Application of reliability techniques for the estimation of uncertainties in fluvial hydraulics simulationsThis article is one of a selection of papers published in this Special Issue on Hydrotechnical Engineering.
Bibliographic record
Abstract
This work forms part of a general research effort into developing a methodology for reliability estimates of results obtained in numerical hydrodynamic computations. We consider the possibility of overtopping at the crest of a dyke of height h0positioned on a river with several uncertain parameters, such as discharge, Manning coefficient, bathymetry, etc. The estimation model is based either on the first-order reliability method (FORM), on the multi-form method, or on the importance sampling methods and is coupled with algorithms for the resolution of an adjoint optimization problem. Numerical tests are carried out on flows over a channel with a bump and on a river. The results obtained with our algorithms are compared with those obtained with the commercial software Nessus®and with the Monte Carlo method. The proposed multi-form approach combined with a robust optimization algorithm provides reliable results within reasonable computation times.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".