Organochlorine Compounds in Lake Superior: Chiral Polychlorinated Biphenyls and Biotransformation in the Aquatic Food Web
Bibliographic record
Abstract
The enantiomeric composition of seven chiral PCB congeners was measured in the Lake Superior aquatic food web sampled in 1998, to determine the extent of enantioselective biotransformation in aquatic biota. All chiral PCB congeners studied (CBs 91, 95, 136, 149, 174, 176, and 183) biomagnified in the Lake Superior aquatic food web, based on biomagnification and food web magnification factors greater than unity. PCB atropisomers were racemic in phytoplankton and zooplankton, suggesting no biotransformation potential toward PCBs for these low trophic level organisms. However, Diporeia and mysids had significantly nonracemic residues for most chiral congeners studied. This observation suggests that these macrozooplankton can stereoselectively metabolize chiral congeners. Alternatively, macrozooplankton obtained nonracemic residues from feeding on organic-rich suspended particles and sediments, which would imply that stereoselective microbial PCB biotransformation may be occurring in Lake Superior sediments at PCB concentrations far lower than that previously associated with such activity. Widely nonracemic PCB residues in forage fish (lake herring, rainbow smelt, and slimy sculpin) and lake trout suggest a combination of both in vivo biotransformation and uptake of nonracemic residues from prey for these species. Minimum biotransformation rates, calculated from enantiomer mass balances between predators and prey, suggest metabolic half-lives on the order of 8 yr for CB 136 in lake trout and 2.6 yr for CB 95 in sculpins. This result suggests that significant biotransformation may occur for metabolizable PCB congeners over the lifespan of these biota. This study highlights the potential of chiral analysis to study biotransformation processes in food webs.
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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 | 0.000 | 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) | 0.001 | 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".