National Residential Flood Risk Assessment
Bibliographic record
Abstract
The National Residential Flood Risk Assessment (NRFRA) is a partial response to the need for consistent national risk information, comparable to insurance portfolio analysis information, in the public domain and has been undertaken as a component of the National Climate Risk Assessment (NCRA) Stage Two under the Infrastructure and Built Environment risk category. This work is being led by the Department of Climate Change, Environment, Energy, and Water (DCCEEW) and is drawing upon the expertise from Geoscience Australia (GA) as part of the Australian Climate Service (ACS). The NRFRA is a quantitative evaluation of the likelihood and magnitude of the physical impacts to the built domain within riverine locations arising from flood hazard with a focus on residential separate houses (SH). The NRFRA has provided some insights on relative state level flood risk. It has assessed the flood risk at over 221,000 homes across Australia and communicated the risk at a range of aggregations. The risk outcomes have been translated into a “Vulnerability Index” that can be associated with other socio-economic metrics to give a more holistic and informative view of local scale community flood risk. While the results have been shown to have limitations primarily due to the fidelity of the probabilistic hazard information available, it has provided a demonstration of how consistent flood risk information can be developed nationally. With improved inputs it can provide local scale understanding of present flood risk and with climate influenced flood hazard it can assess future risk. Together these would identify communities exposed to high flood risk now, how this risk will be exacerbated and what communities will become flood-prone in the future. Potentially, it can be further extended to capture changing exposure with future development and the effectiveness of informed land use planning. This would include mitigation strategies to address climate change implications for flood risk and the effectiveness of strategies to progressively address it.
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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 | 0.001 | 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".