Integrating emerging science to improve estimates of risk to wildlife from chemical exposure: What are the challenges?
Bibliographic record
Abstract
Many jurisdictions require ecological risk assessments for terrestrial wildlife (i.e., terrestrial vertebrates) to assess potential adverse effects from exposure to anthropogenic chemicals. This occurs, for example, at contaminated sites and when new pesticides are proposed, and it occurs for chemicals that are in production and/or proposed for wide-scale use. However, guidance to evaluate such risks has not changed markedly in decades, despite the availability of new scientific tools to do so. In 2019, the Wildlife Toxicology World Interest Group of the Society of Environmental Toxicology and Chemistry (SETAC) initiated a virtual workshop that included a special session coincident with the annual SETAC North America meeting and which focused on the prospect of improving risk assessments for wildlife and improving their use in implementing chemical regulations. Work groups continued the work and investigated the utility of integrating emerging science and novel methods for improving problem formulation (WG1), exposure (WG2), toxicology (WG3), and risk characterization (WG4). Here we provide a summary of that workshop and the follow-up work, the regulations that drive risk assessment, and the key focus areas identified to advance the ability to predict risks of chemicals to wildlife. Integr Environ Assess Manag 2024;20:645-657. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".