An earthquake scenario catalogue for Canada: a guide to using scenario hazard and risk results
Bibliographic record
Abstract
As part of the national Canadian Seismic Risk Model, a collection of earthquake hazard and risk scenarios has been created and will be documented in a series of Open Files and other publications. This first document will help a user in interpreting and understanding raw scenario outputs, without any technical, pre-requisite skills. A future document will help orient a technical user seeking to run their own earthquake scenarios, by introducing the input files and a strategy for running the OpenQuake (OQ) Engine for Canadian earthquake scenarios. Other documents will help nontechnical users interact with the model and understand how it can be used for disaster risk reduction in Canada. Following the first release of scenarios, tentatively scheduled for June 2021, all files will be accessed through the OpenDRR GitHub project page as they become available: . The raw scenario outputs, formatted as comma-separated value (csv) files, contain information about economic losses, building damage, casualties, and other disruptive impacts. All of the impacts are referenced to a unique asset ID, which can be tied to census geographic divisions or latitude/longitude coordinates for plotting. This paper documents all outputs of these models with sufficient detail for a user to begin exploring the results.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.154 | 0.061 |
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".