DELIVERING IF CANADA EQ MODEL TO THE INSURANCE MARKET THROUGH UNIQUE MULTI-LATERAL COLLABORATION
Bibliographic record
Abstract
Natural Resources Canada joined the Global Earthquake Model Foundation to support and strengthen collaboration on implementing the latest Seismic Hazard Model for Canada (CanadaSHM6) and the Canadian Seismic Risk Model (CanSRM1) in GEM’s OpenQuake-engine. Releasing the models as open access in the OpenQuake format made the models available for use for various applications, including testing and validation by the wider scientific community. The hazard, vulnerability, and exposure data developed within the Natural Resources Canada – GEM collaboration was provided to Impact Forecasting (IF), who developed all components necessary to build a fully probabilistic industry-ready catastrophe model featuring CanadaSHM6. In particular, IF prepared a geotechnical model leveraging local Vs30 measurements, an event set covering 200,000 years, pre-calculated random event footprints considering the spatial correlation of ground motion on a variable resolution grid, and an enhanced vulnerability component including automobile curves and custom vulnerability curves for wooden structures depending on the type of their façade and roof. For complete coverage and to satisfy regulatory requirements, it was fundamental to incorporate secondary perils. The model includes landslides and liquefaction as the probability of building failure included in shaking event footprints. The fire following earthquake component uses a recent ignition model for simulation of ignitions and advanced cellular automata for simulation of fire spread and suppression to develop vulnerability for fire following earthquake. For the tsunami component of the model, IF collaborated with the University College London. Tsunami inundations were calculated using machine learning to create surrogate models that generated probabilistic high-resolution inundations, an impossible task with simulation only. The resulting model is accessible for insurance and reinsurance companies via IF proprietary ELEMENTS platform, via OASIS-based Nasdaq Risk Modelling for Catastrophes platform. With relatively low efforts, it can be adopted for other OASIS and non-OASIS platforms. It provides a range of outputs, including annual aggregates and probable maximum loss estimates for single risks or a portfolio of properties. Insurance companies and reinsurance companies can leverage the high-resolution hazard and risk maps for underwriting and risk assessment, available as GIS layers or in the IF Underwriting data service via API.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.007 |
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".