Where did the soil go? Quantifying one year of soil erosion on a steep tile-drained agricultural field
Bibliographic record
Abstract
Distributed measurements of agricultural erosion at the farm-scale are needed to evaluate both the on and off-site impacts of sediment redistribution. While best management practices have been shown to reduce surface erosion rates and improve water quality, their farm-scale effects can be difficult to quantify. In this study we use imagery from an unmanned aerial vehicle (UAV) and structure-from-motion multi-view stereo (SfM-MVS) to quantify erosion rates and their effects on crop yield across a 15.9-ha agricultural field. Our results highlight that the installation of catch basins were able to stop 159.52 t of sediment and associated nutrients from entering the waterway adjacent to the study site over the course of one year, corresponding to an erosion rate of 18.83 t ha−1 yr−1 across six study basins. Poor soil structure resulting from downslope tillage reduced crop yields on topographic shoulders of the study site, while accelerated water erosion processes were responsible for large areas of washout that caused highly variable crop growth in footslopes. The highest crop yields were associated with backslopes and topographically flat regions of the field which experienced minimal erosion. Change-detection results showed that UAV imagery was able to reliably quantify depositional plumes and was comparable to that of a terrestrial laser scanner (TLS) using a ± 0.04 m confidence interval.
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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 | 0.000 | 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 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".