Compacted Clay: Difficulties Obtaining Good Laboratory Permeability Tests
Bibliographic record
Abstract
Abstract Testing the permeability of compacted clay is not easy. The hydraulic conductivity, k(Sr), depends upon porosity nc and degree of saturation Src after compaction, and the values reached during the permeability test, ncf and Srf. The four values are needed to predict k(Sr) using a dual-porosity model with two parameters, a and b. However, many published test reports do not give these four values. The tested clay is often unsaturated, and the measured k(Sr < 100 %) may be confused with its saturated value, ksat, whereas it may be one to three orders of magnitude lower than ksat. This, in turn, may lead a designer to poorly predict the total leakage of a lined cell or lagoon. For fully documented test data, parameter a is between 0.001 and 0.1 and parameter b is between 2.7 and 3.3 (around 3 for a perfect cubic law). Once the values of a and b have been found with correctly performed and fully documented tests, a local k(Sr) value can be predicted at each place the field density and Src have been assessed. This yields many predicted local k(Sr) values, which can then be used with statistics to predict the full- or large-scale hydraulic conductivity and leakage of a liner or cover.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".