Reducing uncertainties and improving sand soil-water retention curve (SWRC) predictions for hazard screening analyses
Bibliographic record
Abstract
An energy-based laboratory-testing program was undertaken to investigate the effects of different testing methods, numerical model fits, and soil fabrics and densities on the soil-water retention curve (SWRC) using a poorly graded sand. Four different reconstitution energies and three saturation levels were used to generate different soil fabrics and structure within a narrow band of possible densities, as limited by the mechanical properties of the soil particles. Tests were performed using a “transient retention imbibition method” and a Fredlund device to develop a statistically representative laboratory SWRC. Testing results for the poorly graded sand indicate little aleatory variability in SWRC from the soil structure. The dominant source of data variability is a function of the epistemic uncertainty associated with the testing methods and fitting models but can be accounted for by a bounded mean SWRC. This bounding allows for the development of a laboratory “proxy” soil, representative of generalized sand SWRC behavior, for use as a hazard screening tool for modeling unsaturated sand behavior. The proxy soil SWRC is compared with other generalized SWRC models and independent SWRC field and laboratory tests, wherein the proxy soil SWRC yields significant increases in accuracy between the estimated and field SWRC behavior.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".