Trends and Patterns of Perfluoroalkyl Substances in Blood Plasma Samples of Bald Eagle Nestlings in Wisconsin and Minnesota, USA
Bibliographic record
Abstract
We analyzed concentrations and trends of perfluoroalkyl substances (PFAS) in blood plasma samples of bald eagle (Haliaeetus leucocephalus) nestlings at 6 study areas in the upper Midwest of the United States, 2006 to 2015, and long-term trends at 2 Lake Superior (USA/Canada) sites, 1995 to 2015. Nestling blood plasma concentrations of the sum of 15 PFAS analytes (∑PFAS) differed among study areas and were highest at the 3 industrialized river sites: pools 3 and 4 of the Mississippi River (pools 3 + 4; geometric mean [GM] = 754 μg/L; range = 633-2930), the Mississippi National River and Recreation Area (GM = 687 μg/L; range = 24-7371), and the lower St. Croix National Scenic Riverway (GM = 546 μg/L; range = 20-2400). Temporal trends in ∑PFAS in nestling plasma differed among study areas; concentrations decreased at pools 3 + 4, Mississippi National River and Recreation Area, and lower St. Croix National Scenic Riverway, but not at the most remote sites, the upper St. Croix River and Lake Superior. Overall, perfluorooctanesulfonate (PFOS) was the most abundant analyte at all study areas, and perfluorodecanesulfonate (PFDS) the second most abundant at industrialized river sites although not at Lake Superior; concentrations of both these analytes declined from 2006 to 2015 over the study area. In addition, nestling age significantly influenced plasma concentrations of ∑PFAS and 7 of the 12 analytes. For these analytes, concentrations increased by 1 to 2%/d as nestlings grew, indicating that age should be considered when using nestling plasma to assess PFAS. Environ Toxicol Chem 2021;40:754-766. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.000 | 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 | 0.000 | 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".