Bibliographic record
Abstract
The Gold Coast is a bustling region in South East Queensland with a large concentration of people and has dynamic and growing business and tourism activity. The region is subject to thunderstorms and tropical cyclones that can generate damaging winds. The Severe Wind Hazard Assessment for South East Queensland evaluates the risk posed by severe winds and has strategies for managing this risk (Edwards et al. 2022). Results from the most recent assessment showed that older residential houses were the most damaged by severe winds and that this contributed disproportionately to community risk. However, lessons from recent wind damage caused by Tropical Cyclone Seroja in Western Australia in 2021 indicated that modern house designs have important vulnerabilities. These findings are a concern for any exposed coastal area and, in particular, for South East Queensland. This paper presents a suite of scenarios developed to address this vulnerability. Specifically, we describe how emergency and disaster managers can conduct capability analyses with the goal to enhance intelligence and planning capabilities. An example of the City of Gold Coast was used to show how it has leveraged these capabilities to improve emergency risk-based planning and begin a community resilience transformation with effective places of refuge and evacuation centres for the community.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".