Experimental Study of Wave Attenuation Across an Artificial Salt Marsh
Bibliographic record
Abstract
Scaled laboratory experiments were conducted to investigate the effectiveness of marsh vegetation in dissipating wave energy and reducing wave overtopping discharges at the crest of a dyke located immediately landward of the marsh. Model dyke and marsh platform features, loosely based on archetypes found in Atlantic Canada, were constructed in a wave basin at 1:20 scale and exposed to a broad range of waves and water level conditions. The 2D experiments were conducted using idealized surrogate vegetation (both rigid and flexible), and the model setup featured four parallel flumes which enabled four alternative configurations to be investigated simultaneously. The experiments investigated the sensitivity of wave attenuation and overtopping to the length of the vegetation field, vegetation characteristics (stem density, height, and flexibility) and varying water levels and wave conditions. The study outputs have helped to address knowledge gaps and provide evidence to support and inform broader use of hybrid marsh-dyke systems and managed dyke realignment to help manage flood and erosion risk and improve coastal resilience in Canada and internationally. This research confirmed the benefit of tidal flats hosting coastal marshes for attenuating waves, reducing overtopping volumes and lessening damage to dyke structures. As expected, taller and denser marshes were more effective in attenuating wave energy for a given marsh width.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".