Seasonal and inter-annual changes in wave attenuation by a constructed salt marsh on a sloping bed
Bibliographic record
Abstract
Understanding growth patterns, survival, and seasonal variations in plant characteristics is essential for newly established vegetated slopes to ensure their long-term effectiveness in dissipating wave energy, particularly in cold climate regions with extended winters and short summers. This study investigates the growth patterns, seasonal and interannual variations in plant morphological properties, and their corresponding effect on wave energy attenuation under controlled conditions, to evaluate the wave dissipation characteristics of newly established vegetated slope over multiple seasons, based on experimental data from large-scale physical modeling experiments. These experiments capture the one-year growth cycle of a newly constructed marsh with live saltmarsh vegetation native to eastern Canada and the USA. The findings reveal that even a newly established marsh with young, relatively sparse plants can contribute to wave energy dissipation. A clear seasonal variation in plant morphological properties was observed, resulting in a significant increase in vegetation-induced wave dissipation after one year of growth. Different species exhibited different responses against wave forces leading to different wave dissipation characteristics depending on their plant traits. However, under conditions of considerable depth-induced wave breaking, the effectiveness of vegetation in dissipating wave energy was considerably reduced. Overall, our data with young saltmarsh plants showed a maximum of about 60% contribution by vegetation to total wave energy dissipation under minimal depth-induced breaking conditions, and a maximum of about 25% contribution to total wave energy dissipation under significant depth-induced breaking conditions, indicating a considerable reduction in percentage contribution to total wave energy dissipation by vegetation with wave breaking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".