Modeling biomagnification and metabolism of contaminants in harp seals of the Barents Sea
Bibliographic record
Abstract
A simple fugacity-based bioaccumulation model is presented for harp seals (Phoca groenlandica), which feed primarily on polar cod (Boreogadus saida) and a pelagic crustacean (Themisto libellula). Using concentration data reported for 15 polychlorinated biphenyl (PCB) congeners and 27 pesticides in the food and blubber of harp seals from the Barents Sea, the model was used to determine biomagnification factors and metabolic half-lives as well as rates of contaminant uptake and loss processes in seals, including a discussion of uncertainty in biomagnification factors and half-lives. Examination of the model output shows considerable, but highly variable, biomagnification attributable to differences in metabolic rates. It is suggested that two biomagnification factors can be defined and should be used in such assessments, one based on concentration ratios and the other on fugacity ratios or lipid-normalized concentrations. A maximum biomagnification factor specific to the seal is deduced using biomagnification data for the most persistent substances, and from this, metabolic half-lives are estimated for all substances. The approach can be applied to other biomagnification situations, thus quantifying metabolic half-lives as a function of the properties of the bioaccumulating substance and ultimately its molecular structure and the physiological characteristics of the consuming animal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".