NEW INSIGHTS ON USING SCALED MARSH PLANT SURROGATES FOR WAVE ATTENUATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has been widely demonstrated in literature that coastal marshes provide positive ecosystem services related to coastal protection, including wave attenuation, storm surge reduction, and erosion prevention (Moller et al., 2014; Wang et al., 2021; Paul and Kerpen, 2021). Physical modelling presents a useful tool for investigating the coastal protection function provided by marsh vegetation in a controlled, repeatable environment, to inform design of nature-based coastal protection strategies, or “nature-based solutions” (NBS). To date, physical modelling studies have been used to investigate the influence of plant biophysical parameters (stem width, stem height, stem flexibility) and hydrodynamic conditions on wave attenuation (e.g., Augustin et al., 2009; Anderson and Smith, 2014; Moller et al., 2014; Ozeren et al., 2014; van Veelen et al., 2020). Such studies have predominantly used surrogate vegetation due to the logistical challenges and facility requirements associated with live plant experiments. Furthermore, most studies have been performed at or near full-scale to reduce uncertainties and scale effects associated with downscaling vegetation, particularly where Reynolds number similarity cannot be preserved (Blackmar et al., 2014). To address existing knowledge gaps related to physical modelling of marsh vegetation at small-scale, experiments were conducted in a 63 m long by 1.22 m wide wave flume at the National Research Council of Canada’s Ocean, Coastal and River Engineering Research Centre, Ottawa, in collaboration with the University of Ottawa and the Institut National de la Recherche Scientifique, Quebec City.
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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 it