Comparing the Sources of Sediment Retained by Beaver Dams and Beaver Dam Analogs
Bibliographic record
Abstract
Abstract Beavers modify riverine systems by building dams that alter downstream fluxes of water and sediment. Where beavers have been lost and stream channels degraded, beaver dam analogs (BDAs) are being used to mimic the effects of beaver engineering. Central to the success of these structures in accelerating stream recovery is creating similar ecosystem responses as beaver dams including sediment retention. Unknown is the relative importance of beaver actions versus erosion in the catchment in generating the retained sediment. This study tested the viability of sediment fingerprinting to determine the source of sediment retained by beaver dams and BDAs in a watershed in Alberta, Canada. Concentrations of 29 elements were measured as potential tracers from known sediment sources: upland, terrace, stream bank, and beaver canal. Virtual mixture tests, used to compare the computed source estimates with known source mixtures, revealed that sediment fingerprinting is a robust method for identifying sources of sediment retained by beaver ponds and BDAs. The un‐mixing model results indicate that on average 56% of the sediment retained by the beaver dams originated from terraces, 23% from uplands, and 13% from beaver canals. About 89% of sediment retained by the BDAs originated from eroding stream banks. We conclude that the geomorphic effects of beavers and their dams are more diverse, resulting in more diverse sources of sediment retained by their dams. This differentiates beaver dams from BDAs. The study has implications for informing management practices that involve beavers and beaver mimicry.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".