Coupling between downstream variations of channel width and local pool–riffle bed topography
Bibliographic record
Abstract
Abstract. A potential control of downstream channel width variations on the structure and planform of pool–riffle sequence local bed topography is a key to the dynamics of gravel bed rivers. How established pool–riffle sequences respond to time-varying changes in channel width at specific locations, however, is largely unexplored and challenging to address with field-based study. Here, we report results of a flume experiment aimed at building understanding of how statistically steady pool–riffle sequence profiles adjust to spatially prescribed channel width changes. We find that local bed slopes near steady-state conditions inversely correlate with local downstream width gradients when the upstream sediment supply approximates the estimated transport capacity. This result constrains conditions prior to and following the imposed local width changes. Furthermore, this relationship between local channel bed slope and downstream width gradient is consistent with expectations from scaling theory and a broad set of field-based, numerical, and experimental studies (n=88). However, upstream disruptions to coarse sediment supply through actions such as dam removal can result in a transient flipping of the expected inverse correlation between bed slope and width gradient, collectively highlighting that understanding local conditions is critical before typically implemented spatial averaging schemes can be reliably applied.
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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 | 0.000 | 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".