Per- and polyfluoroalkyl substances (PFASs) contamination of groundwater in Canada: a (too) short review
Bibliographic record
Abstract
Concerns over per- and polyfluoroalkyl substances (PFASs) contamination of water continue to grow as more PFASs are found in more places and related guideline concentrations generally decline. Reports on the occurrence of PFASs in groundwater from around the globe have been published in scientific journals for over two decades now. Much of this work originates from the United States, China, and European countries. In this review, we investigated the state of studies publishing data on PFAS concentrations in groundwater or identifying PFAS sources to groundwater in Canada. We found and report on only 11 studies in scientific journals (by mid-2024), and the majority of these had linkages (direct or collaborative research, funding, or other key support) to federal or provincial governments. Potential reasons behind there being so few studies are discussed. Additionally, we pose and examine four key questions that highlight areas needing greater investigation in Canada. These are: (1) What is the state of PFASs in groundwater-sourced drinking water across the country? (2) What are background PFASs concentrations for groundwater? (3) What is the prevalence and distribution of PFASs sources to groundwater and risk posed by them? 4. How important is groundwater transport of PFASs to surface waters and aquatic ecosystems?
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".