The PFAS roadmap–Navigating a path together to improved management
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) represent a large-and structurally diverse-group of contaminants that have become ubiquitous in our environment. PFAS are all extremely persistent while some are also bioaccumulative, mobile and/or toxic, which gives rise to significant environmental and health concerns. Despite more than a decade of intensive research, the management of PFAS is still associated with considerable challenges. It is evident that a holistic approach is required to address the challenging global problem of PFAS. This roadmap features expert perspectives from world-renowned leading researchers and practitioners on how best to manage PFAS. The 15 topics cover different facets of the complex PFAS issue, providing a multidisciplinary and multisectoral overview. For each topic, we reflect on the current status of knowledge and offer recommendations on science and technology advances that will help meet current and future challenges. Taken together, the 15 topics cover the entire life cycle of PFAS-from their sources to their destruction. Important themes such as monitoring and analysis, understanding and predicting fate, source controls (regulation and replacement), and existing and emerging strategies for remediation (capture and destroy) are highlighted throughout the roadmap. Overall, there are many recent scientific and technological advancements that show promise for the management of PFAS. However, it is also clear that there is no 'silver bullet' and multifaceted solutions will be needed. Long-term success hinges on sustained collaboration among researchers, policymakers, industries, and communities, which we hope this roadmap will help to catalyze.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".