An examination of trends in the growing scientific literature on approaching wildlife with drones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drones or unoccupied aerial vehicles are rapidly being used for a spectrum of applications, including replacing traditional occupied aircraft as a means of approaching wildlife from the air. Though less intrusive to wildlife than occupied aircraft, drones can still cause varying levels of disturbance. Policies and protocols to guide lowest-impact drone flights are most likely to succeed if considerations are derived from knowledge from scientific literature. This study examines trends in the scientific literature on using drones to approach wildlife between 2000 and 2020, specifically in relation to the publication types, scientific journals that works are published in, purposes of drone flights reported, taxa studied, and locations of studies. From 223 publications, we observed a large increase in relevant scientific literature, the majority of which were peer-reviewed articles published across 86 scientific journals. The largest proportion of peer-reviewed research articles related to aquatic mammals or aquatic birds and the use or trial of drone flights for conducting population surveys, animal detection, or investigations of animal responses to drone flights. The largest proportion of articles were studies conducted in North America and Australia. Since animal responses to drone flights vary among taxa, populations, and geographic locations, we encourage further growth in the volume of relevant scientific literature needed to inform policies and protocols for specific taxa and (or) locations, particularly where knowledge gaps exist.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it