Interannual Comparison Of Aeolian Sediment Transport Events At Greenwich Dunes, PEI, Canada
Bibliographic record
Abstract
Previous work conducted by the signing authors at Greenwich Dunes, in Prince Edward National Park, Canada, suggests that the majority of aeolian sediment input to the dunes over the course of several months is driven by a few isolated events. The type of events that effectively delivered sand from the beach to the dune were of medium frequency and small to medium magnitude. The probabilities of large and very large magnitude wind events to result in strong transport was small because of the limitations imposed by an ice and snow cover, moisture, and short fetch distances. The long term monitoring station responsible for the collection of the nine months data set at Greenwich continued recording for at least another year, and thus the potential existed for an interannual comparison. In this paper we analyse the statistical significance of results from the first year of monitoring given the new available longer time series. In particular we address the existence of large magnitude wind events that result in no aeolian sediment transport and the implications for large over-predictions of sediment input to foredunes at the meso-scale. We use results on the nature of transport events effectively involved in foredune building to discuss previous findings and implications for management and modelling of coastal dunes at the meso-scale.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".