Using numerical models to estimate the impact of run-of-river dams on bed load transport processes and channel bed evolution in British Columbia
Bibliographic record
Abstract
Using a 1-D morphodynamic model as our primary tool, we are investigating the potential impact of run-of-river dams on sediment transport processes and channel bed evolution. The model is used to predict the grain size distribution of sediment exposed at the surface and in transport as well as the bulk transport rate. The model can predict these quantities as a function of space and time in response to dam closure. Initially the model will be applied to the reach downstream of the powerhouse where the channel hydrology is largely unaltered.Given the geomorphic variability of run-of-river sites in British Columbia, and the variability in operating procedures among the sites, we will model several different sediment supply reduction scenarios (e.g. temporary reduction, sediment pulse scenarios, total elimination). In order to ground the model in reality, we will use field data collected from proposed project sites in British Columbia as inputs to the numerical model. Our goal is to provide general predictions that can be used to estimate long-term cumulative impacts of run-of-river projects on channel morphology and channel bed characteristics. Ultimately, our model predictions will be incorporated into a decision support tool designed to help minimize trade-offs between the economics of project development and the sustainability of aquatic ecosystems in British Columbia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".