Re-attachment zone characterisation under offshore winds blowing over complex foredune topography
Bibliographic record
Abstract
Studies of the role of secondary airflow effects demonstrate the importance of offshore flows in dune growth and maintenance. Turbulent processes at the lee side of aeolian dunes have previously been only qualitatively described. The recent incorporation of ultrasonic anemometers, capable of measuring the three components of the wind vector, allows quantification of flow patterns in complex areas such as the lee side of dunes. This paper presents measurements taken with an array of ultrasonic anemometers during an offshore wind event at Magilligan Point, Northern Ireland, where flow separation and reversal associated to offshore winds has been previously reported. A simple analysis using the raw u and w components of the wind was conducted to extract quantitative information on the location of turbulent zones along a dune-beach profile. Results indicate sharp differences between the relation of u and w with distance downwind from the dune crest, which in turn can be used to identify turbulent zones. Variations in wind velocity and direction at the dune crest did not result in changes in the location of turbulent zones at the beach surface, suggesting that turbulent structures are significantly constant in time. A quantitative model based on actual field data and using previous conceptual descriptions as a guide is presented to identify turbulent zones at the beach surface under offshore winds.
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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".