Dietary biomagnification of organochlorine contaminants in Alaskan polar bears
Bibliographic record
Abstract
Concentrations of organochlorine contaminants in the adipose tissue of polar bears ( Ursus maritimus Phipps, 1774) vary throughout the Arctic. The range in concentrations has not been explained fully by bear age, sex, condition, location, or reproductive status. Dietary pathways expose polar bears to a variety of contaminant profiles and concentrations. Prey range from lower trophic level bowhead whales ( Balaena mysticetus L., 1758), one of the least contaminated marine mammals, to highly contaminated upper trophic level ringed seals ( Phoca hispida (Schreber, 1775)). We used δ15N and δ13C signatures to estimate the trophic status of 42 polar bears sampled along Alaska’s Beaufort Sea coast to determine the relationship between organochlorine concentration and trophic level. The δ15N values in the cellular portions of blood ranged from 18.2‰ to 20.7‰. We found strong positive relationships between concentrations of the most recalcitrant polychlorinated biphenyls (PCBs) and δ15N values in models incorporating age, lipid content, and δ13C value. Specifically these models accounted for 67% and 76% of the variation in PCB153 and oxychlordane concentration in male polar bears and 85% and 93% in females, respectively. These results are strong indicators of variation in diet and biomagnification of organochlorines among polar bears related to their sex, age, and trophic position.
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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 | 0.000 | 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) | 0.001 | 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".