Assessing prey selection of snow leopard in Mt. Kangchenjunga, Nepal
Bibliographic record
Abstract
In spring of 2012, we studied the feeding habits of snow leopard using a comprehensive approach that combines fecal genetic sampling, macro and microscopic analysis of snow leopard diets and direct observation of Naur and livestock in Kangchenjunga Conservation Area of east Nepal. Out of collected 88 putative snow leopard scat samples from 140 transects (290 km) in 27 (4*4 km2) sampling grid cells, 83% were confirmed to be from snow leopard. The genetic analysis accounted for 19 individual snow leopards (10 male and 9 female) with a mean population size estimate of 24 (95% CI: 19- 29), and an average density of 3.9 snow leopards/100 km2 within 609 km2. Total available prey biomass of Naur and Yak was estimated at 355,236 kg (505 kg yak/km2 and 78 kg Naur/km2). From the available prey biomass, we estimated snow leopards consumed 7% annually which was comprised of wild prey (49%), domestic livestock (45%), and 6% unidentified items. The estimated 47,736 kg Naur biomass gives a snow leopard-Naur ratio of 1: 59 on a weight basis. The proportion of young Naur was estimated at 17%, with an almost double predation rate at 28%. Predators such as common leopard and wolf share the same habitat and might compete with snow leopard for prey which will likely influence future predator-prey associations in KCA. Along with livestock insurance scheme improvement, there needs to be a focus on improved livestock guarding as well as engaging and educating local people to be citizen scientists on the importance of snow leopard conservation, involving them in long-term monitoring programs and promotion of ecotourism.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".