Process‐based assessment of success and failure in a constructed riffle‐pool river restoration project
Bibliographic record
Abstract
Abstract Although there is increasing consensus that river restoration should focus on restoring processes rather than form, proven techniques to design and monitor projects for sediment transport processes are lacking. This study monitors bedload transport and channel morphology in a rural, an urban unrestored, and an urban restored reach. Objectives are to compare bedload transport regimes, assess the stability and self‐maintenance of constructed riffle‐pool sequences, and evaluate the impact of the project on coarse sediment continuity in the creek. Sediment tracking is done using radio frequency identification tracers and morphologic change is assessed from repeated cross‐section surveys. Mean annual velocity is used to quantify the average downstream velocity of tracers, defined as the mean overall tracer travel length divided by the total study duration. The channel reconstruction slows down the downstream velocity of particles in the D75 and D90 size classes, but does not significantly change the velocity of particles in the D50 size class or smaller. Surveys show that riffle features remain stable and that pool depths are maintained or deepened, while tracer paths match with what has been observed in natural riffle‐pools. However, the slowdown of coarse sediment and increase in channel slope may lead to future failures related to over‐steepening of the banks and a disruption in the continuity of sediment transport in the creek. This study demonstrates how bedload tracking and morphological surveys can be used to assess river restoration projects, and highlights the importance of incorporating coarse sediment connectivity into restoration design and monitoring.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".