Establishment of Broad‐leaved Thickets in Serengeti, Tanzania: The Influence of Fire, Browsers, Grass Competition, and Elephants<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT The role ofEuclea divinorumin the establishment of broad‐leaved thickets was investigated in Serengeti National Park, Tanzania. Thickets are declining due to frequent fires, but have not reestablished when fires have been removed. Seedlings ofE. divinorum, a fire‐resistant tree, were found in grassland adjacent to thickets and as thicket canopy trees and may function to facilitate thicket establishment. Seedlings of thicket species were abundant underE. divinorumcanopy trees but not in the grassland, indicating thatE. divinorumcan facilitate forest establishment. We examinedE. divinorumestablishment in grassland by measuring survival and growth of seedlings with respect to fire, browsers, elephants, and competition with grass. Seedling survival was reduced by fire (50%), browsers (70%), and competition with grass (50%), but not by elephants. Seedling growth rate was negative unless both fire and browsers, or grass was removed. Establishment of thickets viaE. divinorumis not occurring under the current conditions in Serengeti of frequent fires, abundant browsers, and dense grass in riparian areas. Conditions that allowed establishment may have occurred in 1890–1920s during a rinderpest epizootic, and measurements of thicket canopy trees suggest they established at that time.
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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".