Une méthode prédictive non biaisée et géoréférencée d'estimation des dommages résidentiels d'inondation
Bibliographic record
Abstract
Flood risk management for residences requires an economical analysis involving the mean annual damage by floods, taking into account the whole range of probability of floods and the cost of projected fluvial enhancements and measures, taking also into account the residual level of risk. Efficient methods are therefore necessary to estimate these risk values. The proposed approach is of a "distributed" type; it involves numerical modeling for estimating "residential submersion depth", a variable, which explains most of the direct damages to residences. The method relies on an individualized georeferenced definition of each residence. Measured submersion data and the compensations obtained from the huge Saguenay flood in 1996 (Ville de Laterrière) were used to build empirical laws based on submersion. Four distinct relationships were established: residences with or without a basement and valued below or above $50,000 each were assigned a specific relationship. With these relationships, direct residential damages in Laterrière were assessed by using only simulation results at the georeferenced position of sector residences as input. It was then possible to evaluate the total amount of direct home damages in Laterrière.Key words: flood, risk, damage rating curves, Saguenay floods, numerical modeling, predictive model, georeference, geographic information system.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".