Evaluating the protection of wildlife in parks: the case of African buffalo in Serengeti
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human population growth rates on the borders of protected areas in Africa are nearly double the average rural growth, suggesting that protected areas attract human settlement. Increasing human populations could be a threat to biodiversity through increases in illegal hunting. In the Serengeti ecosystem, Tanzania, there have been marked declines in black rhino ( Diceros bicornis ), elephant ( Loxodonta africana ) and African buffalo ( Syncerus caffer ) inside the protected area during a period when there was a reduction of protection through anti-poaching effort (1976–1996). Subsequently, protection effort has increased and has remained stable. During both periods there were major differences in population decline and recovery in different areas. The purpose of this paper is to analyse the possible causes of the spatial differences. We used a spatially structured population model to analyze the impacts of three factors—(i) hunting, (ii) food shortage and (iii) natural predation. Population changes were best explained by illegal hunting but model fit improved with the addition of predation mortality and the effect of food supply in areas where hunting was least. We used a GIS analysis to determine variation in human settlement rates and related those rates to intrinsic population changes in buffalo. Buffalo populations in close proximity to areas with higher rates of human settlement had low or negative rates of increase and were slowest to recover or failed to recover at all. The increase in human populations along the western boundary of the Serengeti ecosystem has led to negative consequences for wildlife populations, pointing to the need for enforcement of wildlife laws to mitigate these effects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it