Modélisation probabiliste du débit de rupture par submersion d’un barrage en remblai
Bibliographic record
Abstract
RÉSUMÉ: Les barrages en remblai, et les barrages en général, sont dimensionnés pour résister aux différents facteurs (météorologique, sismique, humain) qui pourraient causer leur rupture. Cependant, le risque zéro n'existe pas et la probabilité que chaque barrage soit détruit n'est pas nulle. Dans une optique d'optimisation de la sécurisation des zones en aval du barrage, la méthodologie présentée permet de probabiliser la rupture du barrage par submersion, afin de dépasser la simple évaluation de rupture – non rupture. Le calcul de la probabilité de rupture constitue la première étape, les scénarios de rupture étant principalement liés aux occurrences des crues. La deuxième étape est la probabilisation des hydrogrammes de crue au travers de leurs caractéristiques principales, le débit maximal et la durée nécessaire pour atteindre ce débit.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".