Methods of analysis and risk assessment of accidents of hydraulic structures
Bibliographic record
Abstract
Relevance of the research is due to the fact that over the past 10 years severe accidents at large hydropower plants and dams occurred in Russia (SayanoShushenskaya HPP, 2009), USA (Oroville dam, 2018), Brazil (Brumadinho dike, 2019), Colombia (HPP Ituango, 2018) and other countries, the need has arisen to improve the safety programs of the HS and dams. The main method of this important work is to use methods of analysis and assessment of risk accidents of HS and dams. Methods of this important work are to develop modern methodology for analyzing and assessing the risk of accidents of HS and dams. The introduction of the method of analysis and risk assessment in the safety programs of hydraulic structures (dams) in countries that are advanced in the construction of HS (China, Brazil, Canada, USA, Russia, Colombia, Norway, Spain, etc.) shows that in applying risk assessment analysis accidents of HS and dams still a number of difficulties, but this approach is of great benefit in monitoring the safety of HS and dams. The aim of this article is to familiarize and train specialists and hydraulic engineers with modern methods for assessing the safety of HS and dams.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".