Spatial Variability during Shrinkage Testing of Expansive Clays
Bibliographic record
Abstract
Civil infrastructure constructed with, buried in, or underlain by expansive clays is affected by high volumetric changes, especially because large-scale facilities are spatially distributed. This research focused on determining spatial variability during the shrinkage testing of expansive clays. An initially saturated sample (600 mm in diameter) of a high-plasticity clay was exposed to desiccation and thoroughly monitored over five months. The results indicated an expansive clay (30% smectite and 14% illite) in alkaline-pore water (695 mg/L Na+ and 1150 mg/L SO42−) for developing a dispersive soil fabric. The vertical shrinkage in the intact-soil portion was unchanged (remaining at 114 × 106 mm3) in the first 10 days, sharply decreased the initial volume by 30% (up to 280 mm or 80 × 106 mm3) in 68 days, and slowly decreased the initial volume by 40% (up to 240 mm or 68 × 106 mm3) in 145 days. Furthermore, the soil temperature was found to be 10% lower than the air temperature, whereas the relative humidity within the cell was found to be 30% higher than that outside the cell. The soil showed an initial prominent central ridge with a few cracks that gradually evolved into a distinct crack pattern with equal-sized and irregular soil chunks. The average soil surface showed no volume reduction up to 18 days and a subsequent linear reduction, reaching 25% of the initial soil volume by the end of the test.
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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) | 0.000 | 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".