Data from: Managing anabolic steroids in pre-hibernating Arctic ground squirrels: obtaining their benefits and avoiding their costs
Bibliographic record
Abstract
Androgens have benefits, such as promoting muscle growth, but also significant costs, including suppression of immune function. In many species, these trade-offs in androgen action are reflected in regulated androgen production, which is typically highest only in reproductive males. However, all non-reproductive Arctic ground squirrels, irrespective of age and sex, have high levels of androgens prior to hibernating at sub-zero temperatures. Androgens appear to be required to make muscle in summer, which, together with lipid, is then catabolized during overwinter. By contrast, most hibernating mammals catabolize only lipid. We tested the hypothesis that androgen action is selectively enhanced in Arctic ground squirrel muscle because of an upregulation of androgen receptors (ARs). Using Western blot analysis, we found that Arctic ground squirrels have AR in skeletal muscle more than four times that of Columbian ground squirrels, a related southern species that overwinters at approximately 0°C and has low pre-hibernation androgen levels. By contrast, AR in lymph nodes was equivalent in both species. Brain AR was also modestly but significantly increased in Arctic ground squirrel relative to Columbian ground squirrel. These results are consistent with the hypothesis that tissue-specific AR regulation prior to hibernation provides a mechanism whereby Arctic ground squirrels obtain the life-history benefits and mitigate the costs associated with high androgen production.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.021 |
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".