Monitoring wildlife using long‐endurance solar‐electric UAVs
Bibliographic record
Abstract
This report discusses the effectiveness of using small solar‐electric UAV (uncrewed aerial vehicles) for aerial wildlife monitoring. We review four years of aerial wildlife monitoring missions using a 5.5‐m wingspan, solar‐electric UAV that was equipped with a gimballed IR/RGB camera. The effectiveness criteria included the ease of operability, the ability to collect applicable data, the disturbance to animals and humans, and the ability to complete large‐area wildlife surveys. The solar‐electric aircraft was operated for approximately 450 h of monitoring missions under various environmental conditions during all seasons, including at temperatures of −30°C. The UAV was flexible with respect to launch and recovery locations, for example, operating from frozen lakes, fields and roads. Due to the low acoustic footprint of its electric propulsion system, the aircraft did not appear to disturb wildlife, despite flying at relatively low altitudes of 120 m or less above the ground. Its solar‐electric architecture allowed for flight times in excess of eight hours. Subsequently, the aircraft was capable of monitoring relatively large areas. In summary, small solar‐electric UAV have shown merit for wildlife monitoring missions and may serve as a valuable tool for wildlife biologists.
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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 | 0.000 | 0.000 |
| 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".