<b>The status of Nepal’s mammals</b>
Bibliographic record
Abstract
The main objectives of the Nepal National Mammal Red Data Book (RDB) were to provide comprehensive and up-to-date accounts of 212 mammal species recorded in Nepal, assess their status applying the IUCN Guidelines at Regional Levels, identify threats and recommend the most practical measures for their conservation. It is hoped that the Mammal RDB will help Nepal achieve the Convention on Biological Diversity target of preventing the extinction of known threatened species and improving their conservation status. Of the 212 mammal species assessed, 49 species (23%) were listed as nationally threatened. These comprise nine (18%) Critically Endangered species, 26 (53%) Endangered species and 14 (29%) Vulnerable species. One species was considered regionally Extinct. A total of seven species (3%) were considered Near Threatened and 83 species (39%) were Data Deficient. Over sixty percent of Nepal’s ungulates are threatened and almost half of Nepal’s carnivores face extinction (45% threatened). Bats and small mammals are the least known groups with 60 species being Data Deficient. Habitat loss, degradation and fragmentation are the most significant threats. Other significant threats include illegal hunting, small and fragmented populations, reduction of prey base, human wildlife conflict and persecution, climate change, invasive species, disease and inadequate knowledge and research. Adequate measures to address these threats are described. It was also concluded that re-assessments of the status of certain mammal groups be carried out every five years and the setting up of a national online species database and mapping system would also greatly help in land-use planning and policies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".