Spatial heterogeneity of near‐bed hydraulics above a patch of river gravel
Bibliographic record
Abstract
The spatial heterogeneity of fully turbulent, near‐bed flows above gravel river beds is examined using a realistic replica of a natural gravel patch in a large flume. Three‐dimensional velocity time series were obtained at four heights (0.008–0.1 m) above the local bed in each of 99 closely spaced verticals for three flows of increasing intensity. The spatial heterogeneity of time‐averaged velocities and root‐mean‐square velocity fluctuations increases under stronger flows and closer to the bed. However, streamwise velocity becomes spatially homogeneous at a distance from the bed of between 2–4 times the median bed elevation. Heterogeneity in the direction of the velocity vector is independent of mean flow velocity, but in all cases it decreases approximately linearly with distance above the surface. Skewness of the instantaneous velocity distributions suggests that slowly moving fluid emanating from the near‐bed region impinges upon higher levels with greater frequency and greater spatial coverage as the average flow velocity increases. Spatial heterogeneity in turbulent kinetic energy increases with flow velocity and maxima occurr at positions that intercept layers of intense vortex shedding in the lee of obstacle crests. The spatial organization of the flow properties is nonrandom and consistent across the three flows. Simple regression models are developed to provide a basis for investigating the heterogeneity of near‐bed flow at the patch scale (∼2 m 2 ) in gravel bed rivers.
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 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) | 0.000 | 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 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".