Scour of Sand-Gravel Beaches in Front of Seawalls.
Bibliographic record
Abstract
Large-scale physical experiments were conducted in the 5m-wide, 5m-deep and 120m-long wave flume at the Quebec Coastal Laboratory of the national scientific research institute (INRS) to evaluate wave-induced scour depth (d$_{\mathrm{s}})$ at vertical seawalls and on natural beaches. In the initial part of the study, the equilibrium beach profile of a mixed sand-gravel beach with a mean grain size diameter of 12 mm was studied for various beach slopes using regular and irregular waves with intermediate water depths (h$_{\mathrm{0}}\in $ [2.3; 3.8] m) and different wave heights. In the second part of the study, a vertical seawall fronted by a 1:10 sloping mixed sand-gravel beach was tested for more than 50 wave trains using regular and irregular waves with various water depths at the seawall (h$_{\mathrm{w}})$, wave heights and wave periods. The scour depth at the toe of the seawall is highly dependent on the form of wave breaking onto the structure. Sea states where plunging breakers occur directly onto the wall generate jets of water that may penetrate to the seabed and cause a local scour hole immediately adjacent to the seawall. Scour depth is maximum when H$_{\mathrm{b}}$/h$_{\mathrm{w}}$ \textgreater 1 and X$_{\mathrm{b}}$/H$_{\mathrm{b}}$ \textless 1, where H$_{\mathrm{b}}$ is the breaker height and X$_{\mathrm{b}}$ the distance from the seawall of the breaking wave. Comparison with existing semi-empirically derived scour prediction equations was performed.
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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".