The savanna tree<i>Acacia polyacantha</i>facilitates the establishment of riparian forests in Serengeti National Park, Tanzania
Bibliographic record
Abstract
Abstract: Forests are being converted to grasslands and croplands across Africa and natural regeneration of forests is typically poor. In Serengeti National Park, Tanzania, the savanna tree speciesAcacia polyacanthaestablished in riparian grasslands and forest trees subsequently established within these stands. We examined the conditions for establishment of: (1)A. polyacanthaand (2) riparian (non-Acacia) forests. Fire was excluded from three grassland areas for 5 y allowingA. polyacanthato establish during 1999 when dry-season rainfall was high. The seedlings of forest tree species did not establish in grasslands, but were found in largeA. polyacanthastands (> 0.3 ha) with reduced grass cover (< 10%), higher cover of herbs (> 80%) and thorny shrubs (> 90%). Seeding survival was high in large stands (0.87 y−1), but declined in artificial canopy gaps due to the ingrowth of grasses (0.21 y−1) and subsequent fires (0.07 y−1). Shrub removal also reduced seedling survival (0.46 y−1) due to browsing by antelope. We propose that: (1)A. polyacanthaestablishes in pulses perhaps as infrequently as twice per century, and (2) riparian forests in Serengeti have established via facilitation under larger stands where shade excludes grass, and therefore fires and thorny shrubs exclude browsers.
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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) | 0.002 | 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".