A wind tunnel investigation of particle segregation, ripple formation and armouring within sand beds of systematically varied texture
Bibliographic record
Abstract
Abstract This paper reports on a wind tunnel investigation of particle segregation, ripple formation and surface armouring within sand beds of systematically varied particle size distribution, from coarsely skewed to bimodal. By design, the system was closed with no external inputs of mass from an external particle feed. Particles too coarse to travel in saltation for the given range in wind speed were dyed red in order to distinguish them in optical images from finer sand particles, which could be entrained into the unidirectional airflow. A 3D laser scanner measured the changing bed topography at regular time intervals during 18 experiments involving varied combinations of wind speed and bed texture. Image classification techniques were used to investigate the coincident self‐organization of the two populations of particles, as distinguished by their colour. As soon as saltation commenced, some of the red particles segregated into thin discontinuous patches. Particle trapping and sheltering on these rough patches was strongly favoured, causing them to grow preferentially. During the earliest stages of formation, bedform growth coincided with: (i) rapid coarsening of the surface texture; and (ii) the merging of proto‐ripple ‘crests’ to generate larger rhythmic bedforms of lower frequency. Consistent with previous work, ripple size was observed to increase under stronger winds when not exceeding the threshold for entrainment of the coarse‐mode or red particles from the crest. With declining rates of mass transport and particle segregation as the bed surface armoured, and the consequent deceleration of ripple propagation through to the end of each experiment, all surfaces eventually attained a steady‐state morphometry. At saturation, the largest ripples developed on beds having the lowest initial concentration of red particles. Copyright © 2016 John Wiley & Sons, Ltd.
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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".