A methodology for undertaking vulnerability assessments of flood susceptible communities
Bibliographic record
Abstract
Many factors contribute to a communities ’ vulnerability with respect to flooding, including its popula-tion, built environment, and concentration of wealth in a small number of highly vulnerable areas that are susceptible to flooding. This paper presents a planning and risk management tool for assessing the vulnerability of communities to flooding, using a combination of Monte Carlo Simulation tech-niques and multi-criteria analysis. This process has been applied to the Credit River watershed, in Ontario, Canada, to assess the vulnerability of the 22 flood damage centres within the watershed. These flood damage centres have been previously identified in the Canada-Ontario Flood Damage Reduction Program Study (1985). A vulnerability characterization of the Credit River watershed was undertaken in 2007, this work builds upon the previous study. The indices developed in this study provide a quan-titative measure of the vulnerability for each of the 22 flood damage centres, and they are also used to estimate the total expected annual direct and indirect damage costs for each of the flood damage centres. The indices are also a useful tool for stakeholder consultation and communication, and can be used for water resources, landuse and emergency planning within the watershed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".