Modeling grain size adjustments in the downstream reach following run‐of‐river development
Bibliographic record
Abstract
Abstract Disruptions to sediment supply continuity caused by run‐of‐river (RoR) hydropower development have the potential to cause downstream changes in surface sediment grain size which can influence the productivity of salmon habitat. The most common approach to understanding the impacts of RoR hydropower is to study channel changes in the years following project development, but by then, any impacts are manifest and difficult to reverse. Here we use a more proactive approach, focused on predicting impacts in the project planning stage. We use a one‐dimensional morphodynamic model to test the hypothesis that the greatest risk of geomorphic change and impact to salmon habitat from a temporary sediment supply disruption exists where predevelopment sediment supply is high and project design creates substantial sediment storage volume. We focus on the potential impacts in the reach downstream of a powerhouse for a range of development scenarios that are typical of projects developed in the Pacific Northwest and British Columbia. Results indicate that increases in the median bed surface size (D50) are minor if development occurs on low sediment supply streams (<1 mm for supply rates 1 × 10−5 m2 s−1 or lower), and substantial for development on high sediment supply streams (8–30 mm for supply rates between 5.5 × 10−4 and 1 × 10−3 m2 s−1). However, high sediment supply streams recover rapidly to the predevelopment surface D50 (∼1 year) if sediment supply can be reestablished.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".