Compressional and shear wave velocities for artificial granular media under simulated near surface conditions
Bibliographic record
Abstract
Laboratory ultrasonic experiments were made on artificial soil samples in order to observe the effects of slight overburden, sand/clay ratio and pore fluid saturation on compressional and shear wave velocities. Up to several meters of overburden were simulated by applying low uniaxial stress of about 0.1 MPa to a restrained sample. Samples were fabricated from Ottawa sand mixed with a swelling clay (Wyoming bentonite). The amount of clay added was 1 to 40 percent by mass. Most measurements were made under room-dry conditions, but some measurements were made for fully-saturated sand-clay mixtures and for partially-saturated sand samples. For the dry sand-clay samples, compressional (P) velocities were low, ranging from about 200 to 500 m/s for the mixtures at low stress. Shear (S) velocities were about half of the compressional velocity, about 70 to 250 m/s. Dramatic increases in all velocities occurred with small uniaxial loads, indicating strong nonlinearity. Composition and grain packing control the mechanical response at grain contacts and the resulting nonlinear response at low stresses. P and S velocities are sensitive to the amount of clay added, even at low concentrations. At these low equivalent overburden conditions, adhesion and capillarity at grain contacts affect wave amplitudes, velocities, and frequency content in the partial saturation case.
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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) | 0.001 | 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".