Aerial and underwater sound of unmanned aerial vehicles (UAV, drones)
Bibliographic record
Abstract
Unmanned aerial vehicles/systems (UAV/UAS, drones) are increasingly being used for terrestrial and marine ecological surveying and research. Studies on the potential disturbance of fauna by UAVs have been sparse, with most reports on the behavioral responses of birds. Responses of marine mammals have been reported in the case of pinnipeds on land, with very limited information on marine mammals at sea. Whether the stimulus was visual (the UAV or its shadow) or acoustic (noise) is unknown. While UAV technology is developing fast, guidelines for the responsible use of UAVs around fauna are lagging behind. We recorded aerial and underwater sound from four aerial drones in different environments. Sound spectra exhibited distinct tones <2 kHz. Median broadband source levels were 77–89 dB re 20 μPa rms at 1 m in air. Under water, median broadband received levels were <100 dB re 1 μPa rms varying with drone altitude, flight mode, and recorder depth. Drone power spectral density exceeded underwater ambient levels by up to 30 dB between 100 and 10 000 Hz. Drone levels were well below those commonly considered in underwater noise regulations. Simple sound propagation and transmission calculations predicted underwater levels within −3.2 to +6.2 dB of measured levels.
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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 | 0.001 | 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) | 0.002 | 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".