Sources of variation in fecal cortisol levels in howler monkeys in belize
Bibliographic record
Abstract
High cortisol levels are known to cause low fecundity and increased mortality; thus, the prospect of using cortisol as a measure of population health is an exciting one. However, because so many factors can interact to influence cortisol release, it can be difficult to interpret what exactly is creating changes to cortisol levels. This study investigates variation in fecal cortisol levels in a population of black howlers (Alouatta pigra) from 350 fecal samples collected from 33 individuals in more than 4 years. A general linear mixed model revealed that cortisol varied significantly with fruit availability and contact with tourists. When fruit availability was low, cortisol increased, likely because when fruit availability is low monkeys eat less fruit, thus obtaining less sugar. This result may simply reflect cortisol's metabolic function of mobilizing glucose. It also indicates that these monkeys may be experiencing periods of food stress throughout the year, which was earlier thought to be minimal for a primarily folivorous species. Presence of tourists was the only other factor found to lead to high cortisol; with exposure to tourists increasing stress levels. These results highlight the importance of understanding how physiological factors can influence cortisol, making it easier to interpret results and determine the external social or ecological stressors that may increase cortisol.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".