Characterization and Prediction of Fluvial Bank Retreat Using Novel Physical Experiments
Bibliographic record
Abstract
Understanding sediment transport and bank erosion in rivers has been a key area of active research for many decades. This research project uses a unique new Outdoor Experimental River Facility (OERF) located at the Université de Sherbrooke to run novel experiments to further our fundamental understanding of fluvial bank retreat processes. The experimental river is unique in that its floodplain is considerably larger (50 m by 20 m) than standard laboratory facilities and allows for the channel to freely migrate laterally instead of being constrained by laboratory walls. Three stages of progressively larger upstream disturbances consisting of sandbag perturbations were added to a straight gravel-bed channel to determine if any morphological changes occurred. Using an Acoustic Doppler Velocimeter (ADV), water velocity was measured to quantify the flow field and calculate bank and bed shear stress for four experimental phases: 1) no disturbance, 2) small, 3) medium and 4) large sandbag perturbations. Drone photogrammetry was used to generate digital elevation models (DEMs) to visualize erosion and deposition. Bank shear stress measurements were variable, and more research is needed to determine the optimal sampling techniques. Overall, no meander formation occurred during the experiment, which can be attributed to variables such as river armouring, lack of sediment input, and the time frame of the experiment. This study helps provide an increased understanding of bank erosion which will be useful for future bank stabilization projects related to meandering rivers in Quebec, and globally around the world.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".