Ecological and anthropogenic factors influencing the Summer habitat use of <i>Bos gaurus</i> and its conservation threats in Chitwan National Park, Nepal
Bibliographic record
Abstract
Background: ) is listed as a vulnerable species in the IUCN Red List of threatened species due to the rapid population decline caused by human pressures in their habitats. To develop successful conservation plans, it is essential to understand the relationship between Gaur and their habitats. This study aimed to investigate the factors influencing Gaur habitat use and identify and rank conservation threats in Chitwan National Park, Nepal. Methods: in July and August 2022. We used binary logistic regression models to determine the collected ecological and anthropogenic factors influencing the occurrence of Gaur and the relative whole-site threat ranking method to rank conservation threats. Results: dominated forests, and nearest distance to road/path/firelines, while decreasing with the presence of predators. Uncontrolled fire, invasive species, human disturbances, and climate change were ranked as the most prevailing threats to Gaur in our study area. Conservation managers should implement effective habitat management interventions, such as construction of waterhole, firelines maintenance, grassland management, and control of invasive species in the potential habitats, to safeguard and maintain the sustainability of Gaur populations and associated herbivores. Future studies should consider larger geographical settings and multiple seasons, and habitat suitability assessments should be conducted to determine current and future suitable habitat refugia for Gaur and other threatened wildlife species at the landscape level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".