A new National Seismic Hazard Assessment for Australia
Bibliographic record
Abstract
Geoscience Australia has released a new National Seismic Hazard Assessment for Australia — the NSHA18. The new NSHA provides an improved understanding of the seismic hazard and its uncertainties for Australia, allowing communities to be better prepared for earthquake events. Knowing with more certainty how the ground-shaking hazard varies across Australia, allows higher hazard areas to be identified for the development of mitigation strategies so communities can be more resilient to earthquake events. Using the NSHA, decision makers can better consider: •What this could mean for communities in those areas and whether any further action is required •Where to prioritise further efforts •What this could mean for insurance and reinsurance premiums •Identify high and low hazard areas to plan for growth or investment in infrastructure. The NSHA also provides key information to the Australian Government Building Codes Board, so buildings and infrastructure design standards can be updated to ensure they can withstand earthquake events in Australia. This talk will talk about the key features and enhancements of the new NSHA, challenges, and what key assumptions have been contested throughout the development of this fundamental assessment for our country. Since completing his postgraduate studies at Monash University in 2004, Trevor Allen has worked as an earthquake seismologist in Australia, Canada (NRCan) and the United States (USGS). He is currently member of Standards Australia earthquake loading subcommittee and sits on the Canadian Standing Committee on Earthquake Design. Trevor leads the development of Geoscience Australia's National Seismic Hazard Assessment. This talk is presented as part of the Distinguished Geoscience Australia Lecturer series on 3 December 2018.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".