Bioaccumulation Factors for PCBs Revisited
Bibliographic record
Abstract
Bioaccumulation factors (BAFs) for individual polychlorinated biphenyl (PCB) congeners in Barents Sea and White Sea marine calanoid copepods were 1-3 orders of magnitude higher than BAFs in the same species in Canadian and Alaskan Arctic Ocean areas, and in freshwater plankton (Lake Ontario) reported from the mid- to early 1980s. The present study reviews variability in PCB BAFs from the North American Great Lakes and the Arctic Ocean, and discusses possible explanations for the large variation among different studies. BAFs are higher in recent arctic marine and Great Lakes studies than previously reported, and they are at least 10 times higher than those predicted from the octanol-water partition coefficient (KOW). If the recent high BAFs are realistic, it means that earlier reported BAFs are too low. This is likely due to earlier erroneously high quantification of water PCB concentrations, and it implies that bioaccumulation in zooplankton is more efficient than previously assumed. Evidence is presented supporting that also trophic transfer and biomagnification of PCBs in zooplankton leads to BAFs well above those predicted by simple equilibrium partitioning. Overall, miss-measurement of water PCB concentrations and biomagnification contribute significantly to variability in BAFs for PCBs within and among studies. This large variability of BAFs for PCBs in zooplankton illustrated in the present study is of importance for future assessments of potential new bioaccumulative chemicals that rely on measured BAFs, such as the European Union Registration, Evaluation and Authorization of Chemicals program (REACH).
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".