Micro‐topographic analysis of shell pavements formed by aeolian transport in a wind tunnel simulation
Bibliographic record
Abstract
Beach surfaces containing shell materials represent one end‐member of a range of environments in which armoring is the primary control on wind erosion. Unlike spheres and cylinders which have formed the basis of theoretical model formulation and much of the early work in wind tunnels, mollusc shells have complex and non‐uniform shapes which vary with their orientation. Identification of shell perimeter, height and frontal area relative to the bed area (roughness density) is therefore a formidable task, but nonetheless is essential for modeling sediment entrainment from beach surfaces. A methodology is suggested in this paper for capturing and analyzing these geospatial data, in the context of a wind tunnel simulation designed to improve understanding of the geophysical processes involved in armoring. For deposits where non‐erodible shells represent half of the volume of the parent material, the surface appears to be highly stable to wind erosion from the outset, although minor reworking of the intervening, erodible sediment does occur. In comparison, the shell coverage must increase to approximately 30% during wind erosion events in order for any given beach surface to stabilize, especially beach deposits with a low concentration of shells by volume. With suitable calibration, the Raupach shear stress partitioning model can be forced to perform well in predicting the threshold conditions for particle entrainment. However, this approach overlooks the pivotal involvement of particle impact and ricochet in the creation and sculpting of the armored bed. As a case in point, when the shells are removed from digital elevation models of armored beach surfaces formed in aeolian systems, the adjusted topography is not suggestive of the presence of coherent flow structures (e.g., horseshoe vortices and wedge shaped shelter areas) as assumed to exist in the stress partitioning approach for isolated flows. This would suggest that future work on the armoring of natural surfaces affected by wind erosion must allow for more complexity in the flow perturbation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".