Aerial photography collected with a multirotor drone reveals impact of Eurasian beaver reintroduction on ecosystem structure
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
Beavers are often described as ecological engineers with an ability to modify the structure and flow of fluvial systems and create complex wetland environments with dams, ponds, and canals. Consequently, beaver activity has implications for a wide range of environmental ecosystem services including biodiversity, flood risk mitigation, water quality, and sustainable drinking water provision. With the current debate surrounding the reintroduction of beavers into the United Kingdom, it is critical to be able to monitor the impact of beavers upon the environment. This study presents the first proof of concept results showing how a lightweight hexacopter fitted with a simple digital camera can be used to derive orthophoto and digital surface model (DSM) data products at a site where beavers have recently been reintroduced. Early results indicate that analysis of the fine-scale (0.01 m) orthophoto and DSM can be used to identify impacts on the ecosystem structure including the extent of dams and associated ponds, and changes in vegetation structure due to beaver tree-felling activity. Unmanned aerial vehicle data acquisition offers an effective toolkit for regular repeat monitoring at fine spatial resolution, which is a critical attribute for monitoring rapidly changing and difficult to access beaver-impacted ecosystems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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