Hibernation-Associated Changes in Persistent Organic Pollutant (POP) Levels and Patterns in British Columbia Grizzly Bears (<i>Ursus arctos</i> <i>horribilis</i>)
Bibliographic record
Abstract
We hypothesized that depleted fat reserves in grizzly bears (Ursus arctos horribilis) following annual hibernation would reveal increases in persistent organic pollutant (POP) concentrations compared to those present in the fall. We obtained fat and hair from British Columbia grizzly bears in early spring 2004 to compare with those collected in fall 2003, with the two tissue types providing contaminant and dietary information, respectively. By correcting for the individual feeding habits of grizzlies using a stable isotope-based approach, we found that polychlorinated biphenyls (sigmaPCBs) increased by 2.21x, polybrominated diphenylethers (sigmaPBDEs) increased by 1.58x, and chlordanes (sigmaCHL) by 1.49x in fat following hibernation. Interestingly, individual POPs elicited a wide range of hibernation-associated concentration effects (e.g., CB-153, 2.25x vs CB-169, 0.00x), resulting in POP pattern convergence in a PCA model of two distinct fall feeding groups (salmon-eating vs non-salmon-eating) into a single spring (post-hibernation) group. Our results suggest that diet dictates contaminant patterns during a feeding phase, while metabolism drives patterns during a fasting phase. This work suggests a duality of POP-associated health risks to hibernating grizzly bears: (1) increased concentrations of some POPs during hibernation; and (2) a potentially prolonged accumulation of water-soluble, highly reactive POP metabolites, since grizzly bears do not excrete during hibernation.
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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".