Alongshore coupling of eco-geomorphological variables in a beach-dune system
Bibliographic record
Abstract
ABSTRACT: Coastal dune systems are becoming increasingly vulnerable to erosion and washover due to sea-level rise and changes in storm activity with changing climate. The impact, however, is not consistent within and across coastal barriers and there is a need to examine the alongshore variability of beach-dune systems to understand dune resiliency. This includes vegetation, which is responsible for trapping transported sediment and initiating dune formation and varies alongshore in response to a poorly understood eco-geomorphological feedback. Identifying how beach-dune systems and vegetation vary alongshore is important for understanding their resiliency. In previous studies it has been suggested that this feedback leads to scale-invariant foredunes in which the maximum potential dune height is directly related to the distance between vegetation and the shoreline (Lveg). There is, however, no corresponding field data to support this model result across and within barrier systems. This study involves the collection of field data from three beaches along the North Shore of Prince Edward Island, Canada. The dune systems are primarily vegetated by Ammophila breviligulata and vegetation density ranged from 0% to 100% beyond the dune crest, with considerable variability alongshore and between sites. The alongshore variability of the vegetation and its relationship to the morphology of the dune was examined using a 1x1m digital elevation model generated from Structure for Motion using Unoccupied Aerial Vehicles and LiDAR topobathy collected by CBCL Limited. Results suggest that dune morphology is not scale-invariant and that the relationship between dune height and vegetation is dependent on storm surge and beach envelope limits to the establishment of vegetation. Comparison to previously published data from a range of sites supports the scale-variant relationship identified in this study and suggest the need to consider development as a combination of transport, supply, and history.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".