An empirical model of subcritical bedform migration
Bibliographic record
Abstract
Abstract The ability to predict bedform migration in rivers is critical for estimating bed material load, yet there is no relation for predicting bedform migration (downstream translation) that covers the full range of conditions under which subcritical bedforms develop. Here, the relation between bedform migration rates and transport stage is explored using a field and several flume data sets. Transport stage is defined as the non‐dimensional Shields stress divided by its value at the threshold for sediment entrainment. Statistically significant positive correlations between both ripple and dune migration rates and transport stage are found. Stratification of the data by the flow depth to grain‐size ratio improved the amount of variability in migration rates that was explained by transport stage toca70%. As transport stage increases for a given depth to grain‐size ratio, migration rates increase. For a given transport stage, the migration rate increases as the flow depth to grain‐size ratio gets smaller. In coarser sediment, bedforms move faster than in finer sediment at the same transport stage. Normalization of dune migration rates by the settling velocity of bed sediment partially collapses the data. Given the large amount of variability that arises from combining data sets from different sources, using different equipment, the partial collapse is remarkable and warrants further testing in the laboratory and field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".