Bibliographic record
Abstract
Dam risk assessment technique has already been used in the field of dam safety management abroad especially in Canada, Australia and USA in recent twenty years. There are more than 85 000 dams in China and about one-third of them are with severe deficiency and in high risks. Therefore it is real necessary to use this kind of technique to assist prioritizing dam maintenance or rehabilitation, reducing risks caused by dam failure. The technique consists of two important research objectives, dam-break probability analysis and dam failure impact evaluation. Dam failure impact includes three main factors: loss of life, financial loss, society and environment. How to evaluate the severity degree by means of these three factors comprehensively is a challenge of dam rehabilitation decision-making. The paper is to present a comprehensive evaluation coefficient for dam-break severity degree. The study introduces integrative assessment function L which considered weights S_i respectively of life loss, financial loss, society and environment, and their severity coefficients F_i, formed as linear weighted sum method. In order to integrate loss of life, financial loss, society and environment in accordance with existing laws and regulations in China, logarithm non-linear or linear models of data normalization are established to deal with their different units. A series of quantitative values of L which divide disaster event into deferent degree are suggested according to Chinese situation. The method and evaluation model is practically applied at 5 reservoir dams to appraise their severity degree of failure impact respectively and the comparison of these results are made to decide which one is more severe. The analyzed result shows that hazard of all these 5 dams would be extreme severe and should report to State Council as soon as the event of dam break occur.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".