Using emerging science to inform risk characterizations for wildlife within current regulatory frameworks
Bibliographic record
Abstract
Many jurisdictions have regulatory frameworks that seek to reduce the effects of environmental exposures of anthropogenic chemicals on terrestrial wildlife (i.e., mammals, birds, reptiles, and amphibians). The frameworks apply for new and existing chemicals, including pesticides (prospective assessments), and to environmental contamination from releases (retrospective risk assessments). Relatively recently, there have been many scientific advances that could improve risk estimates for wildlife. Here, we briefly describe current regulations from North America (United States and Canada) and from Europe that include risk assessments for wildlife to ascertain whether they are conducive to the use of emerging science and new methods. We also provide examples where new and emerging science may be used to improve wildlife risk characterization and identify areas in need of future research. Integr Environ Assess Manag 2024;20:765-779. © 2024 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management © 2024 Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".