Bibliographic record
Abstract
Abstract Dune response to variable flow has been well documented, but there is no universal model to predict dune dimensions as they respond to imposed flows. Here we use a series of flume experiments to explore dune growth in response to constant flow to better understand the form of dune growth curves. Observations of dune growth from a flat sand bed were made at three flow depths, under five different constant transport stages in a laboratory flume. The transport stages ranged from the sediment entrainment threshold to suspension conditions. The bed was flattened before each run and topography was continually mapped, providing observations of dune growth and morphology at each distinct transport stage condition. The results show that dune growth curves exhibited three different behaviors: (1) exponential growth; (2) punctuated growth, when a period of initially linear growth was abruptly interrupted by exponential growth; and (3) instantaneous growth, when bed evolution happened so quickly that we were unable to take measurements of the phenomenon. Growth behavior is dependent on the applied transport stage, and the time for a growing dune field to reach equilibrium decreases nonlinearly with transport stage. Observations of evolving dunes and the time required to achieve an equilibrium bed state are used to propose a series of relations that can predict dune dimensions through time.
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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) | 0.000 | 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) | 0.001 | 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".