Evaluation de la condition des ouvrages hydrauliques pour l'aide à la maintenance: les pratiques comparées d'Hydro-Québec et du Cemagref sur des parcs de barrages et de digues fluviales, Q.86 - R.65, Vol. III
Bibliographic record
Abstract
Hydro-Québec and Cemagref have developed condition assessment methods for hydraulic structures. They are titled respectively the Condition Index Method for Embankment Dams, and the Graphical Interface System for River Levees. These methods have been derived on the basis of similar methodologies, namely functional analysis, and expert knowledge elicitation and organization. Their developments have been conducted within a group of experts and have led to the edition of technical guides summarizing the expert application rules. The primary research objective is the sequencing of dam maintenance activities within the Hydro-Québec dam inventory, and the levee section lists for Cemagref. The use of these methods attains underlying objectives such as retaining structure safety review and diagnostic, as well as the capitalization of expert knowledge within the organizations. / Hydro-Québec et le Cemagref ont développé des méthodes d'évaluation de l'état des ouvrages, respectivement la méthode des indices de condition pour les barrages en remblai et le SIG Digues pour les digues fluviales. Ces méthodes ont été produites sur la base de méthodologies similaires, l'analyse fonctionnelle et le recueil et la formalisation de l'expertise. Leur développement a été réalisé au sein de groupe d'experts et a conduit à la rédaction de guides techniques constituant les règles expertes d'application. L'objectif prioritaire recherché est la programmation des actions de maintenance des barrages au sein du parc d'Hydro-Québec et des tronçons de digues pour le Cemagref. L'utilisation de ces méthodes permettent d'atteindre des résultats sous-jacents: l'aide à la revue de sécurité et au diagnostic des ouvrages et la capitalisation de la connaissance experte au sein des organismes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".