WAVE ATTENUATION OF SALTMARSH VEGETATION UNDER STORM CONDITIONS
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
Nature-based solutions (NbS) for coastal protection has recently gained increased attention worldwide as a sustainable, economical and eco-friendly alternative to conventional grey structures, particularly under the threat of climate change (Temmerman et al. 2013). Wave energy dissipation by vegetation can be parameterized by the total horizontal force acting on the plant; expressed using a Morison-type equation considering only the form drag component (Dalrymple et al. 1984). Modelling wave-vegetation interaction is challenging in a laboratory environment (Lara et al. 2016) and it is difficult to accomplish a realistic representation of a plant’s biomechanical behavior and geometry using plant mimics or surrogates. Few studies have modelled real saltmarsh vegetation in large scale laboratory facilities (Moller et al. 2014; Maza et al. 2015) and quantified wave attenuation, particularly for engineered living shorelines (Maryland DoE, 2013). Further research is needed, particularly in the Canadian context, to investigate the capacity of different saltmarsh species to effectively attenuate waves and wave runup under storm conditions, to examine the plant’s drag coefficient and to bridge the gap to develop technical design specifications for the detailed design of living shorelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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