Monitoring of Perfluorinated Compounds in Aquatic Biota: An Updated Review
Bibliographic record
Abstract
The goal of this article is to summarize new biological monitoring information on perfluorinated compounds (PFCs) in aquatic ecosystems (post-2005) as a followup to our critical review published in 2006. A wider range of geographical locations (e.g., South America, Russia, Antarctica) and habitats (e.g., high-mountain lakes, deep-ocean, and offshore waters) have been investigated in recent years enabling a better understanding of the global distribution of PFCs in aquatic organisms. High concentrations of PFCs continue to be detected in invertebrates, fish, reptiles, and marine mammals worldwide. Perfluorooctane sulfonate (PFOS) is still the predominant PFC detected (mean concentrations up to 1900 ng/g ww) in addition to important concentrations of long-chain perfluoroalkyl carboxylates (PFCAs; sum PFCAs up to 400 ng/g ww). More studies have evaluated the bioaccumulation and biomagnification of these compounds in both freshwater and marine food webs. Several reports have indicated a decrease in PFOS levels over time in contrast to PFCA concentrations that have tended to increase in tissues of aquatic organisms at many locations. The detection of precursor metabolites and isomers has become more frequently reported in environmental assessments yielding important information on the sources and distribution of these contaminants. The integration of environmental/ecological characteristics (e.g., latitude/longitude, salinity, and/or trophic status at sampling locations) and biological variables (e.g., age, gender, life cycle, migration, diet composition, growth rate, food chain length, metabolism, and elimination) are essential elements in order to adequately study the environmental fate and distribution of PFCs and should be more frequently considered in study design.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".