Evaluating the Effect of Morphologic Units on Fractional Sediment Mobility and Bedload Transport in a Small Pool‐Riffle Reach
Bibliographic record
Abstract
Abstract This study examines the spatial pattern of fractional sediment mobility and assesses the influence of morphologic units on bedload transport in a small pool‐riffle reach with limited supply. Using a 2D hydraulic model and a subsurface‐based sediment transport model, shear stresses, fractional sediment mobility, and bedload transport were examined for flow events ranging in magnitude between 0.2Qbf and 1.5Qbf. Results reveal that while spatial variations in shear stress decrease as discharge increases, only a small proportion of the bed experiences high transport rates. At the reach scale, riffles are the primary morphological unit contributing to fully mobile sediment for all size fractions. However, at a subunit scale, there is evidence of sediment transport reversal for grains >32 mm at flows near or exceeding bankfull discharge in association with shear stress reversal. These transport reversals are important for maintaining pools despite their infrequent occurrence in the study reach. Sediment transport maps indicate that bed morphology considerably influences sediment transport at low to moderate flows. During these events, the shear stress is sensitive to local bed topography and partial mobility is the dominant transport process. In contrast, variations in bedload transport rates decrease during high flows when the flow is less sensitive to variations in bed topography and the bed becomes fully mobile.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".