IS GEM (GLOBAL EARTHQUAKE MODEL) MAKING A DIFFERENCE?
Bibliographic record
Abstract
The GEM (Global Earthquake Model) Foundation was created in 2009 as a private, non-profit foundation located in Pavia, Italy, with the vision to create a world resilient to earthquakes. At its core, GEM develops tools, data and models for use in earthquake and multi-hazard risk assessment worldwide. Ultimately, through partnerships, GEM promotes the application of information to disaster risk reduction. The GEM framework is built upon dozens of partnerships across public and private institutions. GEM’s projects operate at scales from local, to country, regional and global levels, and accessed widely by the GEM community. Collaboration, scientific credibility, openness and public good are GEM’s guiding principles. In 2018 GEM produced its first global earthquake hazard and risk maps, and in June 2023, GEM will release its first major update to these maps, with a wide range of hazard and risk metrics. Beyond these successful collaborations and outputs, how is GEM improving earthquake resilience worldwide? Examples include: • Collaboration with the Canadian government to develop a national hazard and risk model (2020 version) has resulted in products for local planning (e.g. Vancouver), national building code, and catastrophe risk insurance. • In Turkey, supported by the World Bank (2021), GEM, together with JBA, conducted a flood and earthquake risk assessment to evaluate hospital and school infrastructure, resulting in national funding to retrofit the most vulnerable buildings. • In the development of the European earthquake models (2022), GEM’s OpenQuake was used to bring information from over 30 countries together into a homogeneous model. Results are now informing building regulations and insurance and risk financing applications. • GEM has trained more than 1000 engineers and scientists in the use of OpenQuake, resulting in hundreds of papers and risk management applications. In line with Pillar 1 of Sendai Framework for DRR, these are important contributions to building risk knowledge and awareness. GEM continues to expand its network, and, through partnerships, to inform risk management decisions in other areas, such as disaster response and recovery, future exposure and secondary hazards, and is developing important partnerships for multi-hazard risk assessment and climate change adaptation. Progress is often painfully slow, but GEM is making a difference.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".