Assessing the value of mitigation strategies in reducing the impacts of rapid‐onset, catastrophic floods
Bibliographic record
Abstract
Abstract Communities worldwide face dangers due to floods induced by natural events or technical failures. These vulnerabilities are increasing due to continued settlement along coastlines and in floodplains, and may be exacerbated in future by climate change. Flood losses can be mitigated via structural and nonstructural (or community based) means. Risk analysis can be undertaken on behalf of different stakeholders including: policy makers or regulatory bodies; asset owners; the local community; and individuals who live, work or recreate in the hazard impact zones. While methods exist for assessing the risks associated with water impoundment and control structures, less effort has been devoted to developing methods that can assess the merits of community‐based preparation and response activities such as evacuation and sheltering in place. There is a need to identify the best approaches for undertaking assessments of proposed plans, and to explore opportunities for adapting existing models to provide these capabilities. This paper posits the challenge of assessing nonstructural approaches in the context of existing risk analysis methods, proposes a possible direction for developing new methods of analysis, and then demonstrates the application of the proposed methods in support of planning for near‐field tsunami hazards along the Pacific coast of North America.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".