Wildlife monitoring with unmanned aerial vehicles: Quantifying distance to auditory detection
Bibliographic record
Abstract
ABSTRACT There is growing application of small unmanned aerial vehicles (UAVs) for detecting, enumerating, and monitoring wildlife. However, little is known about how the sound from UAVs may be affecting wildlife being studied. We quantified sound levels of 2 UAVs to determine minimum altitudes they can fly before being aurally detected by wildlife. We tested a small quadcopter (SkyRanger; Aeryon Labs, Inc., Waterloo, ON, Canada) and a fixed‐wing platform (eBee; Sensefly Ltd., Cheseaux‐sur‐Lausanne, Vaud, Switzerland) at the University of Calgary in Calgary, Alberta, Canada, between 1000 and 1200 hours on 27 September 2014. We modeled sound propagation and attenuation in relation to the lower hearing thresholds for 3 game species and 2 species of predators. Results indicate that the UAV sound levels differed in the lower frequency ranges, but were otherwise similar above 1.25 kHz. The domestic cat ( Felis silvestris catus ) has the lowest hearing threshold, with the capacity to hear both UAVs from the furthest distance; whereas, the mallard ( Anas platyrhynchos ) has the greatest hearing threshold, which means UAVs can be closer before being aurally detected. Because flying height is related to image resolution, our results indicate that the ability to detect some wildlife species may be affected by the need to fly higher to minimize sound disturbance, potentially requiring higher resolution cameras than those currently used. Also, additional flight permitting may be required if modeling indicates a UAV must fly at a greater height to avoid a behavioral response by the target species. © 2016 The Wildlife Society.
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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 | 0.001 | 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".