A comprehensive dataset of riverine levee overtopping events for advancing risk assessment
Bibliographic record
Abstract
Earthen levees are the primary flood protection infrastructure worldwide, with overtopping recognized as the most prevalent mode of their failure. Despite its significance, detailed field data on overtopping events are often limited and fragmented. Here we present the Levee Loading and Incident Dataset - Overtopping (LLID-OT) version 1.0, a curated dataset of 487 overtopping events in U.S. riverine levees compiled over the past 15 years. Data were collected from diverse sources, including U.S. Army Corps of Engineers reports, aerial imagery, hydrologic data repositories, and eyewitness accounts, and include detailed information on hydraulic loading, soil and foundation classifications, construction/maintenance characteristics, geometry, and breach dimensions. Quality control included cross-referencing sources and verifying key parameters to ensure dataset consistency and reliability. Analysis reveals consistent relationships between breach characteristics and variables such as soil type, overtopping depth and duration, and levee geometry. The LLID-OT supports the development and calibration of data-driven and physics-based models, advancing levee risk assessment, design, and flood management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".