Comparing Existing and Novel Methodologies for Estimating Risk of Liquefaction Triggering and Damage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Estimating the probability that liquefaction will occur at a given site is a critical first step in calculating the seismic risk on lifelines and structures, such as lateral spreading and ground settlement. Several methodologies for estimating this probability exist. The majority of these methods are based on estimating the factor of safety against liquefaction throughout the soil profile and condensing that information into an index. The probability of liquefaction-induced damage can then be estimated qualitatively (e.g., “severe” liquefaction damage corresponds an index threshold) or quantitatively through fragility curves developed using databases of liquefaction observations. In this paper, we compare novel and traditional methods. We apply cloud analysis to a soil profile at a hypothetical site in San Fernando. This approach consists of performing multiple nonlinear site response analyses and synthesizing their results. The advantages and disadvantages of each method for the design and analysis of lifeline systems are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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 it