Balancing boundaries: elephant movements in the changing landscape around Murchison Falls National Park, Uganda
Bibliographic record
Abstract
We examine elephant movement and human–elephant conflict around Murchison Falls National Park (MFNP) in northern Uganda, a landscape shaped by agricultural expansion, displacement, and ongoing land conflict. Elephant movements beyond the Park's boundaries are still poorly understood. Between 2019 and 2022, we collected data through GPS collaring (n = 5), ground observations, and crop-raiding surveys. Collared elephants favoured areas near water, tree cover, and lower elevations, with use of human-occupied areas ranging from 0–24%. Outside MFNP, they sheltered near refuge sites during the day and raided farms at night. Raiding typically targeted mid-growth crops with a median of 30% damage per affected farm. Calves were present in approximately 20% of raids, and some groups exceeded 30 individuals. Only 9% of farmers used deterrents beyond reactive chasing. Snaring injuries were recorded in 32% of observed elephants, indicating persistent poaching pressure. Unresolved land tenure, community distrust, and evictions further complicate elephant management outside the Park. We recommend prioritizing elephant protection inside MFNP by strengthening anti-snaring operations. Outside the Park, boudary communities require training for coordinated night-guarding and locally suitable low-cost deterrents. A feasibility study, in consultation with affected farmers, shoudl assess limited fencing on community lands while retaining a central corridor linking MFNP to areas further north.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".