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Record W7115683381 · doi:10.71846/18-wcee-1542

IS GEM (GLOBAL EARTHQUAKE MODEL) MAKING A DIFFERENCE?

2025· article· en· W7115683381 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)HazardGovernment (linguistics)Risk assessmentFlood mythEarthquake scenarioEmergency managementUrban seismic riskFoundation (evidence)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.251
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it