Climate–surface–pore‐water interactions on a salt crusted playa: implications for crust pattern and surface roughness development measured using terrestrial laser scanning
Bibliographic record
Abstract
Abstract Sodium accumulating playas (also termed sodic or natric playas) are typically covered by polygonal crusts with different pattern characteristics, but little is known about the short‐term (hours) dynamics of these patterns or how pore water may respond to or drive changing salt crust patterning and surface roughness. It is important to understand these interactions because playa‐crust surface pore‐water and roughness both influence wind erosion and dust emission through controlling erodibility and erosivity. Here we present the first high resolution (10 −3 m; hours) co‐located measurements of changing moisture and salt crust topography using terrestrial laser scanning (TLS) and infra‐red imagery for Sua Pan, Botswana. Maximum nocturnal moisture pattern change was found on the crests of ridged surfaces during periods of low temperature and high relative humidity. These peaks experienced non‐elastic expansion overnight, of up to 30 mm and up to an average of 1.5 mm/night during the 39 day measurement period. Continuous crusts however showed little nocturnal change in moisture or elevation. The dynamic nature of salt crusts and the complex feedback patterns identified emphasize how processes both above and below the surface may govern the response of playa surfaces to microclimate diurnal cycles. © 2015 The Authors. Earth Surface Processes and Landforms published by 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".