Data from: Are crop fields pharmacies for megaherbivores? From ecophysiological studies of elephant (<em>Loxodonta cycotis</em>) crop raiders in Gabon
Bibliographic record
Abstract
Damage to crops is a major cause of human-elephant conflict (HEC) in elephant range states. Elephant crop raiding drives farmers' resentment against elephants and reduces local community support for wildlife conservation. While elephant crop raiding ecology is well studied, further investigations on HEC mitigation strategies are still needed. Thus, there is a need to focus on less investigated areas, such as the physiological drivers of elephant crop-raiding behavior, using multidisciplinary sciences. Two physiological proxies, gastrointestinal parasite infestations (GPI) and fecal glucocorticoid metabolite (fGCM) concentrations, common in animal ecophysiology, were used to help understand differences or motivations in the preferences of crops by elephant raiders. The results show, for the first time, that forest elephants may increase the frequency of crop raiding according to GPI, indicating a self-medication behavior. Increases in parasitism prevalence (PP) and parasitism intensity (PI) in sampled boluses led to 28% and 0.16% more intakes of all crops, respectively. Parasitism prevalence (PP) increases in elephant boluses also led to 16% and 25% more bananas and papaya intakes, respectively, while PI increases in boluses led to 0.1% more intakes of both bananas and papaya plants. No such predictions were found for other crops (cassava and palm plant), nor for natural food species. Furthermore, fGCM concentrations were not related to elephant crop raiding. Results highlight a trade-off between the benefit of elephants raiding crops and the danger of encountering farmers by adopting nocturnal crop-raiding behaviours.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".