Assessing stress in a mammal from plasma and feces: A nutritional mismatch between the diet needed and the food -available
Bibliographic record
Abstract
• The stress axis gives fundamental insight into animal condition in nature. • In a hibernating squirrel with simple gut, we quantified cortisol in both plasma and feces. • These two measures were not concordant, differing if squirrels lived in a natural or unnatural meadow. A lack of agreement between the diet an animal needs for reproduction and survival and the food present in its environment has major impact on its fitness. The complexity of their digestive system is critical and may affect stress axis function. The squirrel family (sciurids) have a very simple gut and do not have the microbiome to digest high fiber foods well. Thus, they forage preferentially on forbs, seeds, and fungi, and avoid hard-to-digest grasses. We compared two measures of their stress axis − plasma free cortisol, a glucocorticoid (GC), and fecal cortisol metabolites (FCMs) − in Columbian ground squirrels (Urocitellus columbianus) from two meadows as they were building up body reserves in July for their pending hibernation. One meadow was natural with an abundance of forbs and the other unnatural with an abundance of grasses that had been seeded as a horse pasture. Our two measures went in the opposite direction: GC levels were lower on the natural meadow but FCMs were higher, whereas GC levels were higher on the grass meadow but FCMs lower. The fecal fiber content was lower on the natural meadow. Thus, when interpreting FCM levels, it is critical to understand both the digestive system of the study mammal and its diet to interpret stress axis function.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".