Assessment of known impacts of unmanned aerial systems (UAS) on marine mammals: data gaps and recommendations for researchers in the United States
Bibliographic record
Abstract
The development of advanced technologies to enhance conservation science often outpaces the abilities of wildlife managers to assess and ensure such new tools are safely used in proximity to wild animals. Recently, unmanned aerial systems (UAS) have become more accessible to civilian operators and are quickly being integrated into existing research paradigms to replace manned aircraft. Several federal statutes require scientists to obtain research permits to closely approach protected species of wildlife, such as marine mammals, but the lack of available information on the effects of UAS operations on these species has made it difficult to evaluate and mitigate potential impacts. Here, we present a synthesis of the current state of scientific understanding of the impacts of UAS usage near marine mammals. We also identify key data gaps that are currently limiting the ability of marine resource managers to develop appropriate guidelines, policies, or regulations for safe and responsible operation of UAS near marine mammals. We recommend researchers prioritize collecting, analyzing, and disseminating data on marine mammal responses to UAS when using the devices to better inform the scientific community, regulators, and hobbyists about potential effects and assist with the development of appropriate mitigation measures.
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 | 0.059 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".