Comparison of PI‐SWERL with dust emission measurements from a straight‐line field wind tunnel
Bibliographic record
Abstract
The Portable In situ Wind ERosion Lab (PI‐SWERL) was developed to measure dust emissions from soil surfaces. This small, portable unit can test the emissivity of soils in areas that are difficult to access with a field wind tunnel, and can complete a larger number of tests in less time. The PI‐SWERL consists of a cylindrical enclosure containing an annular flat blade that rotates at different speeds, which generates shear stress upon the surface. The shear stress generated by PI‐SWERL results in the entrainment of particles including dust. PI‐SWERL was developed to provide an index of dust emission potential comparable to the field wind tunnel. The PI‐SWERL dust emission results were compared against those obtained from a ∼12 m long, 1 m wide, 0.75 m high straight line suction‐type portable field wind tunnel by conducting collocated tests at 32 distinct field settings and soil conditions in the Mojave Desert of southern California. Clay‐ to sand‐rich soils that displayed a range of crusting, gravel cover, and disturbance were tested. The correspondence between dust emissions (mg m−2s−1) for the two instruments is nearly 1:1 on most surfaces. Deviation between the two instruments was noted for densely packed gravel surfaces. For rough surfaces a correction can be applied to the PI‐SWERL that results in comparable dust emission data to the wind tunnel. PI‐SWERL can be used to complement research efforts in aeolian geomorphology aimed to quantify spatial and temporal patterns of dust emissions as well as air quality research related to dust emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".