Interspecific differences in mercury and organochlorine pesticide concentrations in Arctic and boreal fishes
Bibliographic record
Abstract
The diversity and complexity of Arctic fish communities increases as boreal species expand their range poleward in response to changing environmental conditions. In turn, borealization of fish communities modifies the species composition of Arctic food webs, trophic interactions, and distribution of contaminants. Contaminants in marine fish and how they vary as a function of feeding ecology and location in Arctic and boreal regions is lacking. Here we assessed the drivers of total mercury (THg) and organochlorine pesticides (OCPs) concentrations in boreal capelin (Mallotus villosus), glacier lanternfish (Benthosema glaciale), Greenland halibut (Reinhardtius hippoglossoides), blue hake (Antimora rostrata), and abyssal grenadier (Coryphaenoides armatus) from the northwest Atlantic and eastern Canadian Arctic. We also examined regional differences in THg concentrations in Arctic cod (Boreogadus saida) across the Canadian Arctic. Length/δ15N and species were the most important determinants of THg concentrations in all fishes, with habitat (δ13C and δ34S) also playing a small role. While most OCPs varied by species, only three varied positively by trophic position (i.e., ΣParlar, ΣDDT, and Dieldrin), and one varied by location (i.e., Dieldrin). Generally, demersal fishes had higher Hg and OCP concentrations than pelagic fishes. Mercury concentrations in Arctic cod were higher in the western than the eastern Canadian Arctic, likely due to increased atmospheric inputs in the Beaufort Sea. Given the likely shift to pelagic systems and the replacement of Arctic residents with less contaminated boreal species (e.g., Arctic cod to capelin), we expect Hg to decrease in Arctic food webs with borealization. In contrast, since OCPs did not vary between Arctic and boreal species, we expect little influence of borealization on OCP concentrations in Arctic fishes.
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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 | 0.000 | 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".