Seasonal variation in diet and nutrition of the northern‐most population of <i>Rhinopithecus roxellana</i>
Bibliographic record
Abstract
There is a great deal of spatial and temporal variation in the availability and nutritional quality of foods eaten by animals, particularly in temperate regions where winter brings lengthy periods of leaf and fruit scarcity. We analyzed the availability, dietary composition, and macronutrients of the foods eaten by the northern-most golden snub-nosed monkey (Rhinopithecus roxellana) population in the Qinling Mountains, China to understand food choice in a highly seasonal environment dominated by deciduous trees. During the warm months between April and November, leaves are consumed in proportion to their availability, while during the leaf-scarce months between December and March, bark and leaf/flower buds comprise most of their diet. When leaves dominated their diet, golden snub-nosed monkeys preferentially selected leaves with higher ratios of crude protein to acid detergent fiber. While when leaves were less available, bark and leaf/flower buds that were high in nonstructural carbohydrates and energy, and low in acid detergent fiber were selected. Southern populations of golden snub-nosed monkey can turn to eating lichen, however, the population studied here in this lichen-absent area have adapted to their cool deciduous habitat by instead consuming buds and bark. Carbohydrate and energy rich foods appear to be the critical resources required for the persistence of this species in temperate habitat. The dietary flexibility of these monkeys, both among seasons and populations, likely contributes to their wide distribution over a range of habitats and environments.
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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".