Effect of Skewness Orientation on Morphological Adjustments in Alluvial Meandering Streams
Bibliographic record
Abstract
Predicting morphological adjustments in alluvial meandering streams remains a challenging task due to the complex nature of the governing inter-related dynamic flow and sediment transport processes. This difficulty is increased in streams with irregular single-channel planform geometries, such as skewed meanders, where the meander apex is shifted in either the up-valley or down-valley direction relative to the meander centroid. Research in confined bank flume experiments has shown that the geometry difference affects flow characteristics and streambed development. The present study extends upon these findings by being the first to investigate the effects of skewness orientation in a wide-channel flume with a fully unconfined bed and banks. Three experiments were completed with an up-valley skewed, a down-valley skewed, and a non-skewed symmetrical channel, using well-sorted coarse sand and no sediment feed. The results had some variabilities in erosion and magnitude of morphological developments due to initial experimental conditions, but our analysis of the bedform positioning showed notable similarities and differences between the geometries. Bedforms typically formed upstream of the apex, with differences in their stream-wise direction extents. This research highlights how channel width-to-depth ratio and bank erodibility significantly impact river evolution, offering new insights into the dynamics of skewed meandering river channels. This study is a novel step towards a better understanding of skewed meandering rivers in unconfined alluvial channels and highlights opportunities for further research.
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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 | 0.001 | 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".