Elastic Wave Velocities in Partially Saturated Ottawa Sand
Bibliographic record
Abstract
A theoretical model is needed to predict the macroscopic mechanical properties of soil from the size, shape, and elastic properties of its constituent particles. To test one such model, we compared measured and calculated values of compressional and shear wave velocities in Ottawa sand. The sand was packed in a cylindrical tank ≈0.9 m in diameter and 0.9 m deep. The velocities were measured in the horizontal direction as a function of depth as the zero tension level of the water in the sand was slowly raised. In the air‐dry sand the velocities varied nonuniformly with depth, reaching a maximum value about two‐thirds of the way to the bottom of the tank. When water was introduced into the bottom of the sand, the nonuniform depth dependence was removed. At higher saturations, the velocities gradually decreased until the zero tension level was at the top of the sand. The nonuniform depth dependence in the dry sand has been attributed to the tank wall supporting part of the gravitational stress in the material. A modified Digby (1981) model was found to adequately account for the results in the wet material. A lumped parameter combining the contacts per grain, size, and the grain roughness was used to fit the data. In terms of the model, it is concluded that the water in the contacts between the grains had little effect on the normal contact stiffness, but reduced the tangential contact stiffness to zero.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".