Vegetation growth promotion and overall strength improvement using biopolymers in vegetated soils
Bibliographic record
Abstract
Planting vegetation is a sustainable and eco-friendly method for shallow slope stabilization. However, in water-limited regions, this method is facing challenges such as retarded vegetation growth, which leads to unprotected soils. Biopolymers, with potentials in both vegetation growth promotion and soil strength enhancement, are therefore tested in this study with regard to their possibility in assisting soil reinforcement with vegetation through vegetation cultivation and direct shear tests. Both sugar-based and protein-based biopolymers improved water availability to growing plants and nutrient uptake. The most suitable polysaccharide xanthan gum was adopted to further explore the effects of treatment conditions (i.e., blending content) and external environment (i.e., precipitation) on the vegetated soil performance. Under a variety of water supplies, xanthan gum with a medium blending content of 0.5% (i.e., with respect to dry soil mass) led to the most substantial improvement in the ability to resist shear loading. This indicates that the appropriate dosage of biopolymers used at the initial stage of plant growth should provide moderate bond strength between soil particles, while not impeding root penetration. Supported by the obtained results, biopolymers are suggested to be used in combination with plants for soil reinforcement for the best efficiency.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".