Impacts of shared home range on human-wildlife conflicts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human-wildlife conflicts (HWCs) are becoming increasingly common in landscapes altered by human activity, often threatening both livelihoods and wildlife conservation. We investigated HWCs in two communities: Bbaale village near Nabugabo Research Site in Uganda (73 households surveyed in 2019) and Manyangalo village near the Lewa-Borana Conservancy in Kenya (50 households surveyed in 2022) using descriptive statistics. We assessed how socioeconomic factors influenced household responses to HWCs using ordinal logistic regression models and explored community perceptions of living near a research site or conservancy. Our results showed that HWCs at Bbaale were reported as more severe (62%), often involving crop damage and livestock losses, while encounters near Manyangalo occurred more frequently (86%) but caused less damage. Households in Bbaale used a range of management strategies, including banging tins (86%), using dogs (60%) and scarecrows (59%), whereas Manyangalo residents primarily relied on noisemaking (100%). Larger cultivable areas were associated with more reported conflicts, and individuals with secondary education reported less severe impacts. Despite differences in HWC experiences, most respondents (Bbaale: 88%, Manyangalo: 86%) in both villages expressed positive views of the research site or conservancy, suggesting local support for conservation initiatives. These findings emphasize the importance of tailoring HWC management strategies to local conditions and community needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it