Quantification of Particle Velocities and Energy Regime in an Aeolian Abrasion Chamber
Bibliographic record
Abstract
Abstract Particle breakdown and fine sediment production by wind abrasion is of long‐standing interest in aeolian science as it contributes to erosion and dust production on Earth and other planetary bodies. The process of aeolian abrasion is largely measured in laboratories to enable standardization of parameters and allow simulation of saltation over long time periods. To be effective, abrasion simulators must reproduce particle interactions similar to those observed in the natural environment. This paper quantifies the particle velocities, pathways and energy regime within a widely used “test‐tube” abrasion chamber. For 17 different sand samples, the instantaneous two‐dimensional vertical and horizontal velocity components of particles moving within the chamber were sampled using a laser Doppler anemometer. Similar to a natural saltation cloud, the movement of particles in the chamber is stochastic and there is a positive relationship between the air inflow rate and the depth of the saltation layer. For air inflow of 14.9 m s−1, particle velocities range from 0.01 to 3.2 m s−1 with median velocity for all particles in the chamber varying from 0.29 to 0.56 m s−1, and total energy ranging from 0.54 to 1.38 J kg−1. These values are similar to those determined for natural saltation clouds. For a constant air inflow rate, the mean total particle velocity increases with particle size. Air inflow rate has a significant effect on mean total particle velocity but between 10 and 100 g the quantity of sample tested is not important. The contribution of this type of experiment to understanding aeolian abrasion processes is evaluated.
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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".