10.1016/s0967-0653(97)80217-7
Bibliographic record
Abstract
Past studies of coastal aeolian sediment transport have adopted a two-dimensional perspective relying on data collected from single survey transects normal to the shoreline to represent wind speed and sediment flux. Although this approach provides a useful approximation to the aeolian system, it presumes that air flow is steady and uniform and that single-point data or transect samples represent broader beach conditions. These conditions are frequently not satisfied because beaches are three-dimensional systems with substantial alongshore variation. This study focuses on spatial variations in sediment transport and in surface sediment characteristics that affect transport during offshore winds. The sediment flux was sampled over 15 minute intervals with vertical traps positioned to monitor small-scale (0-5 m), and medium-scale (0-50 m) alongshore variations. Variability in trapped sediment flux is of the order of + /- 30% of the mean flux at both spatial scales. Alongshore variations for sediment size (+/- 10% of the mean) and carbonate content (+/- 7% of the mean) are too small to explain entirely the variability in sediment flux. Variations in moisture content (+/- 35% of the mean) are large enough to account for flux variability, especially when coupled with the potentially important rule of wind gustiness. The combined effect of wind gustiness and moisture variation produces non-uniform sediment transport, manifested as streamers of sand that move across the beach. The irregular streamer movement results in selective interception by traps producing variable sediment trapping rates.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.992 | 0.994 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".