Miniaturization eliminates detectable impacts of drones on bat activity
Bibliographic record
Abstract
A new way to survey wildlife populations may be possible with advancements in drones, or unmanned aerial vehicles (UAVs) that render aerial technology more accessible and promote surveying in inapproachable habitats. However, it remains unclear whether UAV disturbance deters animals, which would make this method inaccurate for data collection and hazardous to wildlife welfare. This study addresses the viability of UAV use for wildlife research by measuring the effects of UAV flight on acoustic bat detection and comparing bat activity in response to varying UAV models. Depending on the way UAVs effect bat detection rate, it may be possible to identify whether wildlife surveys should be done with UAVs and the drone models best suited for this purpose. The results reveal that larger and louder UAVs deterred significantly more bats, and the smallest and quietest model had no effect on bat detection. Indeed, drone noise was positively correlated with drone size, but drone size had little effect on the range of frequencies emitted. While detecting bats with small and quiet UAVs may be possible, complications still arise with acoustic detection and the species-specific effects of drone flight. The reliability of automatic identification with the acoustic detecting software is limited, as over a quarter of detections were triggered by non-bat noises yet still classified as bats (25.99%). Overall, using drones for wildlife detection should be approached with caution, as this study illustrates that some drones deter and disturb wild bats. If drones are used in wildlife habitat, consider flying smaller and quieter models, which are significantly less disturbing. Otherwise, large and loud drones will likely deter more animals and skew the results of the survey.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".