Understanding the Consequences of Wellington’s Infrastructure Vulnerability to a Major Earthquake
Bibliographic record
Abstract
The Wellington Lifelines Group (WeLG) is comprised of the lifeline utilities serving the Wellington region of New Zealand. They share a collective understanding that Wellington’s infrastructure is vulnerable to natural hazards, which was gained from previous studies based on a variety of modelling and expert opinion approaches. These initial studies provided a foundation for deeper analysis to be undertaken around the vulnerability of the region’s infrastructure to earthquakes. The focus was on the likely damage states, interdependencies, and direct and wider economic consequences of a potential major earthquake in the region. The in-depth study used computer modelling and focused stakeholder engagement to quantify the improved resilience and economic benefit of the construction of a package of infrastructure upgrades. The study informs consideration of the mitigations that could be applied to address the core issues—short-term (emergency planning works), medium term (policy change at central government level), and long-term (the potential construction of new, resilient, infrastructure). This paper outlines this WeLG project, including the objectives, process, and intended outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".