An iterative program to back-analyze grain-size distribution from a predetermined soil–water characteristic curve
Bibliographic record
Abstract
Numerous methods for predicting unsaturated soil properties based on basic soil parameters have been introduced to reduce the cost of unsaturated soil testing. This research proposes an unsaturated soil estimation program that uses a predetermined soil–water characteristic curve (SWCC) to predict the grain-size distribution (GSD) using computer iteration. The results indicate back-calculating a GSD from a given SWCC is possible, and that different GSDs can produce the same SWCC. A Monte Carlo approach examining variations of the GSD was conducted and associated packing porosities are provided. The program was tested on coarse- and fine-grained soils to determine the program’s capability and indicate it is appropriate for samples dominated by silty sand and silt but not clay. The back-analysis program described here could bypass the arduous testing phase of multiple soils to find a suitable SWCC for a capillary break layer as it can start with a predetermined SWCC and estimate a suitable GSD. In such efforts, the most important characteristic (i.e., strength or stiffness) of a soil must be determined when considering the GSD required because an infinite number of GSDs with similar properties could produce the same SWCC.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".