Spatial Interactions of African Elephant <i>Loxodonta africana</i> with the Resident Communities Bordering a Semi-arid National Park in Northern Kenya
Bibliographic record
Abstract
Northern Kenya is endowed with rich ecosystems that support populations of African Elephant, Loxodonta africana . The changing human demographics accompanied by climate change have exacerbated the incidences of human-wildlife conflicts, especially around the protected areas – the parks and reserves. Recorded incidence of conflict between humans and elephants, in particular crop-raiding, is increasing in rural Africa, undermining efforts to conserve biological diversity. Gaining an understanding of the underlying determinants of human–elephant conflict is important for the development of appropriate management tools. We examined the causal factors of the human-elephant conflicts around Marsabit National Park, and evaluated potential mitigation strategies. Our findings showed that incidences of elephant conflicts affected both agricultural farms and livestock around the park. Seasonality (wet and dry) and distance from the park boundary also influenced the frequency of human elephant conflicts. Our findings further showed that the problem animal control unit presently organized by the wildlife management authority in Kenya – the Kenya Wildlife Service had little impacts in mitigating the existing human-elephant conflicts in the area. We discuss the implications of our findings in the contexts of land use planning and partitioning of resources around the Park, and the need for community engagement as a long-term strategy for winning support for conservation of African elephant in northern Kenya.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".