An Improved Conceptual Bayesian Model for Dam Break Risk Assessment
Bibliographic record
Abstract
This paper presents an improved Bayesian model for evaluating the likelihood and consequences of dam failure. It is based on a comprehensive state-of-the-art review of risk assessment associated with dam breaks, with a particular emphasis on the application of Bayesian models. The study delves into the most recent developments in the field, investigating the utilization of Bayesian models, while focusing on two distinct dam types: tailings dams and water dams. Through an extensive survey of over 100 recent articles, the review systematically examines the parameters considered and the effectiveness of Bayesian models in the context of dam break risk assessment. The paper seeks to provide insights into the advantages and limitations of Bayesian approaches, shedding light on their practical utility in enhancing our understanding of dam failure risks. Furthermore, the study proposes a new Bayesian model applicable to tailings dams and water dams. The study also identifies gaps in the current body of knowledge and delineates potential avenues for future research. By critically assessing the efficiency of Bayesian models, this work offers valuable guidance to researchers, engineers, and stakeholders involved in dam safety, disaster preparedness, and risk mitigation. The ultimate goal is to advance our ability to safeguard lives and critical infrastructure in the face of potential dam failures, contributing to a more resilient and secure future.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".