Bibliographic record
Abstract
Using a laser Doppler velocimeter (LDV) system, experiments were carried out to obtain detailed information on the flow over a long train of fixed two-dimensional rough-wall dunes. The experiments provide quantitative data on conventionally averaged velocity and turbulence parameters, as well as insights on the flow structures that play a crucial role in sediment transport. The results indicate that the shape of the dunes has a major influence on the flow features, whereas the effect of near-bed roughness is limited to the wall region extending to a distance of about 80% of the dune height about the crest. However, the length of the separation eddy seems to be influenced by the near-bed roughness. As in flow over a smooth dune, the turbulence data reveal large peaks along a line roughly coinciding with the shear layer emanating from the separation at the crest. However, the location of the peak turbulence is farther extended into the flow away from the bed with increasing bed roughness. Moreover, with increasing roughness the magnitude of the peak is also reduced. A secondary peak in the streamwise turbulence profiles is noticed at some distance from the bed. This feature is not influenced by the near-bed roughness and indicates sustenance of turbulence generated by the previous dune and relates to the convection of the upstream flow history. Based on the results obtained at several stations between two successive dune crests, one can conclude that the quantitative effect of roughness itself is limited to the near-bed region in the measurements at all levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".