Tracking the Ghosts of the Himalayas: Snow Leopard Conservation Insights from Satellite Collar Data
Bibliographic record
Abstract
The snow leopard (Panthera uncia) inhabits mountainous areas of Central and South Asia, including the northern region of Nepal, sharing borders with both India and China. For low-density, far-ranging species inhabiting inaccessible terrain, GPS collars are effective, given the volume of data, the accuracy of locations, and the ability to track numerous individuals simultaneously. For the first time, we analyzed spatiotemporal dynamics using satellite telemetry data to understand differences in movement patterns, time budgeting, and home range utilization between male and female snow leopards, satellite-collared in the northeastern Himalayas of Nepal. The ecological behaviour and time budgeting of snow leopards were modeled by the hidden Markov model (HMM) whereas home ranges were estimated and compared by various methods, such as the minimum convex polygon (MCP) and Kernel Density Estimation (KDE) methods with href and the Brownian bridge movement model. This research showed clear sex differences in movement patterns and home range sizes, which indicate different ecological needs and resource-use techniques. Furthermore, this study provides reliable information on snow leopards from the telemetry data and links it to conservation implications in eastern Nepal to ensure their long-term survival, promote coexistence, and foster cross-border collaboration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".