Sediment input to foredunes: description and frequency of transport events at Greenwich Dunes, PEI, Canada
Bibliographic record
Abstract
Aeolian sediment transport from the beach to the foredune system can be predicted for periods of months or years from hourly wind data collected at standard meteorological stations. However, there is no corresponding data set of transport-limiting factors such as beach surface moisture, snow and ice, pebble lag and restricted fetch length. The remote sensing station described here has been specifically designed to acquire information on the dynamics of the beach and foredune system at high spatial and temporal resolution during long periods of time. The system consists of three digital SLR cameras covering different areas of interest of the beach and foredune controlled by a timer that takes pictures every hour. Coupled with measurements from a 2D Windsonic anemometer, saltation probes and Erosion/Deposition pins, the station provides extensive time series on those factors affecting aeolian transport. This information is managed by a geodatabase which can be used to query and identify the nature and frequency of events that deliver sand from the beach to the dunes. The first step is to obtain an estimate of when and how do transport events take place. For this purpose, a filtering technique has been designed to isolate periods of aeolian activity at the beach and reduce the volume of data to process. This paper presents preliminary results of behavior of the aeolian system at Greenwich Dunes, Prince Edward Island, Canada through a complete year of measurements, and introduces key aspects for future modeling and analysis.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".