Probabilistic seismic landslide mapping for western Metro Vancouver, British Columbia
Bibliographic record
Abstract
The annual direct and indirect costs related to landslides in Canada are estimated to be $200 million. The west coast of British Columbia (BC) has experienced the most landslide-related fatalities within Canada considering its mountainous terrain and unique physiography. In the urbanized Lower Mainland of BC, many residential areas extend to the edges and bases of escarpments. Even small landslides in these locations can damage houses, roads, and other structures. Combined with the high seismic hazard of the region, seismic slope instability becomes a significant geotechnical hazard in the Lower Mainland.\nSeismic slope failures are predicted in practice using seismic displacement prediction models (SDPMs) based on a Newmark sliding block analogy. Seismically induced permanent displacements are calculated using earthquake hazard and soil strength parameters represented by yield acceleration of the slope (ky). This thesis presents a probabilistic solution for the seismic sliding displacement of slopes for the Metro Vancouver region considering the multiple seismicity sources and the latest updates in SDPMs. The uncertainties in input seismic parameters and SDPMs are both taken into account, and probabilistic displacements are determined for different values of ky and the initial predominant frequency of the sliding mass (fs). Regression analysis is performed to develop a regional predictive model to estimate probabilistic displacement values at a 2% probability of exceedance in 50 years hazard level for different slope conditions (i.e., ky and fs values) across Metro Vancouver. High-resolution topography data is used to construct semi-automated polygons to capture slope geometries. The regional displacement models are employed to assign the corresponding seismic displacement values to slopes, and the first probabilistic seismic landslide hazard map for Metro Vancouver is generated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".