Effect of Initial Saturation Level on Leaching and Permeability of Treated Gyoseous Soil
Bibliographic record
Abstract
Water passing through gypseous soil mass dissolved and leached the gypsum slats, making the presence of these soils a problematic issue for important projects and buildings.By implementing the soil with three different stabilizer materials (cement = 8%, lime = 4%, and fly ash = 8%), this effort aims to improve gypseous soil's permeability and leaching properties.To study the influence of initial compacted moisture content on permeability and leaching, all the samples were compacted at their field unit weight with different initial compacted saturation levels (20%, 50%, and 70%).The constant head method used for the calculation of the permeability coefficient keeps the water flow through the soil sample in a glass cylinder constant for a long five days.The results show that compacting the treated soil up to a 50% saturation level leads to a clear decrease in permeability and leaching.The permeability coefficient decreased by about 17%, 27%, and 42% when compacted at initial saturation level of 20%, while decreased by about 76%, 84%, and 92% when compacted at an initial saturation level of 50% and compacting the treating soil at an initial saturation level of 70% decreases the permeability coefficient by about 88%, 95%, and 100% for soils combined with fly ash, lime, and cement, respectively.A statistical analysis was conducted on the experimental results.Non-linear regression analysis was used to derive the relationship between the input variables (degree of saturation, type of treated material, and time) and their effect on the permeability coefficient.
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) | 0.001 | 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".