A preliminary assessment of using conservation drones for Sumatran orang-utan (<i>Pongo abelii</i>) distribution and density
Bibliographic record
Abstract
To conserve biodiversity, scientists monitor wildlife populations and their habitats. Current methods have constraints, such as the costs of ground or aerial surveys, limited resolution of freely available satellite images, and expensive high-resolution satellite images. Recently researchers started to use unmanned aerial vehicles (UAVs or drones) for wildlife and habitat monitoring. Here we tested whether we could detect nests of the critically endangered Sumatran orang-utan on imagery acquired from a camera-mounted drone to determine distribution and density. Our results show that the distribution of nests compares well between aerial and ground-based surveys and that relative density (nest/km) shows a significant correlation between these two survey types. The results also indicate that both methods can be used to detect significant differences in relative density between previously degraded reforested and enriched areas. We conclude that orang-utan nest surveys from drones are a promising survey method to determine distribution and (relative) density of Sumatran orang-utans and perhaps other ape species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".