Habitat and Anthropogenic Determinants of Chinese Pangolin ( <i>Manis pentadactyla</i> ) Burrow Occupancy in Udayapur, Eastern Nepal: Implications for Site‐Specific Conservation
Bibliographic record
Abstract
ABSTRACT Chinese pangolins are found from lowlands to mid‐hills in Nepal and are increasingly vulnerable to extinction due to extensive illegal trade and habitat fragmentation, particularly outside the protected areas network. The information about their ecological preferences in human‐dominated landscapes beyond protected areas is essential for effective habitat management and conservation. This study aimed to assess the density and characteristics of Chinese pangolin burrows, and analyze the key ecological and anthropogenic factors influencing their burrow occurrence in Katari municipality, Udayapur District, Eastern Nepal. We employed a total of 52 strip transects, each of 500 m long and 20 m wide, divided into two 250‐m sections on either side of the main transect and spaced 200 m apart to examine the pangolin burrows. We recorded a total of 124 active burrows and 114 inactive burrows, with a density of 2.38 active burrows per hectare. A higher number of burrows (80.25%) were recorded in the forest habitat, suggesting a discrepancy in burrow distribution across the study area. The majority of the burrows were distributed at 601–800 m above sea level (33.61%), 35°–45° slope (53.78%), and 0%–25% canopy cover (52.94%). The Welch Two‐Sample t ‐test suggested that there was a significant difference in the burrow opening diameter between feeding and resting burrows. Among the 15 pre‐determined ecological parameters measured during the study, a generalized linear mixed model (GLMM) identified 9 ecological parameters as significant variables influencing the Chinese pangolin burrow occurrence in the study area. These were elevation, habitat type, canopy cover, livestock grazing, anthropogenic disturbance, presence or absence of fire, and nearest distance to water sources, roads, and settlements. Long‐term habitat management plans and strategies are recommended, with an emphasis on minimizing anthropogenic disturbances both in forest and farmland areas, and proper preservation of nearby water sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".