Pool‐Riffle Adjustment Due to Changes in Flow and Sediment Supply
Bibliographic record
Abstract
Abstract How do gravel bed pool‐riffle streams adjust to changing upstream water and bedload sediment supplies, and what analysis techniques can help to effectively identify how change occurs? Here, we use a mixture of field and experimental data to examine these problems and apply a suite of traditional and novel analysis approaches to highlight dynamics which might otherwise go undetected. Eleven years of monitoring channel morphology in a small forested watershed indicate that pool‐riffles persist through large changes in upstream water and bedload supply and that bed architecture relief is correlated to flow magnitude. A flume experiment consisting of eight runs was conducted to examine the field case in more detail. The experimental design splits the eight runs into four runs of relatively high water and sediment supply and four of relatively low water supply, with no upstream sediment supply. Experimental results corroborate the field‐based measurements of pool‐riffle persistence, which is due to a coupling between downstream width variations, and spatial patterns of flow velocity and bedload transport. More specifically, measurements made during the flume experiments along a prominent pool‐riffle pair indicate that temporal and spatial changes to topography, flow hydraulics, and bed surface sediment texture are more rich and nuanced than existing generalizations offer. For example, clustering analysis completed using self‐organizing maps indicates that sediment sorting between pools and riffles is not simply a binary type response of finer versus coarser described by some characteristic grain size.
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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 | 0.001 | 0.002 |
| 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".