Surficial liquefaction manifestation severity thresholds for profiles having high fines-content, high-plasticity soils
Bibliographic record
Abstract
The severity of surficial liquefaction manifestation was significantly over-predicted for a large subset of case histories from relatively recent earthquakes that impacted the Canterbury region of New Zealand. Such over-predicts generally occurred for profiles having predominantly high fines-content (FC), high-plasticity soil strata. Herein, the liquefaction case histories from the Canterbury earthquakes are used to investigate the performances of three different manifestation severity index (MSI) models. The prevalence of high FC, high-plasticity strata in a profile is quantified through the soil behavior type index averaged over the upper 10 m of a profile ( Ic10). It is shown that for each MSI model (1) the threshold MSI value distinguishing cases with and without manifestation increases as Ic10 increases and (2) the ability of the MSI to segregate cases with and without manifestation decreases with increasing Ic10. Additionally, probabilistic models are proposed for evaluating the severity of surficial liquefaction manifestation as a function of MSI and Ic10. The approaches presented in this study allow better interpretations of predictions made by existing MSI models, although their efficacy decreases at sites with high Ic10. An improved MSI model is ultimately needed that better accounts for the effects of high-FC, high-plasticity soils more directly.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".