Persistent or not persistent? Polychlorinated biphenyls are readily depurated by grizzly bears (<i>Ursus arctos horribilis</i>)
Bibliographic record
Abstract
Major pharmacokinetic processes influencing polychlorinated biphenyl (PCB) accumulation in mammals include uptake, biotransformation, respiration, and excretion. We characterized some of the factors underlying PCB accumulation/loss by evaluating PCB concentrations and patterns in pre- and posthibernation grizzly bears (Ursus arctos horribilis) and their prey. The PCB congeners with vicinal meta- and para-chlorine unsubstituted hydrogen positions consistently showed loss both before and during hibernation, supporting the idea of a dominant role for biotransformation. Retention of all other studied congeners relative to that of PCB 194 varied widely (from <1 to 100%) and was highly correlated with log octanol-water partition coefficient (p < 0.0001). A lack of loss for most of these other congeners during hibernation supports the notion that excretion (e.g., fecal or urinary) or lack of uptake during the feeding season underlies their lack of accumulation, because hibernating bears do not eat or excrete. We estimate that grizzly bears retain less than 10% of total PCBs taken up from their diet. Our results suggest that for grizzly bears, depuration of PCBs via biotransformation is important (explaining approximately 40% of loss), but that nonbiotransformation processes, such as excretion, may be more important (explaining approximately 60% of loss). These findings, together with the approximately 91% loss of the persistent PCB 153 congener relative to PCB 194 in grizzly bears, raise important questions about how one defines persistence of PCBs in wildlife and may have bearing on the interpretation of food-web biomagnification studies.
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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".