The factors influencing the abrasion efficiency of saltating grains on a clay‐crusted playa
Bibliographic record
Abstract
Abstract The entrainment and subsequent transport of PM10 (particulate matter <10 µm) has become an important and challenging focus of research for both scientific and practical applications. Arid and semi‐arid environments are important sources for the atmospheric loading of PM10, although the emission of this material is often limited by surface crusts. It has been suggested that the primary mechanisms through which PM10 is released from a crusted surface are abrasion by saltating grains or disturbance by agricultural and recreational activities. To examine the importance of saltation abrasion in the emission of PM10, a series of field wind tunnel tests were conducted on a clay‐crusted surface near Desert Wells, Arizona. In a previous part of this study it was found that the emission rate varies linearly with the saltation transport rate, although there can be considerable variation in this relationship. This paper more closely examines the source of the variability in the abrasion efficiency, the amount of PM10 emitted by a given quantity of saltating grains. The abrasion efficiency was found to vary with the susceptibility of the surface to abrasion, the ability of the sand to abrade that surface and the availability of material with a caliper size <10 µm within the crust. Specifically, the results of the study show that the abrasion efficiency is related to the crust strength, the amount of surface disturbance and the velocity of the saltating grains. It is concluded that the spatial and temporal variability of these controls on the abrasion efficiency imposes severe contextual limitations on experimentally derived models, and can make theoretical models too complex and impractical to be of use. Copyright© 2001 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".