Two Decades of Mercury Concentrations in Barents Sea Polar Bears (<i>Ursus maritimus</i>) in Relation to Dietary Carbon, Sulfur, and Nitrogen
Bibliographic record
Abstract
Abstract Temporal trends of total mercury (THg) were examined in female polar bear (Ursus maritimus) hair (n = 199) from the Barents Sea in 1995–2016. In addition, hair values of stable isotopes (n = 190–197) of carbon (δ13C), sulfur (δ34S), and nitrogen (δ15N) and information on breeding status, body condition, and age were obtained. Stable isotope values of carbon and sulfur reflect dietary source (e.g., marine vs terrestrial) and the nitrogen trophic level. Values for δ13C and δ34S declined by −1.62 and −1.18‰ over the time of the study period, respectively, while values for δ15N showed no trend. Total Hg concentrations were positively related to both δ13C and δ34S. Yearly median THg concentrations ranged from 1.61 to 2.75 μg/g and increased nonlinearly by 0.86 μg/g in total over the study. Correcting THg concentrations for stable isotope values of carbon and sulfur and additionally breeding status and age slightly accelerated the increase in THg concentrations; however, confidence intervals of the raw THg trend and the corrected THg trend had substantial overlap. The rise in THg concentrations in the polar bear food web was possibly related to climate-related re-emissions of previously stored Hg from thawing sea-ice, glaciers, and permafrost.
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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".