Using Biopolymers for Beach Erosion Mitigation
Bibliographic record
Abstract
This study aims to investigate the long-term performance of soils amended with Xanthan Gum. Coastal erosion ensues from both human activities and natural environmental dynamics, including waves, currents, and wind. These alterations disrupt the equilibrium in coastal processes. Numerous methods have been adopted to mitigate coastal erosion, such as protection structures, beach nourishment, and vegetation. Since these methods potentially alter the natural coastal ecosystem, more eco-friendly methods, such as stabilization by microbial-induced carbonate precipitation (MICP) or biopolymers, have been explored in recent years to stabilize the coasts against wind and wave erosion. Among biopolymers, Xanthan Gum (XG) proved to have a high potential for decreasing soil erosion. Nevertheless, the alteration of XG efficiency in soil improvement over time still needs more investigation. For this purpose, unconfined compression strength (UCS) of biopolymer-enhanced soil samples is measured at 1, 3, 7, 14, 21, and 28 days after sample preparation. Also, this study considers two XG concentrations of 1% and 2% to provide a better understanding of XG performance. Sample preparation starts by mixing soil samples collected from bluffs on the coastal area of Lake Ontario with polymerized XG. The mixture is then compacted at its optimum water content to achieve the maximum dry density. The compacted samples are then wrapped and kept in a humidity- and temperature-controlled chamber until tested. The results demonstrated that the UCS for samples containing 2% XG increases over time, while the UCS for samples with 1% XG remains unchanged.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".