Seismic monitoring short-duration events: liquefaction in 1<i>g</i>models
Bibliographic record
Abstract
The duration of liquefaction in small models is very short, therefore special monitoring systems are required. In an exploratory sequence of liquefaction tests, S-wave transillumination is implemented with a high repetition rate to provide detailed information on the evolution of shear stiffness during liquefaction. These data are complemented with measurements of acceleration, time-varying settlement, excess pore pressure, and resistivity profiles. Measurements show that excess pore pressure migration from liquefied deep layers may cause or sustain a zero effective stress condition in shallow layers, that multiple liquefaction events may take place in a given formation for a given excitation level, and that unsaturated layers may also reach a zero effective stress condition. The time scale for excess pore pressure dissipation in fully submerged specimens is related to particle resedimentation and pressure diffusion; downward drainage from unsaturated shallow layers may contribute an additional time scale. High resolution resistivity profiling reveals the gradual homogenization of the soil bed that takes place during subsequent liquefaction events. The S-wave transillumination technique can be extended to field applications and implemented with tomographic coverage to gain a comprehensive understanding of the spatial and temporal evolution of liquefaction.Key words: densification, electrical resistivity, multiple liquefaction, pore pressure, shear wave, spatial variability.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".