Decline of sable antelope in one of its key conservation areas: the greater<scp>H</scp>wange ecosystem,<scp>Z</scp>imbabwe
Bibliographic record
Abstract
Abstract Land use has major effects on wildlife conservation. We studied variations of sable antelopeHippotragus nigerdensities between 1990 and 2001 in comparison with various land uses in and around Hwange National Park, Zimbabwe. Trends of other ungulates, including elephantLoxodonta africana, were examined simultaneously, because sable may be sensitive to forage and apparent competition and to high elephant densities. Sable densities declined in the whole region, very likely because of adverse rainfall conditions. Densities were constantly higher in the hunting areas and forestry lands than in the national park. Interestingly, elephant densities showed the opposite, with higher densities in the national park than in the adjacent areas. Whether these results reflect a negative effect of high elephant numbers on sable must still be tested directly. Likewise, while habitat characteristics and lion predation did not appear responsible for the higher sable densities outside the national park, they could not be discounted as an influence on the differing sable densities in different land‐use areas. It is clear, however, that high protection status is not always sufficient to ensure adequate conservation of flagship species. We therefore call for further investigations of ecological interactions within protected areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".