Effect of different vegetation roots on mechanical properties of soil stabilization on slope
Bibliographic record
Abstract
Soil bioengineering is concerned with the soil stabilisation with the reinforcing agent such as plant roots. This approach is extensively popular in developing countries. Most of the study conducted on soil bioengineering is carried out by ecological researchers, whereas there have been few geotechnical research studies in India that focus on using plant roots for reinforcing purposes. This research aims to investigate the changes in soil strength caused by landslides. The soil will be stabilised using plant roots from regionally common plants in the study region. The lemon roots were collected and planted in the soil, and the alterations in geotechnical properties were investigated. The reinforcing process can result in an increase in the values of MDD, UCS, SS, and OMC due to the improved compaction of soil particles. It was found that as the percentage of plant root added to the soil increases, the MDD, UCS, SS, and OMC also increases until 1% of plant root was added by weight. After that point, these properties decreases. Hence, the most favourable proportion for soil stabilisation is 1% of plant root by weight to the soil. Thus the presence of plant roots in the soil matrix enhanced the soil's stability. Therefore, the plant roots that were examined can serve as cost-effective materials for enhancing slope stability,” particularly in places that are susceptible to landslides.
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.
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) | 0.002 | 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".