Marine Coastline Polygonal Ridges and Surface Roughness Development on a Salt-Crusted Playa: Recognition by Structure-from-Motion Photogrammetry
Bibliographic record
Abstract
Salt crust is a normal landform in drying-out salt lake basins or marine regression coastlines, but the surface evolution processes over a decadal or even centenary period are not well understood due to poor data records. Microrelief characteristics control erodibility and erosivity, which will significantly influence wind erosion and dust emission. It is essential to classify the microrelief pattern of salt crust for mapping its spatial distribution and evaluating the environmental process. A desiccated inland tail-end lake would be an example of the coastline surface evolution after regression and represent a good case study of salt crust because of the fewer exogenic process interruptions. For this paper, field work was performed in the Lop Nur playa in China, about 90° E, 40° N, which used to be a salt lake half a century ago. Ground-based photos of the salt crust were acquired and imported into structure-from-motion (SfM) software to produce a fine centimeter-scale digital elevation model (DEM). Two indexes were introduced and extracted from the digital elevation model to classify various types of salt crust: roughness was calculated to evaluate the magnitude and the gray-level co-occurrence matrix (GLCM) score was derived to describe the structure pattern of the salt crust. Moreover, in this paper, sedimentary features during different parts of a playa evaporation cycle are reviewed and peculiar kinds of salt crust found on Lop Nur are further discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".