High resolution sensors in space and time for determination saltation and creep intensity
Bibliographic record
Abstract
ABSTRACT The measurement of aeolian sand transport rates at high temporal and spatial resolution is crucial for further progress in testing and developing numerical models of sand movement by wind and in the modelling of sand dunes, ripples and so on. This paper reports the development and field testing of two sand transport sensors. The first one, a webcam commonly used with personal computers, is a new device in aeolian research. The webcam frame transfer is triggered by a sonic anemometer every 0·1 second. Consecutive frames are compared and analysed in real‐time by a computer program. Changes in pixel light intensity exceeding a threshold level are recorded and interpreted as grain movements. The second sensor is a small‐sized ‘Saltiphone’‐type device made of simple loudspeakers with a diameter of 15 mm as used in MP3 players. It can be deployed as a 2 × 3 array of six such devices distributed to enable horizontal and vertical spatial sampling of the sand flux. The devices are tested under field conditions. Both signals and the sum of microphone impacts over 15 minutes are compared to data gathered using a Guelph sand trap, and very good agreement is found. Measurements in a wind tunnel using sieved natural sand indicate that the webcam can be used to infer additional information about the grain size. As an application, the fluid and impact thresholds for aeolian sand transport are investigated in field measurements by analysing the onset and breakdown of saltation in gust and lull intervals of rising and falling wind speeds, respectively. In this way, constitutive equations for sand transport in terms of the wind speed can be tested. If viable, they can be employed to infer estimates for the thresholds by minimizing the root‐mean‐square error between measured and calculated transport data. Copyright © 2012 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".