Spatial and temporal variability of perfluoroalkyl substances in the Laurentian Great Lakes
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a diverse group of fluorinated organic chemicals that have been used in industrial and consumer applications since the 1950s. PFAS are resistant to chemical and biological degradation and are ubiquitous in the environment, including in water, sediment, and biota in the Laurentian Great Lakes. This critical review evaluates the spatial and temporal variability of commonly studied perfluoroalkyl sulfonates (PFSAs) and perfluoroalkyl carboxylates (PFCAs) in the Great Lakes by synthesizing data collected in water, surface sediment, sediment cores, lake trout (Salvelinus namaycush), and herring gull (Larus argentatus) eggs. The lowest PFAS concentrations in all matrices are detected in Lake Superior, which is located in the most pristine region of the Great Lakes Basin. In contrast, higher concentrations are observed in Lakes Erie and Ontario, which are more impacted by industrial activity and wastewater discharge. The distribution of individual PFAS compounds also varies across the lakes in response to changes in PFAS sources, with higher proportions of PFSAs in the eastern lakes. Sediment and biota are enriched in long chain PFSAs and PFCAs relative to concentrations in the water column, as expected based on predicted partitioning behavior. Sediment cores and bioarchives consistently demonstrate that PFAS concentrations increased in the Great Lakes from the initial time points until the early 2000s. The available data indicate that PFOS and PFOA concentrations decline after this period in the upper Great Lakes, but are stable in Lake Ontario. However, these trends depend on the lake, the individual compound, and the organism considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".