Sensitivity of interfacial hydraulics to the microtopographic roughness of water‐lain gravels
Bibliographic record
Abstract
ABSTRACT Flow within the interfacial layer of gravel‐bed rivers is poorly understood, but this zone is important because the hydraulics here transport sediment, generate flow structures and interact with benthic organisms. We hypothesized that different gravel‐bed microtopographies generate measurable differences in hydraulic characteristics within the interfacial layer. This was tested using a high density of spatially and vertically distributed, velocity time series measured in the interfacial layers above three surfaces of contrasting microtopography. These surfaces had natural water‐worked textures, captured in the field using a casting procedure. Analysis was repeated for three discharges, with Reynolds numbers between 165000 and 287000, to evaluate whether discharge affected the impact of microtopography on interfacial flows. Relative submergence varied over a small range (3.5 to 8.1) characteristic of upland gravel‐bed rivers. Between‐surface differences in the median and variance of several time‐averaged and turbulent flow parameters were tested using non‐parametric statistics. Across all discharges, microtopographic differences did not affect spatially averaged (median) values of streamwise velocity, but were associated with significant differences in its spatial variance, and did affect spatially averaged (median) turbulent kinetic energy. Sweep and ejection events dominated the interfacial region above all surfaces at all flows, but there was a microtopographic effect, with Q2 and Q4 events less dominant and structures less persistent above the surface with the widest relief distribution, especially at the highest Reynolds number flow. Results are broadly consistent with earlier work, although this analysis is unique because of the focus on interfacial hydraulics, spatially averaged ‘patch scale’ metrics and a statistical approach to data analysis. An important implication is that observable differences in microtopography do not necessarily produce differences in interfacial hydraulics. An important observation is that appropriate roughness parameterizations for gravel‐bed rivers remain elusive, partly because the relative contributions to flow resistance of different aspects of bed microtopography are poorly constrained. © 2014 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".