Foods, macronutrients and fibre in the diet of blue sheep (<i>Psuedois nayaur</i>) in the Annapurna Conservation Area of Nepal
Bibliographic record
Abstract
Food resources are often critical regulating factors affecting individual fitness and population densities. In the Himalayan Mountains, Bharal "blue sheep" (Pseudois nayaur) are the main food resource for the endangered snow leopard (Panthera uncia), as well as being preyed upon by other predators. Blue sheep, however, may face a number of challenges including food resource competition with other wild and domestic ungulates, and hunting pressure. Here, we characterized the diet of blue sheep in the Annapurna Conservation Area (ACA) of Nepal and conducted proximate nutritional analysis on a limited number of plants identified as foods. Furthermore, we investigated the macronutrient and fiber balance of these plants using nutritional geometry which is a state-space approach to modeling multidimensional and interactive nutritional aspects of foraging. A total of 19 plant species/genera were identified in blue sheep pellets using microhistological analysis. On average, across seasons and regions of the study area, the two most frequently occurring plants in pellets were graminoids: Kobressia sp. and Carex spp. The macronutrient balance of Kobresia sp. was relatively high in carbohydrate and low in protein, while other plants in the diet were generally higher in protein and lipid content. Analysis of fiber balance showed that the two most consumed plants of blue sheep (i.e., Kobresia spp. and Carex spp.) contained the highest concentration of hemicellulose, which is likely digestible by blue sheep. The hemicellulose and lignin balance of plants ranged relatively widely, yet their cellulose contents showed less variation. Foraging by blue sheep may therefore be a balance between consuming highly digestible high-carbohydrate plants and plants less-digestible but higher in protein and/or lipid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".