Degradation of a Foreland River After the Wenchuan Earthquake, China: A Combined Effect of Weirs, Sediment Supply, and Sediment Mining
Bibliographic record
Abstract
Abstract Since the 2008 Wenchuan (Ms. 8.0) Earthquake, the foreland rivers of the Longmen Mountains have suffered from significant bed degradation, among which the Shi‐ting River has experienced the largest local degradation of more than 20 m in 7 years. Potential reasons of the dramatic degradation include: (a) sediment disconnectivity due to in‐channel weirs; (b) the mobilization effect on gravel of an increased sand supply as a result of earthquake‐induced landslides; and (c) sediment extraction due to intensive mining. In this paper, we study the complex interaction among the above‐mentioned factors in the Shi‐ting River, using a one‐dimensional river morphodynamic model. Simulation results show that in‐channel weirs can reduce bedload transport and lead to bed degradation that is proportional to weir height. When coupled with additional sand supply, the weirs preferentially trap gravel and deliver sand, augmenting the downstream mobility of gravel and thus the degradation. For the Shi‐ting River, the simulated bed degradation agrees well with the observation when an annual sediment mining of 16 million tons is implemented in the simulation, along with the effects of in‐channel weirs and sand supply. The contribution of sediment mining is one order of magnitude larger than the coupling effect of weirs and sand supply. Both the simulation and observation show that the largest bed degradation occurs downstream of the Renmin Weir, due to the large spatial interval between the Renmin Weir and the next grade control structure.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".