Trophic Transfer of Contaminants in a Changing Arctic Marine Food Web: Cumberland Sound, Nunavut, Canada
Bibliographic record
Abstract
Contaminant dynamics in arctic marine food webs may be impacted by current climate-induced food web changes including increases in transient/subarctic species. We quantified food web organochlorine transfer in the Cumberland Sound (Nunavut, Canada) arctic marine food web in the presence of transient species using species-specific biomagnification factors (BMFs), trophic magnification factors (TMFs), and a multifactor model that included δ(15)N-derived trophic position and species habitat range (transient versus resident), and also considered δ(13)C-derived carbon source, thermoregulatory group, and season. Transient/subarctic species relative to residents had higher prey-to-predator BMFs of biomagnifying contaminants (1.4 to 62 for harp seal, Greenland shark, and narwhal versus 1.1 to 20 for ringed seal, arctic skate, and beluga whale, respectively). For contaminants that biomagnified in a transient-and-resident food web and a resident-only food web scenario, TMFs were higher in the former (2.3 to 10.1) versus the latter (1.7 to 4.0). Transient/subarctic species have higher tissue contaminant levels and greater BMFs likely due to higher energetic requirements associated with long-distance movements or consumption of more contaminated prey in regions outside of Cumberland Sound. These results demonstrate that, in addition to climate change-related long-range transport/deposition/revolatilization changes, increasing numbers of transient/subarctic animals may alter food web contaminant dynamics.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".