Enantiomer-specific biomagnification of α-Hexachlorocyclohexane and selected chiral chlordane-related compounds within an arctic marine food web
Bibliographic record
Abstract
Concentrations of achiral and chiral organochlorine contaminants (OCs), including hexachlorocyclohexane isomers (HCH), chlordane congeners (cis- and trans-chlordane, cis- and trans-nonachlor, MC5, MC7, and U82), and related metabolites (oxychlordane [OXY] and heptachlor exo-epoxide [HEPX]), were quantified in seawater (100 L; n = 6) and biota from the coastal Beaufort-Chukchi Seas food web near Barrow (AK, USA). The biota included zooplankton (Calanus spp.; n = 5), fish species such as arctic cod (Boreogadus saida; n = 10), arctic char (Salvelinus alpinus; n = 3), and marine mammals including bowhead whales (Balaena mysticetus; liver: n = 23; blubber: n = 40), beluga whales (Delphinapterus leucas; blubber: n = 20), ringed seals (Phoca hispida; blubber: n = 20), and bearded seals (Erignathus barbatus; blubber: n = 7). The food web magnification factors (FWMFs) for HCHs and chlordane compounds ranged from 0.5 (gamma-HCH) to 6.5 (HEPX) and were expected based on known recalcitrance and biotransformation of OCs. The enantiomer fractions (EFs) of all chiral OCs were near racemic (EF = 0.50) in the seawater, zooplankton, and all fish analyzed. In contrast, the EFs for most OCs analyzed were nonracemic (EF # 0.50) in the marine mammals blubber (range: 0.09-0.79) because of enantiomer-specific biotransformation and (or) accumulation. However, EF values were not significantly correlated with isotopically determined trophic level. The EFs for all chiral OCs (except alpha-HCH) in bowhead whale liver closely approximated the values in zooplankton, suggesting that the accumulation of chiral OCs from prey into this cetacean is not enantiomer specific. However, the modification of EFs from bowhead liver to blubber suggests that this species has the ability to enantioselectively biotransform and accumulate several chiral OC compounds.
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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) | 0.001 | 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".