to our understanding of the toxicological mechanisms; however, in order to move forward there is a pressing need for testing that is faster and less expensive based upon the chemical specific mode-of-action (MOA). Currently, a comprehensive set of these alternative methods does not yet exist, although the state of the science is rapidly evolving. The next two to ten years will see a global progression towards the use of integrated testing strategies (ITS) in decision-making for both data-rich and data-poor chemicals. Regulatory deployment of integrated approaches to testing and assessment (IATA) will depend upon the types of chemicals and the nature of the decision-making process by regulatory authorities. Regulators need to recognise that adoption of IATA strategies would require the engagement and approval of public stakeholders in order to alleviate concerns regarding potential adverse risks to human health and the environment. The new approach cannot be used to simply streamline processes or sacrifice human and environmental safety for social or economic benefits.
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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 | 0.061 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| 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".