Sediment dynamics of watershed urbanization and river restoration: Insights from 10 years of research in small gravel‐bed rivers
Bibliographic record
Abstract
Abstract Watershed urbanization frequently leads to river channel enlargement and incision. Mitigation strategies such as in‐channel restoration and stormwater management have been developed to counteract these problems, yet alternatives are rarely considered with respect to the sediment dynamics that underlie the process of river degradation. In the current paper, we revisit two heavily urbanized small gravel‐bed rivers where aggressive stormwater management and river restoration projects were completed. The goal is to consider how information from 10 years of research on sediment dynamics might change the adopted mitigation strategies. By synthesizing previous work and novel analyses, we provide a diagnosis of the effects of urbanization and existing restoration efforts on the channel morphology and sediment dynamics and develop a sediment augmentation strategy to restore the dynamic equilibrium of the study reach. Additionally, we model the cumulative impact of various stormwater management strategies on sediment dispersal. We place these river management strategies within a framework that seeks to address the root cause of urban river degradation by rebalancing the sediment supply and capacity of these 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".