Patterns of Bioaccumulation of Polybrominated Diphenyl Ether and Polychlorinated Biphenyl Congeners in Marine Mussels
Bibliographic record
Abstract
Marine mussels (Modiolus modiolus) and sediment from 14 stations near a municipal outfall and three reference locations were analyzed for polybrominated diphenyl ethers (PBDEs) and polychlorinated biphenyls (PCBs) to evaluate and compare patterns of bioaccumulation of individual congeners between these two groups of chemicals. Of the 47 PBDEs and 209 PCBs analyzed, 34 PBDE and 153 PCB congeners or coeluting groups of congeners were detected in one or more matrices. The predominant PBDE congeners were BDEs 47, 99, 100, and 209, accounting for 80-90% of the total PBDEs in all matrices. PCBs and PBDEs exhibited a parabolic relationship of the biota-sediment accumulation factor (BSAF) versus the log octanol-water partition coefficient(Kow). Below Kow 10(5.5), BSAFs ranged between 1 and 3, reflecting approximate equilibrium between mussels and sedimentforthese relatively water soluble congeners. BSAFs increased with increasing Kow to maximum values of approximately 30-100 for congeners with Kow approximately 10(7) and then declined at higher Kow to a value of approximately 1 for BDE 209. BSAFs for PBDEs were generally 2- to 3-fold higher than those for PCBs of a similar Kow. The calculated BSAFs for PBDE congeners indicate that PBDEs have a pattern of bioaccumulative behavior in mussels similar to that of the PCBs, and that some PBDE congeners may be more bioaccumulative in mussels than PCBs.
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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) | 0.000 | 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".