LEAP-UCD-2017 Centrifuge Test at KAIST
Bibliographic record
Abstract
Since the earthquakes of Niigata (Japan, 1964) and Alaska (USA, 1964), the dangers of liquefaction have been highlighted and research into liquefaction has been actively performed. Particularly, as part of the provision and verification of liquefaction data through physical modeling by using centrifuge and numerical prediction, the Liquefaction Experiments Analysis Project (LEAP) was launched. The purpose of the recent LEAP-UCD-2017, in which nine facilities participated, was to evaluate the uncertainty and repeatability of response in a previous study for LEAP-GWU-2015. The ground models were prepared in a rigid box with a 5° sloping model with relative densities of 85 and 50% by using Ottawa sand. The models were subjected to nondestructive and destructive motions based on a ground motion consisting of a tapered 1 Hz sine wave. This paper describes not only the experimental procedure in detail but also the difference in the sensor response of the ground model corresponding to the relative density of 85 and 50% during liquefaction. Moreover, it provides data for permanent horizontal–vertical displacement through liquefaction and for validation of the numerical model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".