Assessing the Hazard from Aging dams in the U.S.A.
Bibliographic record
Abstract
Climate change, inadequate maintenance, and aging beyond the design life increase the probability of dam failure. Dam failures can have significant social, financial, and environmental impacts. Financial losses can extend beyond infrastructure replacement costs, with cascading effects in multiple sectors such as electricity, transportation, water supply, and environmental services. The existing dam hazard classifications in the United States do not formally characterize “hazard hotspots” considering these impacts. Given that there are over 90,000 dams with different states of disrepair, maintenance, and budgetary constraints, a better way to rank their potential hazard and allocate resources for risk mitigation is needed. We present an approach that is scalable over many regions for rapidly assessing the magnitude and exposure of a dam failure for a preliminary ranking of the priority areas of concern. The estimation of the consequences of a dam failure including financial losses, affected critical infrastructure, and population is addressed using publicly available dam break and consequence tools and national infrastructure datasets. Dams can be ranked using seven criteria following the Analytical Hierarchical Process. The application of the framework is demonstrated with dams in the Cumberland River Basin. The main barrier to applying this approach at a national scale is the estimation of the inundation area upon dam failure, and we outline a strategy to implement it. The importance of increasing the resilience of dams is becoming more critical given the increasing interest in hydropower as a renewable energy source in the face of climate change.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".