Spatial and Temporal Trends of Perfluoroalkyl Substances in Global Ocean and Coastal Waters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) have been widely detected in global surface waters since the early 2000s. Here, we have compiled and analyzed the published data for perfluorocarboxylates (PFCAs) and perfluorosulfonates (PFSAs) in surface waters of coastal seas, the Great Lakes, and open oceans to examine temporal and geospatial trends. Mass discharges from major rivers were also estimated. A large number of measurements of individual PFAS have been made in these surface waters (29 500 values), with seven C4-C10 PFSAs and nine C4-C12 PFCAs accounting for 83% of all data. However, most results (85% for PFSAs; 80% for PFCAs) were for the coastal seas of Western Europe, China, Korea, and Japan, while results were limited for coastal North America and lacking for South America and Africa. Highest median concentrations of PFCAs and PFSAs were reported in the Bohai and Yellow Seas region of China as well as in the North and Baltic seas in Europe. Significant declines in median PFSAs and C7-C12 PFCAs were also observed for the period 2012-2018 in these same regions, and for 2004-2017 in the Great Lakes. Mass discharge estimates indicated continued substantial riverine emissions of long chain (C7-C12) PFCAs in the period 2015-2019 for the coastal seas of China and reductions in emissions for Western European rivers compared to earlier time periods.
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.
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 | 0.000 | 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 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it