Nutrient deficit rather than distance of farming activities from the boundary of protected areas drives crop raids by elephants
Bibliographic record
Abstract
Human-wildlife conflicts resulting from the raiding of agricultural crops by elephants are among the major challenges affecting the conservation of this flagship species. Several studies have pointed at human activities, such as farming nearer to protected areas boundaries, as the main driver of these conflicts. Studies comparing the quality of food between agricultural crops and the natural vegetation in the elephants’ natural habitats as the potential key driver of these conflicts, are almost non-existent. We tested if there were differences in the incidences of crop raids with distance of farming away from protected area boundaries. Further, we compared the food quality of agricultural crops to the natural vegetation in the mammals’ habitat in and around Kasanka National Park in Zambia. Surprisingly, there was no difference in the incidences of crop raids relative to the distance of farming away from the protected area boundary. Further, the results show higher protein, energy, and moisture composition in the often-raided agricultural crops than the natural vegetation. However, the natural vegetation had higher ash, vitamin C, and fiber composition relative to agricultural crops. Broadly, our results suggest that the natural vegetation in the wild may not necessarily have all the key nutrients in adequate proportions to meet the body requirements of elephants. Therefore, elephants raid the crops to compensate this nutrient deficit, irrespective of how far the farms may be situated from the boundaries of protected areas.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".