Morphodynamics of the Shi-ting River after Wenchuan Earthquake: Effects of in-channel weirs and fine sediment supply on river incision
Bibliographic record
Abstract
Since the 2008 Wenchuan (Ms. 8.0) earthquake, the Shi‑ting River in Sichuan Province, China has suffered massive bed incision, with the largest incision depth being more than 20 m in 7 years. Potential reasons include: breaks in sediment connectivity due to widespread in-channel weirs; the supply of fine sediment after the earthquake; intensive sand mining, etc. In this study, we simulate the combined role of in-channel weirs and fine sediment supply in determining the massive bed incision in the Shi-ting River. A one-dimensional river morphodynamic model is implemented. The simulated results show that the in-channel weirs can lead to bed incision and bed coarsening in the downstream channel. For a weir with a height of 5 m, the maximum incision depth is about 5 m, and the extent of downstream incision is no more than 20 km within 20 years. The supply of fine sediment can enhance the downstream channel incision, as the weir preferentially traps coarse sediment but passes the fine sediment downstream. However, a combination of in-channel weirs and the fine sediment supply cannot explain the dramatic incision (20 m in 7 years) as observed in the Shi-ting River. This suggests that the mining of gravel and sand had a significant role in driving channel degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".