Measurement and estimation of pore shrinkage and pore distribution in a clayey till during soil-water characteristic curve tests
Bibliographic record
Abstract
The soil-water characteristic curve (SWCC) of fine-grained soils is usually determined experimentally. In many applications, such as design of mine waste covers and landfill liners, the unsaturated permeability function, k(h), is often derived theoretically from the measured SWCC. Implicit in these derivations is the transformation of the SWCC to a pore-size distribution (PSD), typically assumed to be constant and mono-modal. PSDs of a clayey till compacted at various water contents were measured after compaction, after flexible-wall permeability testing, and during and after SWCC tests. The measurements show that the PSD changes significantly during permeability and SWCC testing. A method is advanced for predicting the observed changes in PSD during SWCC testing. PSDs are determined for soil samples subjected to the highest and lowest suctions applied during the SWCC test. The measured PSDs are transformed to account for pore trapping; the transform assumes that flow occurs through two sets of randomly distributed pores in series. To model pore shrinkage, the pores are idealized as elastic cylinders. PSDs measured after different suction applications in the SWCC tests are compared with predictions of the shrinkage model. The method can also be used to predict the SWCC. Measured and predicted values are compared.Key words: landfill liners, mine waste covers, soil-water characteristic curve, pore-size distribution.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".