A modeling approach for aeolian sediment input to coastal dunes
Bibliographic record
Abstract
Coastal dune evolution results from a complex balance between beach and dune budgets, wind and wave activity, and a number of other factors which vary over different temporal and spatial scales. Traditional approaches based on instantaneous transport equations are insufficient to predict sediment input to the foredunes at medium scales, and inferring information at larger scales from short-term experiments results problematic without knowledge of the timing and magnitude of particular transport events. There is a need to explore different ways to model aeolian activity at a scale of months to years, where most management practices take place. Challenges consist in developing appropriate instrumentation, methodologies to analyze the output data, and theoretical frameworks where to place new modeling approaches. This paper summarizes the efforts taken at Greenwich Dunes (Canada) to develop strategies to quantify/model sediment input to the foredune at a medium scale. Fieldwork consisted on the deployment of a remote sensing station based on digital cameras and coupled with anemometers and safires. Data was processed using ArcGIS 9.2 and PCI Geomatica 9.1, and managed by an ArcCatalog Geodatabase. Time series covered factors such as shoreline position, fetch distances, or maps of surficial moisture content. Modelling followed two steps: an initial filtering technique that isolated potential transport events and determined when transport took place, and a second stage that calculated their magnitude while keeping the spatial and temporal variability of the factors involved. Filters included the presence of ice/snow, the range of wind angles that potentially deliver sediment to the dunes, a minimum threshold wind speed, and a maximum percentage of surficial moisture content, all of which could shut down aeolian sediment transport. Preliminary results show that this modeling approach can produce improved predictions of annual sediment supply to the foredune compared to models based on wind speed and direction only.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".