Seasonal Variation Of Wave Attenuation Capacity Of Canadian Saltmarsh Vegetation
Bibliographic record
Abstract
Nature-based Solutions (NbS) for coastal protection have been widely recognized as sustainable, economical, and eco-friendly alternatives to conventional grey structures, particularly under the threat of climate change (Temmerman et al., 2013). Living shorelines are a form of NbS, which incorporate natural elements (such as saltmarshes) that provide flood and erosion risk management benefits. Climate change impacts, such as rising sea levels and reducing sea-ice cover (Savard et al., 2016), are increasingly motivating communities in Canada to consider incorporating living shorelines in coastal protection schemes. The efficacy of wave energy dissipation by vegetation depends on both hydrodynamic conditions and plant characteristics. However, plant parameters, such as standing biomass exhibit seasonal fluctuations, leading to corresponding variations in attenuation capacity (Schulze et al., 2019). Hence, the design of NbS utilizing saltmarsh vegetation must account for seasonal variations to ensure sustained efficacy, especially within the context of Canadian regional climates, which are typically characterized by extended, stormy winters and shorter summer seasons. Few studies have quantified wave attenuation by real saltmarsh vegetation in large-scale laboratory facilities (Möller et al., 2014; Maza et al., 2015; Ghodoosipour et al., 2022), particularly for species native to the east coast of Canada. There is a knowledge gap on how seasonality affects wave attenuation by saltmarsh vegetation and how attenuation varies from the lower marsh to the higher marsh depending on species-specific plant traits. Research is needed to bridge this gap and develop technical guidance for the design of performant living shorelines in Canada.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".