Seasonal pollutant levels in littoral high-Arctic amphipods in relation to food sources and terrestrial run-off
Bibliographic record
Abstract
Increasing terrestrial run-off from melting glaciers and thawing permafrost to Arctic coastal areas is expected to facilitate re-mobilization of stored legacy persistent organic pollutants (POPs) and mercury (Hg), potentially increasing exposure to these contaminants for coastal benthic organisms. We quantified chlorinated POPs and Hg concentrations, lipid content and multiple dietary markers, in a littoral deposit-feeding amphipod Gammarus setosus and sediments during the melting period from April to August in Adventelva river estuary in Svalbard, a Norwegian Arctic Aarchipelago. There was an overall decrease in concentrations of ∑POPs from April to August (from 58 ± 23 to 13 ± 4 ng/g lipid weight; lw), Hg (from 5.6 ± 0.7 to 4.1 ± 0.5 ng/g dry weight; dw) and Methyl Hg (MeHg) (from 5 ± 1 to 0.8 ± 0.7 ng/g dw) in G. setosus. However, we observed a seasonal peak in penta- and hexachlorobenzene (PeCB and HCB) in May (2.44 ± 0.3 and 23.6 ± 1.7 ng/g lw). Sediment concentrations of POPs and Hg (dw) only partly correlated with the contaminant concentrations in G. setosus. Dietary markers, including fatty acids and carbon and nitrogen stable isotopes, indicated a diet of settled phytoplankton in May-July and a broader range of carbon sources after the spring bloom. Phytoplankton utilization and chlorobenzene concentrations in G. setosus exhibited similar seasonal patterns, suggesting a dietary uptake of chlorobenzenes that is delivered to the aquatic environment during spring snowmelt. The seasonal decrease in contaminant concentrations in G. setosus could be related to seasonal changes in dietary contaminant exposure and amphipod ecology. Furthermore, this decrease implies that terrestrial run-off is not a significant source of re-mobilized Hg and legacy POPs to littoral amphipods in the Adventelva river estuary during the melt season.
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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 | 0.000 |
| 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".