Managing residual flood risk behind levees: Comparing <scp>USA</scp>, France, and Quebec (Canada)
Bibliographic record
Abstract
Abstract Although hydraulic infrastructure such as levees remain important for flood risk management in the USA, France, and Quebec (Canada), there is increasing emphasis on nonstructural measures, such as regulatory flood maps, to reduce exposure and vulnerability, for example, preventing people from building in high hazard areas. One key concept related to areas protected by levees is that of “residual risk”, that is, the risk from floods greater than the design standard of the levees (levee overtopping) and from levee breach. In this article, we review the legislative framework for regulatory flood maps in the USA, France, and Quebec (Canada) and compare how residual risk behind protective structures is taken into account (or not) in regulatory flood maps. We find big differences in how the USA, France and Canada manage residual risk behind the levees. While in France the area behind levees is part of the regulatory flood prone area, and land use restrictions, building codes, emergency measures and risk communication are mandatory, in the USA the area behind levees is only shown as part of the regulatory flood prone area if the levee is not accredited. In Quebec, regulatory flood maps in general follow the French approach with a few exceptions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".