Field characterization of three‐dimensional lee‐side airflow patterns under offshore winds at a beach‐dune system
Bibliographic record
Abstract
Characterization of three‐dimensional (3D) airflow remains elusive within a variety of environments and is particularly challenging over complex dune topography. Previous work examining airflow over and in the lee of dunes has been restricted to two‐dimensional studies and has concentrated on dune shapes containing angle of repose lee sides only. However, the presence of vegetation in coastal dunes creates topographic differences and irregular shapes that interfere with flow separation at the crest and significantly modify lee‐side airflow patterns and potential transport. This paper presents the first 3D field characterization of airflow patterns at the lee side of a subaerial dune. Flow information was obtained using an array of 3D ultrasonic anemometers deployed over a beach surface during seven offshore wind events. Data were used to measure cross‐shore and alongshore lee‐side airflow patterns using the three dimensions of the wind vector. Distances to re‐attachment were similar to previous studies, but the range of transverse incident wind directions resulting in flow separation (0+/−35°) was almost twice that previously reported (0+/−20°). Airflow reversal took place with winds as slow as 1 m s−1. Transverse offshore winds generated areas of opposing wind directions both within the reversed zone and beyond re‐attachment, contrary to consistent deflection in only one direction found in transverse desert dunes. Patterns of flow convergence‐divergence have been reported in fluvial studies. However, while convergence was associated with weak reversal in fluvial settings, it appeared to be related to strong flow reversal here and could be produced by pressure differentials at the dune crest.
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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 | 0.000 |
| 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".