Geometry and grain‐size sorting of ripples on low‐energy sandy beaches: field observations and model predictions
Bibliographic record
Abstract
ABSTRACT There are very few field measurements of nearshore bedforms and grain‐size distribution on low‐energy microtidal beaches that experience low‐amplitude, long‐period waves. Field observations are needed to determine grain‐size distribution over nearshore bedforms, which may be important for understanding the mechanisms responsible for ripple development and migration. Additional nearshore field observations of ripple geometry are needed to test predictive models of ripple geometry. Ripple height, length and sediment composition were measured in the nearshore of several low‐energy beaches with concurrent measurements of incident waves. The distribution of sediment sizes over individual ripples was investigated, and the performance of several models of ripple geometry prediction was tested both spatially and temporally. Sediment samples were collected from the crest and trough of 164 ripples. The sand‐sized sediment was separated from the small amount (generally <3%) of coarser material (>2 mm) that was present. Within the sand‐sized fraction, the ripple crests were found to be significantly coarser, better sorted and more positively skewed than the troughs. Overall, the troughs were finer than the crests but contained a greater proportion of the small fraction of sediment larger than 2 mm. The field model of Nielsen (1981 ) and the model of Wiberg & Harris (1994 ) were found to be the most accurate models for predicting the wavelength of parallel ripples in the nearshore of the low‐energy microtidal environments surveyed. The Wiberg & Harris (1994 ) model was also the most accurate model for predicting ripple height. Temporal changes in ripple wavelength appear to be dependent on the morphological history of the bed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".