PLANT CONSUMPTION BY GRIZZLY BEARS REDUCES BIOMAGNIFICATION OF SALMON-DERIVED POLYCHLORINATED BIPHENYLS, POLYBROMINATED DIPHENYL ETHERS, AND ORGANOCHLORINE PESTICIDES
Bibliographic record
Abstract
The present study characterizes the uptake and loss of persistent organic pollutants (POPs) in grizzly bears (Ursus arctos horribilis) by sampling and analyzing their terrestrial and marine foods and fecal material from a remote coastal watershed in British Columbia, Canada. The authors estimate that grizzly bears consume 341 to 1,120 µg of polychlorinated biphenyls (PCBs) and 3.9 to 33 µg of polybrominated diphenyl ethers daily in the fall when they have access to an abundant supply of returning salmon. The authors also estimate that POP elimination by grizzly bears through defecation is very low following salmon consumption (typically <2% of intake) but surprisingly high following plant consumption (>100% for PCBs and organochlorine pesticides). Excretion of individual POPs is largely driven by a combination of fugacity (differences between bear and food concentrations) and the digestibility of the food. The results of the present study are substantiated by a principal components analysis, which also demonstrates a strong role for log KOW in governing the excretion of different POPs in grizzly bears. Collectively, the present study's results reveal that grizzly bears experience a vegetation-associated drawdown of POPs previously acquired through the consumption of salmon, to such an extent that net biomagnification is reduced.
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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".