Does the presence of elephant dung create hotspots of growth for existing seedlings?
Bibliographic record
Abstract
Abstract Megaherbivores play a central role in the evolution and functioning of ecosystems. In tropical forests elephant species are some of the few remaining megaherbivores. Through elephant foraging, nutrients that would be locked in leaves and stems, taking months or years to decay, are quickly liberated for use. In 10 experimental sites in Kibale National Park, Uganda, we set up 10 pairs of plots (4 × 4 m), each pair involved one treatment, elephant dung addition, and one control. After 1 y, we quantified growth (height and leaf number) and survival of young light-demanding (12) and shade-tolerant (19) plant species (439 stems in total). In general, the addition of elephant dung did not increase seedling growth, and it only increased the number of leaves in shade-tolerant plants with a large initial number of leaves. Researchers have speculated that the loss of elephants would shift the composition of African forests to slow-growing tree species. However, this is not supported by our finding that shows some slow-growing shade-tolerant plants grew more new leaves with additional nutrient input from elephant dung, a condition that would occur if elephant numbers increase.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".