Can we rely on forest reserves for primate conservation?
Bibliographic record
Abstract
Tropical forests contain much of the world's biodiversity, yet their rate of decline is increasing. The strategy most frequently used to protect this biodiversity is to make parks and reserves. While there is a great deal of research on the effectiveness of parks for protecting biodiversity, there is little research on how well extractive reserves conserve biodiversity. Here, we evaluate the effectiveness of four forest reserves in western Uganda at maintaining populations of primates and compare census data from the reserves to data from the neighbouring well-protected Kibale National Park. The relative abundance of the five most common primates in the park was approximately four times that of the forest reserves. In the forest reserves, evidence of new human encroachment was seen every 500 m, while in the park it was seen every 100,000 m. Two recommendations emerge from our research: (i) for forest reserves, such as those studied here, to have conservation value for primates, extraction must be reduced and (ii) until the long-term viability of the populations in forest reserves can be ascertained, they should not be considered in estimates of the sizes of endangered species protected ranges.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".