Strategic resources for assessing PFAS ecological risks at AFFF sites
Bibliographic record
Abstract
Abstract The use of aqueous film forming foam (AFFF) in the United States has caused concern about the potential effects of per- and polyfluoroalkyl substances (PFAS) on ecological resources. Moreover, the limited availability of scientific information and a lack of guidance have collectively resulted in significant challenges for ecological risk assessors supporting site-specific investigations and management decisions at PFAS-impacted sites. To address these needs, the environmental science and technology program of the Department of Defense (DoD), the Strategic Environmental Research and Development Program (SERDP), began funding four desktop review projects in 2018. Project teams were tasked with developing strategic guidance for assessing ecological risk at AFFF-impacted DoD sites. The projects focused specifically on ecological risk assessment practices for threatened and endangered (T&E) species; however, the recommendations and approaches presented in the projects are also applicable and adaptable to common species. The present review provides risk assessors and site managers an overview of how the resources provided in these projects can support ecological-risk-based management decisions at AFFF and other PFAS-impacted sites. Additionally, we synthesize perspectives offered by the four projects on a path forward for PFAS ecological risk assessment, including research needs that we recommend should be prioritized by the scientific community. Integr Environ Assess Manag 2021;17:746–752. © 2021 SETAC KEY POINTS Aqueous film forming foam (AFFF) use has resulted in concern regarding potential ecological risks associated with per- and polyfluoroalkyl substances (PFAS), and a lack of guidance has resulted in significant challenge for site-specific investigations and management decisions at AFFF sites. The Strategic Environmental Research and Development Program (SERDP) began funding four desktop review projects in 2018 to develop strategic guidances for assessing ecological risk at AFFF-impacted sites. This review provides risk assessors and site managers an overview of how the resources provided in these projects can support ecological risk-based management decisions at AFFF and other PFAS-impacted sites. The review also highlights key research needs that, if addressed, will aid in improving site-specific ecological risk assessments for PFAS at AFFF sites.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".