Ecological and economic analysis of poaching of the greater one‐horned rhinoceros (Rhinoceros unicornis) in Nepal
Bibliographic record
Abstract
Nepal's greater one-horned rhinoceros (Rhinoceros unicornis) faces serious threats from poaching. Poaching of these rhinos is a complex problem, influenced by such diverse factors as the price of rhino horn on the international market, local socioeconomic factors, and the population dynamics of the species. Few studies have attempted to address this complexity. In this study, we model the poaching and population dynamics of the one-horned rhinoceros within an integrated framework of ecological, socioeconomic, political, and legal dimensions. The poaching model for rhinos in Royal Chitwan National Park (RCNP) in Nepal is combined with the population model for the species within a simulation framework and explored under various alternative policy scenarios with differing external socioeconomic and political conditions as well as internal policy response. We predict that, under the current (2003-2005) rhino conservation strategy, poaching would continue to be a major threat to the rhino population in RCNP. Furthermore, the internal policy response must begin to consider external factors such as socioeconomic conditions within the park buffer zone to be more effective in the long run. Finally, we find that, for long-run control, antipoaching policies should be directed at increasing the opportunity costs of poaching by creating better alternative economic opportunities, and at antipoaching enforcement.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".