Evolution of the ripple field under a wave regime on the offshore of the breaker bar, along the beach profile: Experiment using CT-Scan imaging.
Bibliographic record
Abstract
Beach profile stability is a key factor of the coastal erosion. To understand if a beach profile is \nstable it is essential to analyze this profile in a physical model. A beach model under waves \nregime was generated in a small scale model to investigate the formation and the evolution of a \nripple field over a sloping (1:15) sand beach with a 0.215 mm sand (Ottawa sand). The aim of \nthis research is to improve the knowledge of the beach profile dynamics and the contribution of \nthe ripple field to the nearshore bar development. This experiment simulates the formation and \nevolution of ripples from a flat beach profile, with wave of 1.5 second period and 4 cm height. \nThe flume was instrumented with a Siemens CT-Scan in both spiral and perfusion modes and \nthe particle image velocimetry (PIV). These two instruments were covering the offshore of the \nbreaker bar by a series of measurement each 20 minutes during a 5 hours period. \nThe aims of these studies are to determine the evolution of the suspended sediment \nconcentration by both CT-Scan and PIV, the evolution of the current distribution by PIV and \nfinally the variation of the water concentration under the sand column during wave passage. \nThese evolutions are monitored during the beach profile evolution from a flat bed to a nearshore \nbar bed. Then, the X-ray tomography method coupled with a PIV method showed promising \nresults to measure the simultaneous evolution of the suspended load zone, the bedload zone \nand the transition zone.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".