Analyse comparée de deux méthodes d'amélioration de la qualité des données utilisées lors de l'expertise de barrages
Bibliographic record
Abstract
The safety assessment of civil works relies on the use of a large amount of data. These data are frequently 'imperfect': they contain uncertainty, imprecision, incompleteness that lower the data quality. It is important to develop methods and tools allowing the assessment and the control of imperfections related to data used during dam reviews. These systems should allow on the one hand, the quantification of the data quality and on the other hand, the decrease of imperfections thanks to corrective actions. Two methods were developed independently from one another: a method developed by Hydro-Québec in Canada (condition index method) and a method developed by Cemagref in France. This paper proposes a comparative study concerning these two methods. / L'évaluation de la sécurité d'un ouvrage de génie civil repose sur l'utilisation d'une grande quantité de données. Ces données sont fréquemment « imparfaites » : elles contiennent incertitude, imprécision, incomplétude qui réduisent la qualité des données. Il est important de développer des méthodes et outils d'évaluation et de maîtrise de ces imperfections afin d'une part, de quantifier la qualité des données utilisées pour évaluer la condition des ouvrages et d'autre part, de déclencher des actions correctives pour réduire ces imperfections. Deux méthodes ont été développées de manière indépendante afin de répondre à cette problématique : une méthode développée par Hydro-Québec au Canada (méthode des indices de condition) et une méthode développée par le Cemagref en France. Cet article propose une étude comparée de ces deux méthodes.
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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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".