Systematic evaluation of a stock unmanned aerial vehicle (UAV) system for small-scale wildlife survey applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) may soon represent a viable option for use in a variety of wildlife research and management applications. This M.Sc. thesis presents an assessment of a small stock UAV system, the CropCam, as a wildlife research instrument in terms of measured performance in specific trial missions and general capacity to meet certain practical requirements. The UAV proved effective for surveying flocks of snow geese (Chen caerulescens), though ineffective for Canada geese (Branta canadensis), and carried out censuses without disturbing birds. It was variably successful at detecting black bears (Ursus americanus), woodland caribou (Rangifer tarandus), white-tailed deer (Odocoileus virginianus) and grey wolves (Canis lupus) in pseudo-natural enclosures, and factors affecting their visibility were analyzed. The UAV is affordable, portable and relatively easy to use, however it is difficult to master, prone to sustaining damage and functionally restricted by camera performance, range and landing site requirements. Promising results demonstrated in this study combined with rapid ongoing development of UAV markets warrant further exploration of wildlife research and management applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it