Mitigating crop raiding by forest elephants and baboons at Kibale National Park
Bibliographic record
Abstract
Abstract In Africa, most protected forests are in densely human‐dominated landscapes where human–wildlife conflict is intense. We documented farmer perceptions and responses to crop‐raiding wildlife from Kibale National Park, Uganda. Crop raiding was mostly (95%) by baboons ( Papio anubis ) and elephants ( Loxodonta africana ). While the financial loss caused by baboons and elephants did not differ, elephants were perceived as more damaging. Guarding and trenches were perceived as the most effective deterrent strategies for baboons and elephants, respectively. Distance from the park boundary and household income were significantly associated with a greater likelihood of crop raiding. Distance from the park, household head age and the species that raided crops, influenced whether a household applied one or more deterrent strategies. Households headed by women or older adults were most vulnerable, experiencing greater losses to raiding. Patterns of human–wildlife conflict around Kibale forest are complex, but the extent of crop damage was mostly determined by distance from the park and farm socio‐economic status and thus their ability to mitigate or deter raiding. Managing crop raiding requires collaboration between the park and affected farmers to ensure that mutually managed deterrent methods, such as trenching (elephants) and guarding (baboons), are effectively shared, applied and maintained.
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) | 0.003 | 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 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".