A flood risk assessment to municipal infrastructure due to changing climate part I: methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractExtreme rainfall events that are occurring more frequently as an effect of climate change and variability are causing increasing damages to municipal infrastructure. A methodology is developed to quantify the risk to municipal infrastructure from climate change-related flooding. The risk is measured using a combination of flow/frequency, stage/damage and damage/frequency curves. The measure of risk is termed the Risk Index and calculated for each infrastructure element within a municipality. The risk is aggregated and summed by spatial unit and presented in the form of risk tables and maps. The risk index takes into account both quantitative and qualitative information obtained from research and interviews with technical experts. The results from the application of the methodology to a municipality will lead to better policy and informed decision making.Keywords:: flood risk assessmentfloodplain managementspatial riskclimate changerisk mitigation AcknowledgementsThis work was made possible by financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC), Ontario Graduate Scholarship (OGS) and by the City of London. The authors would also like to thank our colleagues Hyung-Il Eum and Dragan Sredojevic whose work provided the input to this research.
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.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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 it