Method for consequence curves as applied to flood risks
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
This article summarises research intended to expand current study methodologies targeting flood risks with regard to specific issues and emergency preparedness requirements of municipalities. Various methods are currently available to predict the consequences of flooding risks. DOMINO is one such tool used to study flooding risks from natural events or from potential dam breaks. A complementary tool, CONSEQ, was developed to compute the impacts and present them in the form of consequence curves. This tool uses a specific method to assess all tangible and direct damages from exceptional flooding. However, intangible damages and the needs of municipalities downstream of the facility will also be taken into account. This article presents the DOMINO and CONSEQ tools as well as the methodology used to study consequences in relation to these analytical tools. It also describes the requirements of municipal emergency managers in order to draw consequence curves.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it