Aeolian particle flux profiles and transport unsteadiness
Bibliographic record
Abstract
Abstract Vertical profiles of aeolian sediment flux are commonly modeled as an exponential decay of particle (mass) transport with height above the surface. Data from field and wind‐tunnel studies provide empirical support for this parameterization, although a large degree of variation in the precise shape of the vertical flux profile has been reported. This paper explores the potential influence of wind unsteadiness and time‐varying intensity of transport on the geometry (slope, curvature) of aeolian particle flux profiles. Field evidence from a complex foredune environment demonstrates that (i) the time series of wind and sediment particle flux are often extremely variable with periods of intense transport (referred to herein as sediment “flurries”) separated by periods of weak or no transport; (ii) sediment flurries contribute the majority of transport in a minority of the time; (iii) the structure of a flurry includes a “ramp‐up” phase lasting a few seconds, a “core” phase lasting a few seconds to many tens of seconds, and a “ramp‐down” phase lasting a few seconds during which the system relaxes to a background, low‐intensity transport state; and (iv) conditional averaging of flux profiles for flurry and nonflurry periods reveals differences between the geometry of the mean profiles and hence the transport states that produce them. These results caution against the indiscriminate reliance on regression statistics derived from time‐averaged sediment flux profiles, especially those with significant flurry and nonflurry periods, when calibrating or assessing the validity of steady state models of aeolian saltation.
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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) | 0.001 | 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".