Spatial trends and bioaccumulation of organochlorine pollutants in marine zooplankton from the Alaskan and Canadian Arctic
Bibliographic record
Abstract
Planktonic copepods (Calanus glacialis and C. hyperboreus; n = 37) and water (n = 19) were collected to examine the spatial distribution and bioaccumulation of organochlorine contaminants (OCs) in the Alaskan and Canadian Arctic. The rank order of total OC (sigma OC) group concentrations in Calanus samples was toxaphene > or = sigma polychlorinated biphenyls (PCBs) > sigma hexachlorcyclohexane (HCH) > sigma DDT > sigma chlordane-related compounds (CHLOR) > sigma chlorobenzenes (ClBz). The dominant analyte was alpha-HCH in all water and zooplankton samples. The most abundant toxaphene congener in water and zooplankton samples was the hexachlorobornane B6-923. Organochlorine contaminant group concentrations in Alaskan zooplankton and water samples were lower than those in samples collected from sites in the eastern Canadian Arctic. Comparison of PCB and toxaphene congener profiles in zooplankton and water samples suggests that biotransformation by cytochrome P-4502B isozymes is low in Calanus, and limited phase I metabolism may occur. The log relationship of bioaccumulation factor (log BAF) versus octanol-water partition coefficient (log Kow) relationship was near 1:1 for OCs within the log Kow range of 3 to 6. A curvilinear model provided a better relationship between these two variables when OC compounds with log Kow > 6 were included. These results suggest that hydrophobic OCs (log Kow 3-6) in Calanus species are at equilibrium with the water concentrations and that physical partitioning, rather than biotransformation, is the major factor governing OC profiles in marine zooplankton.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".