Spatial and temporal trends of selected trace elements in liver tissue from polar bears (Ursus maritimus) from Alaska, Canada and Greenland
Bibliographic record
Abstract
Spatial trends and comparative changes in time of selected trace elements were studied in liver tissue from polar bears from ten different subpopulation locations in Alaska, Canadian Arctic and East Greenland. For nine of the trace elements (As, Cd, Cu, Hg, Mn, Pb, Rb, Se and Zn) spatial trends were investigated in 136 specimens sampled during 2005-2008 from bears from these ten subpopulations. Concentrations of Hg, Se and As were highest in the (northern and southern) Beaufort Sea area and lowest in (western and southern) Hudson Bay area and Chukchi/Bering Sea. In contrast, concentrations of Cd showed an increasing trend from east to west. Minor or no spatial trends were observed for Cu, Mn, Rb and Zn. Spatial trends were in agreement with previous studies, possibly explained by natural phenomena. To assess temporal changes of Cd, Hg, Se and Zn concentrations during the last decades, we compared our results to previously published data. These time comparisons suggested recent Hg increase in East Greenland polar bears. This may be related to Hg emissions and/or climate-induced changes in Hg cycles or changes in the polar bear food web related to global warming. Also, Hg:Se molar ratio has increased in East Greenland polar bears, which suggests there may be an increased risk for Hg(2+)-mediated toxicity. Since the underlying reasons for spatial trends or changes in time of trace elements in the Arctic are still largely unknown, future studies should focus on the role of changing climate and trace metal emissions on geographical and temporal trends of 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 | 0.001 |
| 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) | 0.000 | 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".