Analyzing topographic change profiles in coastal foredune systems: Methodological recommendations
Bibliographic record
Abstract
Estimates of total sand volume in foredunes are commonly made for purposes of predicting coastal erosion and inundation potential during major storms, which is of critical importance for resource management and engineering purposes. However, changes in dune volume through time are potentially of greater relevance to process geomorphologists because volume changes are diagnostic of the long-term evolution of beach-dune systems and can be related to drivers of change (e.g., storm climatology, sediment supply, human action). The methods by which dune volume change estimates are made vary widely in the literature because there are no established protocols that provide guidance on the horizontal distances over which volume integration should be performed. In addition, identifying a diagnostic geomorphic feature such as the dune toe as a limit on integration is fraught with subjective uncertainties. In this paper, an objective methodology for quantifying dune volume change directly (rather than absolute volume) is proposed that is simple, intuitive, and robust. The horizontal limits of integration are identified by zero-crossings in the topographic change profiles between two transect surveys taken at different times, and this avoids challenges associated with identification of the dune toe as a fixed profile feature. Instead, the proposed method focuses on locations of morphodynamic significance where there has been a transition from erosion to accretion in the time interval between surveys.
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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 | 0.073 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".