Integrating Multiple Levee-Breaching Scenarios and Flood Events to Develop a Probabilistic Spatial Flood-Hazard Map of Etobicoke Creek in Toronto, Canada
Bibliographic record
Abstract
Forecasting flood characteristics (e.g., water levels and velocity) is a growing concern due to climate change. It is therefore necessary to consider the stability conditions of earthen levees used to mitigate floods during a flood risk assessment. This technical note presents a method to assess probabilistic flood hazard that takes into account levee failures, for a levee located along Etobicoke Creek in Toronto, Canada. We compute flood scenario probabilities resulting from multiple flood scenarios that accounts for both the levee failures across the length of the levee, and different levee-failure mechanisms (e.g., backward erosion and overtopping). Then, for each location of the flooded area, we compute a cumulative flood exceedance probability curve for flood depth and velocity. This method provides a flood-hazard map (depth and velocity) for a given probability and probabilistic maps for given values of depth or velocity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".