Improving an estimation model for dam failure-induced loss of life and customizing it for North America
Why this work is in the frame
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Bibliographic record
Abstract
The potential loss of life (LOL) resulting from dam failures represents a critical concern in dam safety and disaster management. Accurate estimation of LOL is paramount for informed decision-making, emergency preparedness, and the minimization of human casualties in such events. This paper proposes an improved model for LOL estimation associated with dam failures and shows how to customize it to specific regions, exemplifying with North America. The approach categorizes dam failure into subcases based on flood severity and the distance from the dam. Two empirical equations that serve as the calculation method for LOL formulated through multivariate regression analysis are derived using thirty-two dam failure subcases in North America. The datasets were split into train and test sets, yielding R 2 values of 0.9949 for low severity cases and 0.9955 for medium-high severity cases on the test sets. Graham's model was selected as a comparison benchmark due to its straightforward application, established use in LOL estimation, and minimal data requirements. The successful implementation of this model suggests its potential applicability for diverse regions, contributing to improved disaster preparedness and response strategies, as well as enhancing dam safety and community well-being downstream of dams. • A method to estimate loss of life induced by dam failures was improved and applied to North America. • Historical dam failures were modelled to gather data for model development. • The model is quick and easy to apply, and performed very well on test cases achieving R 2 close to 1. • All details necessary to develop similar models for other regions are included in the paper.
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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 | 0.001 |
| 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