Population mapping of gibbons in Kalimantan, Indonesia: correlates of gibbon density and vegetation across the species’ range
Bibliographic record
Abstract
The first comprehensive survey of gibbons (Hylobates spp.) across Indonesian Borneo was carried out over 3 years to (1) determine whether densities of gibbon species are correlated with vegetation characteristics, and if so, whether the same characteristics are correlated with density across all forest types; and (2) determine population densities in the survey areas and identify threats to the areas. To achieve this, a total of 8 forest blocks were surveyed, involving 53 independent survey locations and repeat surveys in 3 forest blocks. Our data show that gibbons are ubiquitous where there is forest; however, the quality of forest affects population density, forest block size affects longevity of populations, and populations are susceptible to the 'compression effect', i.e. populations occupy smaller fragments at unsustainably high densities. We show the effects of forest disturbance (logging, fire, fragmentation) on gibbon distribution and density and highlight issues for long-term conservation. We discuss the use of minimum cross-sectional area, habitat variables and presence of top foods to determine population density and to identify a threshold below which gibbons cannot persist. We discuss the conservation issues facing all Bornean gibbons, including natural hybrids (H. muelleri H. albibarbis). The answers to these research questions will help mitigate threats to gibbons and their habitat, as well as identify key habitat for gibbon populations within and outside the protected area network.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".