Topographic change and numerically modelled near surface wind flow in a bowl blowout
Bibliographic record
Abstract
Abstract A number of studies have measured and numerically modelled near surface wind velocity over a range of aeolian landforms and made suppositions about topographic change and landform evolution. However, the precise measurement and correlation of flow dynamics and resulting topographic change have not yet been fully realized. Here, using repeated high‐resolution terrestrial laser scanning and numerical flow modelling within a bowl blowout, we statistically analyse the relationship between wind speed, vertical wind velocity, turbulent kinetic energy and topographic change over a 33‐day period. Topographic results showed that erosion and deposition occurred in distinct regions within the blowout. Deposition occurred in the upwind third of the deflation basin, where wind flow became separated and velocity and turbulent kinetic energy decreased, and erosion occurred in the downwind third of the deflation basin, where wind flow reattached and aligned with incident wind direction. Statistical analysis of wind flow and topographic change indicated that wind speed had a strong correlation with overall topographic change and that vertical wind velocity (including both positive and negative) displayed a strong correlation with negative topographic change (erosion). Only weak or very weak correlations exist for wind flow parameters and positive topographic change (accretion). This study demonstrates that wind flow modelling using average incident wind conditions can be utilized successfully to identify regions of overall change and erosion for a complex aeolian landform, but not to identify and predict regions where solely accretion will occur. © 2019 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".