Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?
Bibliographic record
Abstract
Since the turn of the century, emerging unmanned aircraft systems (UAS) have found increasingly diverse applications in wildlife science as convenient, very high-resolution remote sensing devices. Achieved or conceptualized applications include optical surveying and observation of animals, autonomous wildlife telemetry tracking, and habitat research and monitoring. As the technology continues to progress and interest from the wildlife science community grows, there may yet be much untapped potential for UAS to contribute to the discipline. We present a review of the published primary literature on the application of UAS in wildlife science and related fields. This is followed by a systematic review of the broader wildlife science literature published since the turn of the century to assess where UAS are likely to make important contributions going forward based on the trends that have emerged thus far. UAS, in particular small lightweight models, are generally well suited for collecting data at an intermediate spatial scale between what is easily coverable on the ground and what is economically coverable with conventional aircraft. They are particularly useful for monitoring wildlife and habitats in places that are difficult to access or navigate from the ground, as well as approaching sensitive or aggressive species.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".