Residential flood risk in Metro Vancouver due to climate change using probability boxes
Bibliographic record
Abstract
To enhance the decision-making process and reduce economic losses caused by future flooding, it is critical to quantify uncertainty in each step of flood risk analysis. To address the often-missing uncertainty quantification, we propose a new methodology that combines damage functions and probability bounds analysis. We estimate the likely direct tangible damage to 375,973 residential buildings along the Fraser River through Metro Vancouver, Canada, for a range of climate change driven flood scenarios, while transparently representing the associated uncertainties caused by sampling error, imprecise measurement, and uncertainty in building height and basement conditions. Furthermore, for the purposes of developing effective management strategies, uncertainties in this study are classified into two categories, namely aleatory and epistemic. According to our findings and absent significant action, we should expect an enormous increase in flood damage to the four categories of residential buildings considered in this study by the year 2100. Moreover, the results show that second-order Monte Carlo simulation cannot adequately represent epistemic uncertainty for small sample sizes. In such a case, we recommend employing a probability box to delineate the epistemic uncertainty.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".