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Record W4390275340 · doi:10.1785/0120230182

The 2022 Aotearoa New Zealand National Seismic Hazard Model: Process, Overview, and Results

2023· article· en· W4390275340 on OpenAlexaff
Matthew C. Gerstenberger, Sanjay Singh Bora, Brendon Bradley, Christopher J. DiCaprio, Anna Kaiser, Elena Florinela Manea, Andy Nicol, Chris Rollins, Mark Stirling, K. K. S. Thingbaijam, Russ Van Dissen, Elizabeth Abbott, Gail M. Atkinson, Chris Chamberlain, Annemarie Christophersen, Kate Clark, Genevieve Coffey, Chris A. de la Torre, Susan Ellis, Jeff Fraser, Kenny Graham, Jonathan Griffin, Ian Hamling, Matthew Hill, Andrew Howell, Anne Hulsey, Jesse Hutchinson, Pablo Iturrieta, K. M. Johnson, V. Oakley Jurgens, Rachel Kirkman, R. M. Langridge, Robin Lee, Nicola Litchfield, J. Maurer, Kevin R. Milner, Sepi J. Rastin, Mark Rattenbury, David A. Rhoades, John Ristau, Danijel Schorlemmer, Hannu Seebeck, Bruce E. Shaw, Peter J. Stafford, Andrew Stolte, John Townend, Pilar Villamor, Laura Wallace, Graeme Weatherill, C. A. Williams, Liam Wotherspoon

Bibliographic record

VenueBulletin of the Seismological Society of America · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of VictoriaWestern University
Fundersnot available
KeywordsAotearoaHazardProcess (computing)Corporate governanceSeismologyComputer scienceGeographyGeologyEngineeringPolitical scienceBusinessFinanceLaw

Abstract

fetched live from OpenAlex

Abstract The 2022 revision of Aotearoa New Zealand National Seismic Hazard Model (NZ NSHM 2022) has involved significant revision of all datasets and model components. In this article, we present a subset of many results from the model as well as an overview of the governance, scientific, and review processes followed by the NZ NSHM team. The calculated hazard from the NZ NSHM 2022 has increased for most of New Zealand when compared with the previous models. The NZ NSHM 2022 models and results are available online.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBulletin of the Seismological Society of AmericaSame topicearthquake and tectonic studiesFrench-language works237,207