Using a scaled model to assess the performance of different sediment augmentation strategies in a restored channel
Bibliographic record
Abstract
Sediment augmentation is an increasingly popular strategy for restoring rivers, mitigating flooding, and improving fish habitat. However, it is still unclear where along a river sediment seeding produces effective results, or what the fate of sediment is once placed under different flow conditions. Using a set of flume experiments conducted on a scaled pool-riffle reach, we assess the evolution of the planned augmented sediment cover in the Penticton Creek restoration project in British Columbia, Canada. We investigated three sediment seeding patterns described based on the seeding locations through the pool as: Head-seed (HS), tail-seed (TS), and full-seed (FS). For each seed pattern, the reach response to flood events with magnitudes ranging from 2- to 100-year return discharges was assessed. Our results show that while the FS channels are superior at retaining alluvial materials during low floods (i.e., 2-yr), they rapidly lose this ability as the flood magnitude increases. Examining maps of bed erosion reveals that in the FS channels, nearly all the pool area is vulnerable to a high risk of bed scour during high flood events. However, the bed scour only occurred in the HS and TS channels at locations where sediment had been seeded, dispersing eroded materials throughout the pool area. Our findings suggest that for restoring fish habitat in channels with limited sediment supplies, HS and TS seeding patterns are more effective at mitigating the risk of bed erosion during extreme floods. From a practical perspective, an HS or TS needs less sediment to complete than an FS channel, providing a more economic strategy for restoring channels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".