Beaver pond identification from multi-temporal and multi- sourced remote sensing data
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
The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America. Despite much progress in targeting habitat management in staging and wintering areas, methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required. This is particularly true for species that breed in remote, inaccessible areas such as the American black duck, an intensively managed game bird in Eastern North America. Although evidence suggests that black ducks prefer productive, nutrient-rich waterbodies, such as beaver ponds, information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge. Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap. In this study, we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario. The use of multi-source data, including a Digital Elevation Model, a Sentinel-2 Multi-Spectral Image, and RadarSat 2 Polarimetric data, enabled us to identify individual beaver ponds on the landscape. Our model correctly identified an average of 83.0% of the known beaver dams and 72.5% of the known beaver ponds based on validation with an independent dataset. This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents. Furthermore, the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it