Sediment supply to beaches: Cross‐shore sand transport on the lower shoreface
Bibliographic record
Abstract
Abstract Many beaches have been built by an onshore supply of sand from the shoreface, and future long‐term coastal evolution critically depends on cross‐shore sediment exchange between the upper and the lower shorefaces. Even so, cross‐shore sediment supply remains poorly known in quantitative terms and this reduces confidence in predictions of long‐term shoreline change. In this paper, field measurements of suspended sediment load and cross‐shore transport on the lower shoreface are used to derive a model for sediment supply from the lower to the upper shoreface at large spatial and temporal scales. Data collection took place at five different field sites that exhibit a wide range of wave conditions and sediment characteristics. Data analysis shows that both suspended sediment load and cross‐shore sediment transport scale with the grain‐related mobility number which ranged up to ψ ≈ 1000 in the measurements while the effect of orbital velocity skewness is more limited. A 1 year long simulation of sediment transfers between the lower and the upper shorefaces on a natural beach compares well with transport rates estimated from long‐term bar migration patterns and aeolian accretion on the same beach.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".