Diet and nutrient balance of wild Asian Elephants Elephas maximus in Nepal
Bibliographic record
Abstract
We estimated the nutritional content of major wild (n = 22) and agricultural crop plants (n = 3) consumed by Asian Elephants Elephas maximus in Nepal during the wet and dry seasons, respectively. We then used nutritional geometry to explore the macronutrient balance of these plant species, as well as the overall diet of elephants in both the dry and wet seasons. Furthermore, we compared the diet of the Nepal elephants with the previously published diet of Indian population of elephants. We found that despite intraspecific and seasonal variation, the overall diet of elephants was relatively stable in protein (P) intake relative to non-protein macronutrients (fat + carbohydrate; non-protein (NP)), and neutral detergent fibre (NDF) between the wet (16% crude protein (CP): 26.7 % NP: 57.3% NDF; and, 10.4% CP: 13.7% NP: 75.7% NDF) in dry season, which suggests protein intake prioritization in support of previous work on captive elephants. Furthermore, the diet of Indian population of elephants (wet season: 16.0%P: 22.5%NP: 61.4%NDF and dry season: 11.1%P: 18.0 %NP: 70.7 %NDF) showed a similar pattern to the Nepal elephants, suggesting active regulation of macronutrient and NDF intake across populations despite differences in food consumed as part of their diets. Importantly, NDF intake in addition to non-protein macronutrients is likely necessary for elephants to stabilize their protein intake balance; thus, it is important to consider a multidimensional nutritional perspective in elephant conservation planning. The study has concluded that in a well-managed seasonal habitat, elephants can regulate their preferred macronutrient and NDF intake from available natural food plants without resorting to agricultural crop depredation.
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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 | 0.000 | 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".