17th Canadian Hydrotechnical Conference
Bibliographic record
Abstract
ABSTRACT: We report the implementation and testing of a bedload sediment transport module that has been added to the 2D depth-averaged hydrodynamic model River2D. The purpose of this new River2D Morphology model is to simulate the morphodynamic changes of bed elevation in alluvial rivers. The model has been initially tested using three experimental cases of bed elevation changes in straight alluvial channels: (1) Bed aggradation due to sediment overloading; (2) bed degradation due to sediment feed shut-off (similar to degradation below a dam); and (3) knickpoint migration. The knickpoint migration experiment involved a short reach of 10 % bed slope that caused supercritical flow and a hydraulic jump. The computed longitudinal profiles of bed and water surface elevations compare well with their measured counterparts, especially for the first two case studies. The model remained stable, even in the case of transcritical flow, challenging previous results of several researchers that have suggested that a decoupled model like this one (i.e. sediment equations are solved after flow equations) should become unstable when the Froude number approaches one or the boundary conditions change quickly. The model has proved to be reliable and stable for modeling straight alluvial channels. Applications to curved alluvial channels are shown in a companion paper (Part II).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.211 | 0.069 |
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".