The status of anthropogenic threat at the people-park interface of Bwindi Impenetrable National Park, Uganda
Bibliographic record
Abstract
SUMMARY Effective management of anthropogenic threats is key to sustaining biological diversity in protected areas. Types and distribution of threats to Bwindi Impenetrable National Park, Uganda were investigated to assess the Park's status 12 years after it was upgraded from a forest reserve to a national park. Bwindi, like many tropical forested parks, is surrounded by dense human populations. Threats were quantified in 104 1-km edge-interior transects set around the Park. The distribution of threats was patchy and was most common within 300–350 m of the edge. The commonest threat was harvesting of wood and poles. Other threats included occurrence of exotic species, degradation of adjacent habitat fragments and high impact of problem animals on some of the neighbouring communities. The fact that threats were primarily associated with the edges of the Park, when previously they were widespread throughout the Park, suggests that illegal resource harvesting has been reduced since the forest was upgraded to a national park. Park legislation, enforcement and related conservation efforts have been effective, and there should be increased effort to manage the people-park interface. Edge-based assessments appear to be useful for quantifying threats to protected areas and identifying areas in which they are concentrated.
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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 | 0.002 |
| 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 | 0.002 |
| 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".