Aerial photography collected with a multirotor drone reveals impact of Eurasian beaver reintroduction on ecosystem structure
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.
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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) | 0.003 | 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".