Feeding preferences and nutritional niche of wild water buffalo (Bubalus arnee) in Koshi Tappu Wildlife Reserve, Nepal.
Bibliographic record
Abstract
We sought to further the understanding of how an animal’s foraging ecology is influenced by their feeding preferences and nutritional composition of forage items. Our research identified these aspects of wild water buffalo’s (Bubalus arnee) foraging ecology in Nepal. First, we sought to describe the foraging preferences of wild water buffalo in terms of the relative abundance of functional forage groups (i.e., forbs, graminoids, and browse) in their diet. We observed signs of wild water buffalo foraging on 54 plant species. We found wild water buffalo consume graminoids and forbs 2-3 times more frequently than browse items. Then, we investigated the composition of nutrients (i.e., carbohydrates, proteins, and lipids) in wild water buffalo diets to develop an estimate of their realized nutrient niche. Of the 54 plant species foraged, we analyzed the nutritional composition of the 16 most frequently foraged species. We found the realized nutrient niche of wild water buffalo is dominated by carbohydrates, but there was a positive correlation between the relative frequency of foraged items and protein content. Our study contributes important information on the feeding preference and nutritional content of the endangered wild water buffalo. Our results can be used to inform conservation and management strategies of this species in wild. Selection of potential translocation sites should include key species that are higher in protein content and frequently forages by wild water buffalo (e.g., Typha elephantina) to help mitigate human-wildlife conflicts. Further, we provide valuable insight into the mechanisms influencing the foraging ecology of wild herbivores.
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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 | 0.001 | 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".