Estimating cohesive sediment erosion and deposition rates in wide rivers
Bibliographic record
Abstract
Sediment erosion and deposition rates are two of the most important factors that influence fluvial geomorphology. Several experimental devices have been constructed to estimate cohesive sediment erosion rate. However, estimated erosion rates may not be reliable for large rivers due to limited soil sampling and a high dependency of cohesive sediment behaviour on several physical, mechanical, and electrochemical properties of the sediment and eroding fluid. A new methodology has been developed to estimate the erosion and deposition rate of wide rivers using in situ measurements. To test this methodology, an acoustic Doppler current profiler was used to collect bathymetry and velocity profiles over a study area along the Red River in Winnipeg, Canada. Sediment concentration profiles along an 8.5 km reach of the river were measured several times under different flow conditions. Finally, an advection–dispersion equation was numerically solved using measured and calculated streamwise dispersion coefficients, flow and channel characteristics to calculate net erosion and deposition over the study area. Moreover, an exponential relationship was obtained between the river discharge and longitudinal dispersion coefficient for the Red River.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".