Numerical modeling of gravel bed river response to meander straightening: The coupling between the evolution of bed pavement and long profile
Bibliographic record
Abstract
Artificial meander straightening (rectification) was conducted in the early 1960s along the Sainte‐Marguerite River, Canada, in order to facilitate highway construction along the valley. Previous studies [ Talbot and Lapointe, 2002 ] confirm that vertical reprofiling, coupled with pavement coarsening in the degrading reach, were the main responses counteracting the disequilibrium in gravel transport rates triggered at rectification of this system. Numerical simulations, using SEDROUT2.0, a one‐dimensional hydraulic and sediment transport model, and validated against the observed channel response, show the important role played by an advancing wave of pavement coarsening down the rectified reach in modulating the bed degradation response. Simulations extending into the future reveal an asymptotically slowing approach to equilibrium in the middle of the 21st century, with a response half‐time of the order of 10 years. In near‐threshold gravel bed systems like the Sainte‐Marguerite River, pavement coarsening after rectification buffers the system against extreme degradation. Most significantly for watershed management, this also appears to severely limit the extent of propagation of degradation upstream of the rectification.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".