Estimating potential surface water storage and captured sediment volumes in peatland restoration sites using drones
Bibliographic record
Abstract
Peatland restoration addresses multiple United Nation’s Sustainable Development Goals and offers numerous ecosystem services. Quantifying surface water storage and captured sediment volumes of emplaced dams on restoration sites is highly desirable, for example as evidence to funders, but cannot be practically achieved with field surveys due to the number of sites and number of dams per site. In contrast, camera-equipped drones can efficiently capture high spatial resolution photographs of restoration sites which can be used to construct digital surface models, from which volumetrics can be calculated using Geographical Information Systems. This approach was demonstrated using a 5-year repeat drone survey of a restoration site (∼0.18 km2) containing 125 stone dams and 329 coir rolls. Stone dams and coir rolls were estimated to provide 248.0 m3 of potential surface water storage in July 2019, of which ∼71% was filled by captured sediment by July 2024. Stone dams accounted for ∼83% of potential surface water storage volume in 2019 and ∼68% in 2024, as well as ∼88% of the captured sediment. Efficiently quantifying surface water storage provides evidence to restoration funders with water-related interests and may help with the development of water-related credits/metrics as additional mechanisms to privately finance peatland restoration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".