Effects of Human Presence on Chimpanzee Nest Location in the Lebialem-Mone Forest Landscape, Southwest Region, Cameroon
Bibliographic record
Abstract
In several areas of Africa, great apes experience increasing predation pressure as a result of human activities. In this study, terrestrial and arboreal nest construction among chimpanzee (Pan troglodytes ellioti) populations was investigated in the Lebialem-Mone Forest Landscape (LMFL), Southwest Region, Cameroon, to examine the anthropogenic effects on nest location. Data on the height, distribution and approximate age of chimpanzee night nests were collected during two 4-week primate field surveys (July to August 2010; July 2011) at two field sites (Bechati and Andu) within the LMFL. Data were collected using the line transect method. Chimpanzee night nests were categorized by their location: arboreal versus terrestrial. During the two field surveys, arboreal night nests were the most frequently constructed nest type at both sites, and the only type of night nest constructed at Bechati. Terrestrial night nests were also constructed at Andu. The main difference between these two sites is the level of human predation and agricultural development. At Bechati chimpanzees inhabit forest regions around dense, expanding villages and are regularly hunted by humans. However, at Andu the chimpanzee populations are not under the same threat. Therefore, terrestrial night nest construction in the LMFL appears to be a behavior exhibited where there is less human presence.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".