Influence of drying–wetting cycles on soil-water characteristic curve of undisturbed granite residual soils and microstructure mechanism by nuclear magnetic resonance (NMR) spin-spin relaxation time (<i>T</i><sub>2</sub>) relaxometry
Bibliographic record
Abstract
Due to the formational environment and climatic variability, granite residual soils with grain-size distribution ranging from gravel to clay undergo multiple drying–wetting cycles. The influences of multiple drying–wetting cycles on the soil-water characteristic curve (SWCC) and pore-size distribution (POSD) of undisturbed granite residual soils are investigated using the pressure plate test and nuclear magnetic resonance (NMR) spin-spin relaxation time (T2) distribution measurement, respectively. Results show that the water-retention capacity and air-entry value decrease and pores become more uniform with increasing drying–wetting cycles. After four drying–wetting cycles, the soil reaches a nearly constant state. The POSD change of multiple drying–wetting cycle samples is consistent with the SWCC of the soils. Furthermore, a modified van Genuchten model in terms of cumulative pore volume is used to obtain the best-fit POSD of the drying–wetting cycle samples. The shape and changing tendency of both curves of SWCC and POSD are quite similar and achieved a better correlation. It can be concluded that the SWCC is strongly dependent on the POSD of the soil and NMR T2 relaxometry can be used as an alternative to the assessment of microstructural variation of residual soils subjected to the periodic drying and wetting process.
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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 | 0.000 | 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".