Water retention characteristics and mechanical properties of vegetated biopolymer and biochar-reinforced sandy loam
Bibliographic record
Abstract
Vegetation is a sustainable strategy for erosion control and slope stabilization, though its initial cultivation can be lengthy and potentially weaken soil structures. This study compared two bio-mediated ground improvement techniques, biopolymer and biochar, known for their supportive effects on vegetation growth. Additionally, a novel treatment combining biopolymer and biochar was examined for its potential in vegetated-engineering practices. Engineering performance was assessed through soil water characteristic curve, vegetation growth, direct shear testing, and rainfall simulation. The results revealed that biopolymer and biochar treatments enhanced soil water capacity but negatively impacted vegetation germination rates and shear strength of the reinforced soil, attributed to hydrogel formation, and increased soil water content from irrigation. In comparison, soil reinforced with the combined method showed a promotion in the vegetation while maintaining the soil’s mechanical performance throughout the cultivation period and exhibited only minor reductions in the shear strength compared to other reinforced soils. Moreover, the new treatment showed improved soil erodibility under a majority of rainfall occasions, regardless of the vegetation coverage. This enhanced engineering performance by the new treatment is believed to be the polymerisation between the biopolymer hydrogel, biochar, and soil particles.
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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".