Trophic Magnification of Poly- and Perfluorinated Compounds in a Subtropical Food Web
Bibliographic record
Abstract
Perfluorinated compounds (PFCs) are known to biomagnify in temperate and Arctic food webs, but little is known about their behavior in subtropical systems. The environmental distribution and biomagnification of PFCs, extractable organic fluorine (EOF), and total fluorine were investigated in a subtropical food web. Surface water, sediment, phytoplankton, zooplankton, gastropods, worms, shrimps, fishes, and waterbirds collected in the Mai Po Marshes Nature Reserve in Hong Kong were analyzed. Trophic magnification was observed for perfluorooctanesulfonate (PFOS), perfluorodecanoate (PFDA), perfluoroundecanoate (PFUnDA), and perfluorododecanoate (PFDoDA) in this food web. Risk assessment results for PFOS, PFDA, and perfluorooctanoate (PFOA) suggest that current PFC concentrations in waterbird livers are unlikely to pose adverse biological effects to waterbirds. All hazard ratio (HR) values reported for PFOS and PFOA are less than one, which suggests that the detected levels will not cause any immediate health effects to the Hong Kong population through the consumption of shrimps and fishes. However, only 10-12% of the EOF in the shrimp samples was comprised of known PFCs, indicating the need for further investigation to identify unknown fluorinated compounds in wildlife.
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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 | 0.000 |
| 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) | 0.002 | 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".