Assessing aeolian beach‐surface dynamics using a remote sensing approach
Bibliographic record
Abstract
ABSTRACT A remote sensing technique for assessing beach surface moisture was used to provide insight into beach‐surface evolution during an aeolian event. An experiment was carried out on 21 October 2007 at Greenwich Dunes, Prince Edward Island National Park, Canada, during which cameras were mounted on a mast on the foredune crest at a height of about 14 m above the beach. Maps of beach surface moisture were created based on a calibrated relationship between surface brightness from the photographs and surface moisture content measured in situ at points spaced every 2.5 m along a transect using a Delta‐T moisture probe. A time sequence of maps of surface moisture content captured beach surface evolution through the transport event at a spatial and temporal resolution that would be difficult to achieve with other sampling techniques such as impedance probes. Erosion of the foreshore and berm crest resulted in an increase in surface moisture content in these areas as the wetter underlying sediments were exposed. Flow expansion downwind of the berm crest led to sand deposition and a consequent decrease in surface moisture content. Remote sensing systems such as the one presented here allow observations of the combined evolution of beach surface moisture, shoreline position, and fetch distances during short‐term experiments and hence provide a comprehensive rendering of sediment erosion and transport processes. Copyright © 2012 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) | 0.001 | 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".