Feeding preferences and nutritional niche of wild water buffalo (<i>Bubalus arnee</i>) in Koshi Tappu Wildlife Reserve, Nepal
Bibliographic record
Abstract
; hereafter "buffalo") in the Koshi Tappu Wildlife Reserve of Nepal during autumn using a multidimensional nutritional niche framework. We identified 54 plant species as being foraged by buffalo. We found that buffalo consumed graminoids and forbs 2-3 times more frequently than browse items. Proximate analyses of the 16 most frequently foraged plants indicated that buffalo diets were highest in carbohydrate (40.41% ± 1.82%) followed by crude protein (10.52% ± 0.93%) and crude fat (1.68% ± 0.23%). The estimated macronutrient balance (i.e., realized nutrient niche) of the buffalo diet (20.5% protein: 72.8% carbohydrate: 6.7% lipid) was not significantly different than the average balance of all analyzed food items based on 95% confidence regions. Our study suggests that buffalo are likely macronutrient specialists, yet may be generalists in the sense that they feed on a wide range of food items to achieve a nutrient balance similar to that available in forage items. However, the four most frequently consumed items tended to be higher in protein energy than less frequently consumed foods, suggesting some preference for higher protein forage relative to relatively abundant carbohydrates. Although limited in scope, our study provides important information on the nutritional ecology of buffalo, which may be useful for the conservation and management of this endangered species.
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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".