Water retention and volumetric characteristics of intact and re-compacted loess
Bibliographic record
Abstract
A laboratory testing program was conducted to investigate the effects of microstructure on the water retention curve (WRC) and wetting–drying induced volume change in loess. The axis translation and vapor equilibrium techniques were adopted to control suction in the range of 0–400 kPa and 4–140 MPa, respectively. Hysteresis in the WRC of loess was observed for the entire range of suction studied. Compared to re-compacted loess, intact loess exhibits a more pronounced hysteresis in the suction range below 20 kPa, which can be explained by the ink-bottle pore neck effect or constricted pores. The hypothesis is supported by microstructural evidence of mercury intrusion porosimetry and scanning electron microscopy tests. However, re-compacted loess exhibits larger hysteresis than intact loess for suctions above 30 kPa. A conceptual model was introduced, which links WRC to the corresponding pore-size density (PSD) function. Regarding volume change, more noticeable drying-induced shrinkage, but yielding at a lower suction, was observed for re-compacted loess. This is consistent with the compression test results. Stress has a significant effect on change of PSD and constricted macropores leading to a shift in the main wetting curve and a less pronounced hysteresis. Intact loess exhibits a stress-dependent wetting-induced collapse and drying-induced shrinkage.
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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 | 0.001 |
| 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 | 0.001 |
| 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".