Drone noise differs by flight maneuver and model: implications for animal surveys
Bibliographic record
Abstract
Drones are becoming a common tool for animal monitoring; however, sound emitted from drones may disturb animals and bias survey results. Understanding noise levels produced by different flight maneuvers, altitudes (i.e., above ground level (AGL)), and drone models could mitigate animal disturbance during surveys. We measured maximum sound (dB) emitted during three flight maneuvers (hovering, flyover, and turning) among eight AGLs (15–120 m) and two vertical maneuvers (ascending and descending) for four commercially available quadcopter drone models (DJI Matrice 300, Matrice 200, Phantom 3, and Autel Evo II), accounting for wind speed and comparing to ambient (background) noise. Ascending, descending, and hovering produced more noise compared to flyover and turning maneuvers. One large drone (Matrice 200, 4.7 kg) produced more noise than the two smaller drones (Evo II, 1.2 kg and Phantom 3, 1.1 kg). However, the largest drone (Matrice 300, 6.4 kg) produced noise similar to smaller models and was the quietest among all models from 75 to 120 m AGL, providing potential size advantages with less noise disturbance. Our results indicate that flights consisting of flyover and turning maneuvers likely cause less noise disturbance than surveys with prolonged hovering over animals.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".