On the transition between 2D and 3D dunes
Bibliographic record
Abstract
Abstract Sediment transport in sand‐bedded alluvial channels is strongly conditioned by bedforms, the planimetric morphology of which can be either two‐ or three‐dimensional. Experiments were undertaken to examine the processes that transform the bed configuration from two‐dimensional (2D) dunes to three‐dimensional (3D) dunes. A narrowly graded, 500 μm size sand was subjected to a 0·15 m deep, non‐varying mean flow ranging from 0·30 to 0·55 m sec−1 in a 1 m wide flume. Changes in the planimetric configuration of the bed were monitored using a high‐resolution video camera that produced a series of 10 sec time‐lapsed digital images. Image analysis was used to define a critical value of the non‐dimensional span (sinuosity) of the bedform crestlines that divides 2D forms from 3D forms. Significant variation in the non‐dimensional span is observed that cannot be linked to properties of the flow or bedforms and thus appears random. Images also reveal that, once 2D bedforms are established, minor, transient excesses or deficiencies of sand are passed from one bedform to another. The bedform field appears capable of absorbing a small number of such defects but, as the number grows with time, the resulting morphological perturbations produce a transition in bed state to 3D forms that continue to evolve, but are pattern‐stable. The 3D pattern is maintained by the constant rearrangement of crestlines through lobe extension and starving downstream bedforms of sediment, which leads to bifurcation. The experiments demonstrate that 2D bedforms are not stable in this calibre sand and call into question the reliability of bedform phase diagrams that use crestline shape as a discriminator.
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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 | 0.001 | 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".