Extinction risk in eastern African flora
Bibliographic record
Abstract
Hippos are selective nocturnal grazers that are capable of modifying the landscape by creating grazing lawns.Lawns occur as a consequence of intensified, consistent cropping of tall bunch grass species, the effect of which is to modify vegetation dynamics within the landscape, such that the plant diversity of the landscape is enhanced, forage quality is improved, soil nutrient availability is augmented, and fire regimes may be altered.However, there is much ambiguity as to whether factors such as water table depth contribute toward lawn formation in the iSimangaliso Wetland Park system, or, whether lawns can be created based solely on feedbacks between past grazing events and future ones.Sampling took place in July 2012, during the dry season.Data were collected along 30 linear transects; 20 located in grazed vegetation (lawn sites) and 10 in adjacent non-grazed vegetation (non-lawn sites), and species composition, vegetation height, depth to the water table, soil C% and the fire margin were measured.Here we show that hippos were the primary biological agents contributing toward lawn formation, as water table depth was not a significant predictor of vegetation height.However, changes in vegetation across the landscape may be accentuated by soil type, grazing intensity and topography.The implication for park management is that culling the local hippo population is likely to have substantial ramifications on forage availability for smaller herbivores, plant community composition, as well as fire dynamics.However, the abiotic components of the system cannot be overlooked, and monitoring the effect of rainfall on flooding and on forage biomass will become increasingly important in a changing climate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".