Short communication: Multiscalar drag decomposition in fluvialsystems using a transform-roughness correlation (TRC) approach
Bibliographic record
Abstract
Abstract. In natural open-channel flows over complex surfaces, a wide range of superimposed roughness elements may contribute to flow resistance. Gravel-bed rivers present a particularly interesting example of this kind of multiscalar flow resistance problem, as both individual grains and bedforms can potentially be important roughness elements. In this paper, we propose a novel method of estimating the relative contribution of different physical scales of river bed topography to the total drag, using a transform-roughness correlation (TRC) approach. The technique, which requires only a single longitudinal profile, consists of (1) a wavelet transform which decomposes the surface into roughness elements occurring at different wavelengths, and (2) a `roughness correlation' that estimates the drag associated with each wavelength based on its geometry alone, expressed as ks. We apply the TRC approach to original and published laboratory experiments and show that the multiscalar drag decomposition yields estimates of grain- and form-drag that are consistent with estimates in channels with similar morphologies. Also, we demonstrate that the roughness correlation may be used to estimate total flow resistance via a conventional equation, suggesting that it could replace representative roughness values such as median grain size or the standard deviation of elevations. An improved understanding of how various scales contribute to total flow resistance may lead to advances in hydraulics as well as channel morphodynamics.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".