A Case History in the Fraser River Basin on Different Liquefaction Triggering Assessments and Considerations for Their Use
Bibliographic record
Abstract
Geotechnical engineers often need to complete geotechnical site investigations with one of the outputs being the completion of liquefaction triggering assessments. Typically, these assessments rely on empirical procedures that have been developed from seismic case history data and use of various in situ test methods. The numerous investigation methods available each have their own different considerations for their use for these assessments such as the application of test specific or more general correction factors. In a perfect world, the liquefaction triggering conducted by the different assessment methods would yield the same level of liquefaction susceptibility for the same soil unit. Unfortunately, differences between the outputs of the triggering procedures and other epistemic uncertainties often occur leading to inconsistencies. This paper presents a case study from a recent project in the lower mainland reviewing different investigation methods and their respective liquefaction triggering assessments. In addition, lab tests conducted on undisturbed samples were compared to the empirically based triggering methods.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".