Rough Correlations: Meta‐Analysis of Roughness Measures in Gravel Bed Rivers
Bibliographic record
Abstract
Abstract Bed roughness height (k) is a key parameter for velocity prediction in open‐channel flows. There is not yet a firm consensus about whether characteristic particle size D84 or (standard deviation of the channel thalweg) better describes k in gravel bed streams. A data set of 1,788 flume and 713 field measurements with a wide range of channel morphologies and flow conditions were compiled to test whether D84 or is a better descriptor of k and to explore the influence of several controls on flow resistance variation. Tests were performed using four well‐known flow resistance equations. The results consistently show that outperforms D84 in predicting velocity and the Smart and Jäggi equation, with as k, outperforms other equations. The data set was grouped based on R/k (R is the hydraulic radius), channel morphologies, and study sites. performs better than D84 as a measure of k in all morphologies and much better for channels with large instream wood. The analysis shows R/k is a major control on resistance variation as contains more site‐specific information like bed structure. The topography measurements for step‐pool channels should at least contain measurements on key roughness elements like steps. For gravel‐dune or plane‐bed channels, the proper resolution should be higher than 1/2 dune wavelength and 2D84, respectively. The choice of proper reach length relates to both R/k and roughness type. Further, hydraulic geometry functions with either D84 or as k are proposed, and the relation between the two metrics is discussed.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".