Miniaturization eliminates detectable impacts of drones on bat activity
Bibliographic record
Abstract
Abstract Advances in operational simplicity and cost efficiency have promoted the rapid integration of unoccupied aerial vehicles (UAVs) into ecological research, yet UAVs often disturb wildlife, potentially biasing measurements. Studies of UAV effects on wildlife to date have focused on UAV trajectory or distance; however, UAV size and noise could be critical variables influencing wildlife responses. Bats are cryptic aerial species that are difficult to survey using conventional means, and so we tested the effectiveness of drone‐based acoustic surveys for bats. We recorded the number of acoustic bat detections with and without a UAV present. We used three small, commercial rotary UAVs varying in size and noise intensity (249, 907, 1,380 g). Larger and louder UAVs deterred significantly more bats, with no effect of take‐off distance on bat activity. The smallest and quietest UAV model had a similar change in bat activity compared with control measurements. Drone noise increased with drone size, but all drones emitted in a similar range of frequencies that overlapped with the larger bat species that were also those most impacted by the UAV. During the 5‐minute surveys, there was no evidence of bat habituation to UAVs although bats returned quickly once the UAV survey ended. We urge wildlife researchers to consider drone size during wildlife surveys. Smaller and quieter models have negligible impacts on wildlife, eliminating the impact of drones on wildlife in some cases.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".