Regional liquefaction-induced ground displacement predictions for Canada using 5th Generation NBCC Seismic Hazard Model
Bibliographic record
Abstract
Significant regions of Canada are seismically active, and areas underlain by loose, saturated sediments in these regions are expected to experience liquefaction-induced lateral spread permanent ground displacements (PGDs) under earthquake shaking. The empirical predictive equations are commonly used for estimating the PGDs especially when detailed numerical analyses are not undertaken. The key input parameters for these equations comprise earthquake magnitude (M) and source distance (R), along with geotechnical and topographic parameters; however, there is ambiguity over choosing appropriate site-specific values of M and R. The current study predicts PGD probabilistic hazard curves that are applicable for locations in southwestern and southeastern regions of Canada using a probabilistic seismic hazard assessment (PSHA) framework. This was achieved by embedding two relevant empirical models for predicting lateral spread PGDs (replacing the ground motion prediction equations) within the 2015 National Building Code of Canada framework that is currently available in OpenQuake platform. Moreover, deaggregation analyses have been performed to understand the M and R contributions from different seismic sources on the predicted site-specific hazard. The developed PGD curves can be readily adopted for estimating site-specific lateral spread PGD demand as input to performance-based design of structures in seismically active areas of Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".