Incorporating climate change model projections into ecological risk assessments to help inform risk management and adaptation strategies: Synthesis of a SETAC Pellston Workshop®
Bibliographic record
Abstract
The impacts of global climate change are not yet well integrated with the estimates of the impacts of chemicals on the environment. This is evidenced by the lack of consideration in national or international reports that evaluate the impacts of climate change and chemicals on ecosystems and the relatively few peer-reviewed publications that have focused on this interaction. In response, a 2011 Pellston Workshop® was held on this issue and resulted in seven publications in Environmental Toxicology and Chemistry. Yet, these publications did not move the field toward climate change and chemicals as important factors together in research or policy-making. Here, we summarize the outcomes of a second Pellston Workshop® on this topic held in 2022 that included climate scientists, environmental toxicologists, chemists, and ecological risk assessors from 14 countries and various sectors. Participants were charged with assessing where climate models can be applied to evaluating potential exposure and ecological effects at geographical and temporal scales suitable for ecological risk assessment, and thereby be incorporated into adaptive risk management strategies. We highlight results from the workshop's five publications included in the special series "Incorporating Global Climate Change into Ecological Risk Assessments: Strategies, Methods and Examples." We end this summary with the overall conclusions and recommendations from participants. Integr Environ Assess Manag 2024;20:359-366. © 2023 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".