LEAP-ASIA-2019 Centrifuge Test at Kyoto University
Bibliographic record
Abstract
Abstract The Liquefaction Experiments and Analysis Projects (LEAP) is an international collaborative project that aims to verify, validate, and quantify the uncertainty of numerical liquefaction models. “LEAP-ASIA-2019” is one of the LEAP’s exercises, whose main objectives are to validate the “generalized scaling law” for lateral spreading and to fill the gaps of experiments to complete the dataset obtained as part of the “LEAP-UCD-2017.” Within this project, a total of five models were developed at the geotechnical centrifuge of Kyoto University to simulate the dynamic behavior of a submerged, uniform-density, 20-m-long, 4-m-deep, and 5° sloping deposit of Ottawa F-65 sand. This paper presents the key features of the model preparation and testing process while also examining the applicability of the “generalized scaling law” for centrifuge modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.009 |
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; both teacher heads agree on what is shown here.
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".