Opportunistic behaviour or desperate measure? Logging impacts may only partially explain terrestriality in the Bornean orang-utan <i>Pongo pygmaeus morio</i>
Bibliographic record
Abstract
Abstract There is a lack of information on how the Endangered Bornean orang-utan Pongo pygmaeus morio moves through its environment. Here we report on a camera-trapping study carried out over 2.5 years to investigate the orang-utan's terrestrial behaviour in Wehea Forest, East Kalimantan, Indonesia. We set 41 camera trap stations in an area of secondary forest, 36 in recently logged forest immediately adjacent to Wehea Forest, and 20 in an area of primary forest in the heart of Wehea Forest. A combined sampling effort of 28,485 trap nights yielded 296 independent captures of orang-utans. Of the three study sites, orang-utans were most terrestrial in recently logged forest, which may be only partially explained by breaks in the canopy as a result of logging activity. However, orang-utans were also terrestrial in primary forest, where there was a closed canopy and ample opportunity for moving through the trees. Our results indicate that orang-utans may be more terrestrial than previously thought and demonstrate opportunistic behaviour when moving through their environment, including using newly constructed logging roads for locomotion, possibly indicating some degree of resilience to human disturbance. This finding is important because of the potential role of sustainably logged forests for orang-utan conservation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".