Influence of Carbon and Lipid Sources on Variation of MercuryAnd Other Trace Elements in Polar Bears (Ursus Maritimus)
Bibliographic record
Abstract
In the present study, the authors investigated the influence of carbon and lipid sources on regional differences in liver trace element (As, Cd, Cu, total Hg, Mn, Pb, Rb, Se, and Zn) concentrations measured in polar bears (Ursus maritimus) (n=121) from 10 Alaskan, Canadian Arctic, and East Greenland subpopulations. Carbon and lipid sources were assessed using δ13C in muscle tissue and fatty acid (FA) profiles in subcutaneous adipose tissue as chemical tracers. A negative relationship between total Hg and δ13C suggested that polar bears feeding in areas with higher riverine inputs of terrestrial carbon accumulate more Hg than bears feeding in areas with lower freshwater input. Mercury concentrations were also positively related to the FA 20:1n-9, which is biosynthesized in large amounts in Calanus copepods. This result raises the hypothesis that Calanus glacialis are an important link in the uptake of Hg in the marine food web and ultimately in polar bears. Unadjusted total Hg, Se, and As concentrations showed greater geographical variation among polar bear subpopulations compared with concentrations adjusted for carbon and lipid sources. The Hg concentrations adjusted for carbon and lipid sources in Bering–Chukchi Sea polar bear liver tissue remained the lowest among subpopulations. Based on these findings, the authors suggest that carbon and lipid sources for polar bears should be taken into account when one is assessing spatial and temporal trends of long-range transported trace elements.
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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) | 0.000 | 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".