Hillslope sediment fence catch efficiencies and particle sorting for post‐fire rain storms
Bibliographic record
Abstract
Abstract Sediment fences are often used to monitor hillslope erosion, but these can underestimate sediment yields due to overtopping of runoff and associated sediment. We modified four sediment fences to collect and measure the runoff and sediment that overtopped the fence in addition to the sediment deposited behind the fence. Specific objectives were to: (1) determine the catch efficiency of sediment fences measuring post‐fire hillslope erosion; (2) assess particle sorting of sand, silt/clay, and organic matter from each hillslope through the sediment fence and subsequent runoff collection barrels; (3) evaluate how catch efficiency and particle size sorting relate to site and rainfall‐runoff event characteristics; and (4) use runoff simulations to estimate sediment fence volumes for future post‐fire monitoring. Catch efficiency ranged from 28 to 100% for events and 38 to 94% per site for the entire sampling season, indicating a relatively large underestimation of sediment yields by sediment fences. Most of the eroded sediment had similar proportions of sand and silt/clay as the hillslope soils, but the sediment behind the fence was significantly enriched in sand while the sediment that overtopped the fence was more strongly enriched in silt/clay. The sediment fences had capacities of 3 m 3 for hillslopes of 0.19–0.43 ha, but simulations of runoff for 2‐ to 100‐year storms indicate that the sediment fences would need a capacity of up to 240 m 3 to store all of the runoff and associated sediment. More accurate measurements of sediment yields with sediment fences require either increasing the storage capacity of the sediment fence(s) to accommodate the expected volume of runoff and sediment, reducing the size of the contributing area, or directly measuring the runoff and sediment that overtop the fence. © 2020 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 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".