Predicting soil—water characteristic curves of compacted plastic soils from measured pore-size distributions
Bibliographic record
Abstract
The unsaturated hydraulic conductivity function, k, is often predicted from the soil–water characteristic curve (SWCC). Most methods implicitly or explicitly derive a pore-size distribution (PSD) from the SWCC, which is then used to calculate k at any suction. Two important factors ignored by most methods are the change in the PSD during the SWCC test, and the influence of pore geometry on the SWCC. In this paper the SWCCs and evolution of the PSDs of four fine-grained compacted soils are measured. The SWCCs are measured from 0 to 3000 kPa by the axis-translation technique, and the PSDs are obtained by mercury intrusion porosimetry. A method is developed to predict the SWCCs from the measured PSD distribution data. Processes modelled include the effect of pore geometry on shrinkage during the SWCC tests, the isolation of water by the invading non-wetting phase (air), and differences in the relative accessibility of pores from the surface of the mercury intrusion and SWCC test samples. Different methods to model the above phenomena are developed and compared. The best predictions of gravimetric water content lie between one and two standard deviations of the measured SWCC data.
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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 | 0.002 |
| 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) | 0.000 | 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".