Censusing large mammals in Kibale National Park: evaluation of the intensity of sampling required to determine change
Bibliographic record
Abstract
Abstract Monitoring programmes are essential for management of large mammal populations because they can detect population change. It is vital that we have the means to evaluate the effectiveness of protected areas. Kibale National Park is a stronghold for large mammal conservation in Uganda. Past wildlife surveys in Kibale focused on specific taxa or areas, but our large mammal survey covered the entire protected area and we evaluated the intensity of sampling required to determine population change. Using line transect sampling, we found that the distribution of large mammals was nonrandom and related to habitat‐type. However, confidence intervals of population estimates revealed that much more intensive sampling was required to detect changes in population density at a time scale reasonable for management. For many species, populations would have to decline by 40–60% for this method to detect population change. Post‐stratification decreased confidence intervals of density estimates slightly, increasing our ability to detect change. However, confidence intervals of estimates were still too large to detect a meaningful population change on a time scale that would allow management to take action. Most incidences of illegal activity were about 5 km from the park boundary; however, animal densities were not lower in this area.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".