The utilization of large savanna trees by elephant in southern Kruger National Park
Bibliographic record
Abstract
Abstract: Elephant are believed to be one of the main ecological drivers in the conversion of savanna woodlands to grassland. We assessed the impacts of elephant on large trees (≥5 m in height) in the southern section of the Kruger National Park. Tree dimensions and utilization by elephant were recorded for 3082 individual trees across 22 transects (average length of 3 km and 10 m wide). Sixty per cent of the trees exhibited elephant utilization and 4% were dead as a direct result of elephant foraging behaviour. Each height class of tree was utilized in proportion to abundance. However, the size of the tree and the species influenced the intensity of utilization and foraging approach. Sclerocarya birrea was actively selected for and experienced the highest proportional utilization (75% of all trees). Interestingly, the proportion of large trees that were utilized and pushed over increased with distance from permanent water, a result which has implications for the provision of water in the KNP. We conclude that mortality is likely to be driven by a combination of factors including fire, drought and disease, rather than the actions of elephant alone. Further investigation is also required regarding the role of senescence and episodic mortality.
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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 | 0.000 |
| 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) | 0.001 | 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".